diff --git a/.gitignore b/.gitignore index 0567e20..2691f58 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,3 @@ -*.properties +src/**/*.properties +doc/docker/doc/ + diff --git a/AUTHORS.md b/AUTHORS.md new file mode 100644 index 0000000..6a1780a --- /dev/null +++ b/AUTHORS.md @@ -0,0 +1,14 @@ +# Authors and Contributors + +The following people have contributed code and/or ideas to the current version of the Neuroelectrophysiology Analysis Ontology. + +* Cristiano Köhler (orcid: 0000-0003-0503-5264) [1, 2] +* Michael Denker (orcid: 0000-0003-1255-7300) [1] + +1. Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany +2. RWTH Aachen University, Aachen, Germany + +# Contact data + +If you have questions regarding the content of this repository, please send an +e-mail to [c.koehler@fz-juelich.de](mailto:c.koehler@fz-juelich.de). diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..da6ab6c --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,396 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. Creative Commons makes its licenses and related +information available on an "as-is" basis. Creative Commons gives no +warranties regarding its licenses, any material licensed under their +terms and conditions, or any related information. Creative Commons +disclaims all liability for damages resulting from their use to the +fullest extent possible. + +Using Creative Commons Public Licenses + +Creative Commons public licenses provide a standard set of terms and +conditions that creators and other rights holders may use to share +original works of authorship and other material subject to copyright +and certain other rights specified in the public license below. The +following considerations are for informational purposes only, are not +exhaustive, and do not form part of our licenses. + + Considerations for licensors: Our public licenses are + intended for use by those authorized to give the public + permission to use material in ways otherwise restricted by + copyright and certain other rights. Our licenses are + irrevocable. Licensors should read and understand the terms + and conditions of the license they choose before applying it. + Licensors should also secure all rights necessary before + applying our licenses so that the public can reuse the + material as expected. Licensors should clearly mark any + material not subject to the license. This includes other CC- + licensed material, or material used under an exception or + limitation to copyright. More considerations for licensors: + wiki.creativecommons.org/Considerations_for_licensors + + Considerations for the public: By using one of our public + licenses, a licensor grants the public permission to use the + licensed material under specified terms and conditions. If + the licensor's permission is not necessary for any reason--for + example, because of any applicable exception or limitation to + copyright--then that use is not regulated by the license. Our + licenses grant only permissions under copyright and certain + other rights that a licensor has authority to grant. Use of + the licensed material may still be restricted for other + reasons, including because others have copyright or other + rights in the material. A licensor may make special requests, + such as asking that all changes be marked or described. + Although not required by our licenses, you are encouraged to + respect those requests where reasonable. More considerations + for the public: + wiki.creativecommons.org/Considerations_for_licensees + +======================================================================= + +Creative Commons Attribution 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree +to be bound by the terms and conditions of this Creative Commons +Attribution 4.0 International Public License ("Public License"). To the +extent this Public License may be interpreted as a contract, You are +granted the Licensed Rights in consideration of Your acceptance of +these terms and conditions, and the Licensor grants You such rights in +consideration of benefits the Licensor receives from making the +Licensed Material available under these terms and conditions. + + +Section 1 -- Definitions. + + a. Adapted Material means material subject to Copyright and Similar + Rights that is derived from or based upon the Licensed Material + and in which the Licensed Material is translated, altered, + arranged, transformed, or otherwise modified in a manner requiring + permission under the Copyright and Similar Rights held by the + Licensor. For purposes of this Public License, where the Licensed + Material is a musical work, performance, or sound recording, + Adapted Material is always produced where the Licensed Material is + synched in timed relation with a moving image. + + b. Adapter's License means the license You apply to Your Copyright + and Similar Rights in Your contributions to Adapted Material in + accordance with the terms and conditions of this Public License. + + c. Copyright and Similar Rights means copyright and/or similar rights + closely related to copyright including, without limitation, + performance, broadcast, sound recording, and Sui Generis Database + Rights, without regard to how the rights are labeled or + categorized. For purposes of this Public License, the rights + specified in Section 2(b)(1)-(2) are not Copyright and Similar + Rights. + + d. Effective Technological Measures means those measures that, in the + absence of proper authority, may not be circumvented under laws + fulfilling obligations under Article 11 of the WIPO Copyright + Treaty adopted on December 20, 1996, and/or similar international + agreements. + + e. Exceptions and Limitations means fair use, fair dealing, and/or + any other exception or limitation to Copyright and Similar Rights + that applies to Your use of the Licensed Material. + + f. Licensed Material means the artistic or literary work, database, + or other material to which the Licensor applied this Public + License. + + g. Licensed Rights means the rights granted to You subject to the + terms and conditions of this Public License, which are limited to + all Copyright and Similar Rights that apply to Your use of the + Licensed Material and that the Licensor has authority to license. + + h. Licensor means the individual(s) or entity(ies) granting rights + under this Public License. + + i. Share means to provide material to the public by any means or + process that requires permission under the Licensed Rights, such + as reproduction, public display, public performance, distribution, + dissemination, communication, or importation, and to make material + available to the public including in ways that members of the + public may access the material from a place and at a time + individually chosen by them. + + j. Sui Generis Database Rights means rights other than copyright + resulting from Directive 96/9/EC of the European Parliament and of + the Council of 11 March 1996 on the legal protection of databases, + as amended and/or succeeded, as well as other essentially + equivalent rights anywhere in the world. + + k. You means the individual or entity exercising the Licensed Rights + under this Public License. Your has a corresponding meaning. + + +Section 2 -- Scope. + + a. License grant. + + 1. Subject to the terms and conditions of this Public License, + the Licensor hereby grants You a worldwide, royalty-free, + non-sublicensable, non-exclusive, irrevocable license to + exercise the Licensed Rights in the Licensed Material to: + + a. reproduce and Share the Licensed Material, in whole or + in part; and + + b. produce, reproduce, and Share Adapted Material. + + 2. Exceptions and Limitations. For the avoidance of doubt, where + Exceptions and Limitations apply to Your use, this Public + License does not apply, and You do not need to comply with + its terms and conditions. + + 3. Term. The term of this Public License is specified in Section + 6(a). + + 4. Media and formats; technical modifications allowed. The + Licensor authorizes You to exercise the Licensed Rights in + all media and formats whether now known or hereafter created, + and to make technical modifications necessary to do so. The + Licensor waives and/or agrees not to assert any right or + authority to forbid You from making technical modifications + necessary to exercise the Licensed Rights, including + technical modifications necessary to circumvent Effective + Technological Measures. For purposes of this Public License, + simply making modifications authorized by this Section 2(a) + (4) never produces Adapted Material. + + 5. Downstream recipients. + + a. Offer from the Licensor -- Licensed Material. Every + recipient of the Licensed Material automatically + receives an offer from the Licensor to exercise the + Licensed Rights under the terms and conditions of this + Public License. + + b. No downstream restrictions. You may not offer or impose + any additional or different terms or conditions on, or + apply any Effective Technological Measures to, the + Licensed Material if doing so restricts exercise of the + Licensed Rights by any recipient of the Licensed + Material. + + 6. No endorsement. Nothing in this Public License constitutes or + may be construed as permission to assert or imply that You + are, or that Your use of the Licensed Material is, connected + with, or sponsored, endorsed, or granted official status by, + the Licensor or others designated to receive attribution as + provided in Section 3(a)(1)(A)(i). + + b. Other rights. + + 1. Moral rights, such as the right of integrity, are not + licensed under this Public License, nor are publicity, + privacy, and/or other similar personality rights; however, to + the extent possible, the Licensor waives and/or agrees not to + assert any such rights held by the Licensor to the limited + extent necessary to allow You to exercise the Licensed + Rights, but not otherwise. + + 2. Patent and trademark rights are not licensed under this + Public License. + + 3. To the extent possible, the Licensor waives any right to + collect royalties from You for the exercise of the Licensed + Rights, whether directly or through a collecting society + under any voluntary or waivable statutory or compulsory + licensing scheme. In all other cases the Licensor expressly + reserves any right to collect such royalties. + + +Section 3 -- License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the +following conditions. + + a. Attribution. + + 1. If You Share the Licensed Material (including in modified + form), You must: + + a. retain the following if it is supplied by the Licensor + with the Licensed Material: + + i. identification of the creator(s) of the Licensed + Material and any others designated to receive + attribution, in any reasonable manner requested by + the Licensor (including by pseudonym if + designated); + + ii. a copyright notice; + + iii. a notice that refers to this Public License; + + iv. a notice that refers to the disclaimer of + warranties; + + v. a URI or hyperlink to the Licensed Material to the + extent reasonably practicable; + + b. indicate if You modified the Licensed Material and + retain an indication of any previous modifications; and + + c. indicate the Licensed Material is licensed under this + Public License, and include the text of, or the URI or + hyperlink to, this Public License. + + 2. You may satisfy the conditions in Section 3(a)(1) in any + reasonable manner based on the medium, means, and context in + which You Share the Licensed Material. For example, it may be + reasonable to satisfy the conditions by providing a URI or + hyperlink to a resource that includes the required + information. + + 3. If requested by the Licensor, You must remove any of the + information required by Section 3(a)(1)(A) to the extent + reasonably practicable. + + 4. If You Share Adapted Material You produce, the Adapter's + License You apply must not prevent recipients of the Adapted + Material from complying with this Public License. + + +Section 4 -- Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that +apply to Your use of the Licensed Material: + + a. for the avoidance of doubt, Section 2(a)(1) grants You the right + to extract, reuse, reproduce, and Share all or a substantial + portion of the contents of the database; + + b. if You include all or a substantial portion of the database + contents in a database in which You have Sui Generis Database + Rights, then the database in which You have Sui Generis Database + Rights (but not its individual contents) is Adapted Material; and + + c. You must comply with the conditions in Section 3(a) if You Share + all or a substantial portion of the contents of the database. + +For the avoidance of doubt, this Section 4 supplements and does not +replace Your obligations under this Public License where the Licensed +Rights include other Copyright and Similar Rights. + + +Section 5 -- Disclaimer of Warranties and Limitation of Liability. + + a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE + EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS + AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF + ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, + IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, + WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR + PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, + ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT + KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT + ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU. + + b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE + TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, + NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, + INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, + COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR + USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN + ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR + DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR + IN PART, THIS LIMITATION MAY NOT APPLY TO YOU. + + c. The disclaimer of warranties and limitation of liability provided + above shall be interpreted in a manner that, to the extent + possible, most closely approximates an absolute disclaimer and + waiver of all liability. + + +Section 6 -- Term and Termination. + + a. This Public License applies for the term of the Copyright and + Similar Rights licensed here. However, if You fail to comply with + this Public License, then Your rights under this Public License + terminate automatically. + + b. Where Your right to use the Licensed Material has terminated under + Section 6(a), it reinstates: + + 1. automatically as of the date the violation is cured, provided + it is cured within 30 days of Your discovery of the + violation; or + + 2. upon express reinstatement by the Licensor. + + For the avoidance of doubt, this Section 6(b) does not affect any + right the Licensor may have to seek remedies for Your violations + of this Public License. + + c. For the avoidance of doubt, the Licensor may also offer the + Licensed Material under separate terms or conditions or stop + distributing the Licensed Material at any time; however, doing so + will not terminate this Public License. + + d. Sections 1, 5, 6, 7, and 8 survive termination of this Public + License. + + +Section 7 -- Other Terms and Conditions. + + a. The Licensor shall not be bound by any additional or different + terms or conditions communicated by You unless expressly agreed. + + b. Any arrangements, understandings, or agreements regarding the + Licensed Material not stated herein are separate from and + independent of the terms and conditions of this Public License. + + +Section 8 -- Interpretation. + + a. For the avoidance of doubt, this Public License does not, and + shall not be interpreted to, reduce, limit, restrict, or impose + conditions on any use of the Licensed Material that could lawfully + be made without permission under this Public License. + + b. To the extent possible, if any provision of this Public License is + deemed unenforceable, it shall be automatically reformed to the + minimum extent necessary to make it enforceable. If the provision + cannot be reformed, it shall be severed from this Public License + without affecting the enforceability of the remaining terms and + conditions. + + c. No term or condition of this Public License will be waived and no + failure to comply consented to unless expressly agreed to by the + Licensor. + + d. Nothing in this Public License constitutes or may be interpreted + as a limitation upon, or waiver of, any privileges and immunities + that apply to the Licensor or You, including from the legal + processes of any jurisdiction or authority. + + +======================================================================= + +Creative Commons is not a party to its public +licenses. Notwithstanding, Creative Commons may elect to apply one of +its public licenses to material it publishes and in those instances +will be considered the “Licensor.” The text of the Creative Commons +public licenses is dedicated to the public domain under the CC0 Public +Domain Dedication. Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. + diff --git a/README.md b/README.md index c9aea9a..b2302a0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,69 @@ # Neuroelectrophysiology Analysis Ontology (NEAO) - The NEAO is a collection of controlled vocabularies and concepts to describe the typical processes involved in the analysis of neural activity data acquired using electrophysiology techniques. -The goal of the NEAO is to provide a common framework for annotating neuroelectrophysiology data analysis workflows. This will result in the unambiguous identification of the processes and data elements involved, and also extended semantic information that can be used to reason about the analyses. +The goal of NEAO is to provide a common framework for annotating neuroelectrophysiology data analysis workflows. This will result in the unambiguous identification of the processes and data elements involved, and also extended semantic information that can be used to reason about the analyses. + +The published ontology is accessible through the main namespace http://purl.org/neao. This repository contains the sources and centralizes the development efforts within the community. + +For requests or additions to NEAO, please first open an issue in the [issue tracker](https://github.com/INM-6/neuroephys_analysis_ontology/issues). + +## Table of contents + +- [Code repository](#code-repository) +- [Acknowledgments](#acknowledgments) +- [License](#license) + +## Code repository + +### OWL sources + +NEAO was developed using [Protégé 5.5](https://protege.stanford.edu/software.php). + +The ontology source code (OWL serialized as Turtle) is organized into subfolders inside the `\src` folder: + +- `base`: the top-level classes of the NEAO model that are imported by other modules. It defines the three main classes, the software implementation description, and all the related properties. +- `steps`: module to extend the **AnalysisStep** base class in order to define the specific analysis steps and their semantic groupings. +- `data`: module to extend the **Data** base class in order to define the specific data entities and their semantic groupings. +- `parameters`: module to extend the **AnalysisParameter** base class in order to define the specific parameters and their semantic groupings. +- `bibliography`: module that defines individuals with the bibliographic references used to annotate **AnalysisStep** classes. + +Each folder contains the OWL file with the source, and an XML catalog file that is used to perform local imports correctly when loading into Protégé. A top-level OWL file `neao.owl` is contained in `\src` and imports all the submodules to produce the final NEAO OWL source. This file should be edited only for the NEAO ontology metadata. + +### Documentation + +The documentation source is found in the `/doc` folder: + +- The HTML pages are built using [WIDOCO](https://github.com/dgarijo/Widoco). NEAO is based on the JAR release that must be downloaded to the local system ([available here](https://github.com/dgarijo/WIDOCO/releases/latest)). The recommended path is `~/opt/widoco/widoco.jar`. + + For the documentation build, `xml_grep` from [xmltwig](https://github.com/mirod/xmltwig/tree/master) is needed to post-process the generated files. In Ubuntu or other Debian based Linux distributions, it can be installed using a package manager: + + `apt install xml-twig-tools` + + Also, as part of the documentation and release process, the code from all modules is merged into a single OWL source using [ROBOT](https://robot.obolibrary.org/). ROBOT must be installed and accessible in the shell, i.e., by runing the `robot` command ('Getting Started' instructions [here]([https://robot.obolibrary.org/](https://robot.obolibrary.org/))). The recommended path for the JAR file and shell script is `/usr/local/bin` (but you can also add the folder with ROBOT files to the `PATH`). + + The complete build is done by running the `build_doc.sh` bash script. The version must be passed as a script argument: + + `./build_doc.sh x.x.x` + + If the WIDOCO JAR is stored in a path other than `~/opt/widoco/widoco.jar` , it must be passed as a second argument: + + `./build_doc.sh x.x.x local/path/to/widoco.jar` + +- The subfolder `/doc/source` contains the configuration files, images, and plain HTML files used with WIDOCO. The documentation sources are separated for each ontology submodule, as WIDOCO generates a separate documentation page for each. After the typical documentation page is built, the generated HTML files are edited to remove sections that are not needed, and the custom HTML text (for the Introduction, Description, and Acknowledgement sections) is inserted. + +- The subfolder `/doc/releases` contains the built HTML documentation and final OWL code of each version of NEAO released. Each release is stored in a subfolder named after the version (as defined when calling the `build_doc.sh` script). + +- To visualize the documentation after the build, the generated files can be inserted into an Apache Docker image (this is required for the [WebVOWL](http://vowl.visualdataweb.org/webvowl.html) visualizations). The Docker files are located in the `/doc/docker` subfolder. Docker must be available and running in your system ([instructions here](https://docs.docker.com/engine/install/)). + + First, the image must be built with the `build.sh` bash script (that will take the latest documentation files in `/doc/releases`). + + After a successful build, the local server can be initialized by running the image with `run.sh`. Documentation will be accessible at [http://localhost:8080](http://localhost:8080). + +## Acknowledgments + +This work was performed as part of the [Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)](https://hds-lee.de) and received funding from the Helmholtz Association of German Research Centres. This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the European Union’s Horizon Europe Programme under the Specific Grant Agreement No. 101147319 ([EBRAINS](https://ebrains.eu) 2.0 Project), the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany (NRW-network "[iBehave](https://ibehave.nrw)", grant number: NW21-049), and the Joint Lab "Supercomputing and Modeling for the Human Brain." + +## License + +CC BY 4.0 (see [LICENSE.txt](LICENSE.txt)) diff --git a/doc/build_doc.sh b/doc/build_doc.sh new file mode 100755 index 0000000..a8686aa --- /dev/null +++ b/doc/build_doc.sh @@ -0,0 +1,215 @@ +#!/bin/bash +set -e + +log_message () { + printf "\n\n[$(date)] $1\n\n" +} + + +insert_html () { + DEST_FILE=$1/index-en.html + DOC_FILES=$2 + log_message "Inserting custom HTML text in '$DOC_FILES' into '$DEST_FILE'" + gawk -i inplace -v r="$(cat $DOC_FILES/introduction.html)" '{gsub(/introduction.html/,r)}1' $DEST_FILE + gawk -i inplace -v r="$(cat $DOC_FILES/description.html)" '{gsub(/description.html/,r)}1' $DEST_FILE + gawk -i inplace -v RS='{ "is multiline" }' -v r="$(cat $DOC_SRC_FOLDER/acknowledgements.html)" '{gsub(/(

\nThe authors would like to thank.*documentation\.<\/p>)/,r)}1' $DEST_FILE +} + + +clean_section () { + DEST_FILE=$1/index-en.html + log_message "Cleaning '$2' from '$DEST_FILE'" + CONDITION="div[@id=\"$2\"]" + xml_grep --html --exclude "$CONDITION" $DEST_FILE > $DEST_FILE.tmp + mv $DEST_FILE.tmp $DEST_FILE +} + + +clean_annotation () { + DEST_FILE=$1 + log_message "Cleaning information for annotation property '$2' from '$DEST_FILE'" + + # List headers + CONDITION="a[@title=\"$2\"]" + xml_grep --html --exclude "$CONDITION" $DEST_FILE > $DEST_FILE.tmp + mv $DEST_FILE.tmp $DEST_FILE + + # Property panel + CONDITION="div[@id=\"$2\"]" + xml_grep --html --root 'div[@id="annotationproperties"]' --exclude "$CONDITION" $DEST_FILE > $DEST_FILE.tmp + mv $DEST_FILE.tmp $DEST_FILE +} + +clean_annotation_properties () { + DEST_FILE=$1/index-en.html + echo "Cleaning annotation_properties from '$DEST_FILE'" + clean_annotation "$DEST_FILE" "http://purl.org/dc/terms/creator" + clean_annotation "$DEST_FILE" "http://purl.org/dc/terms/created" + clean_annotation "$DEST_FILE" "http://purl.org/dc/terms/license" + clean_annotation "$DEST_FILE" "http://www.w3.org/2004/02/skos/core#prefLabel" + clean_annotation "$DEST_FILE" "http://purl.org/vocab/vann/preferredNamespacePrefix" + clean_annotation "$DEST_FILE" "http://purl.org/vocab/vann/preferredNamespaceUri" + gawk --i inplace -v RS='{ "is multiline" }' -v r="" '{gsub(/(

  • \n\s+\n\s+<\/li>)/,r)}1' $DEST_FILE +} + + +rename_files () { + BASE_FOLDER=$1 + mv $BASE_FOLDER/index-en.html $BASE_FOLDER/index.html + mv $BASE_FOLDER/ontology.ttl $BASE_FOLDER/$2.ttl + mv $BASE_FOLDER/ontology.nt $BASE_FOLDER/$2.nt + mv $BASE_FOLDER/ontology.owl $BASE_FOLDER/$2.xml + mv $BASE_FOLDER/ontology.jsonld $BASE_FOLDER/$2.jsonld +} + +remove_files () { + BASE_FOLDER=$1 + rm $BASE_FOLDER/$2.ttl + rm $BASE_FOLDER/$2.nt + rm $BASE_FOLDER/$2.xml + rm $BASE_FOLDER/$2.jsonld +} + +# This script requires xml_grep +if ! command -v xml_grep &> /dev/null +then + printf "\nxml_grep is needed. Please install it using your package manager (e.g., 'apt install xml-twig-tools').\n\n" + exit +fi + +# This script requires ROBOT +if ! command -v robot &> /dev/null +then + printf "\nROBOT is needed. Please install it from 'https://robot.obolibrary.org'.\n\n" + exit +fi + +# Get release version from command line +CURRENT_RELEASE=$1 +if [ -z "$CURRENT_RELEASE" ] +then + printf "\nProvide the current release version to identify which documentation will be built.\n" + printf "The files of the requested release will be built and stored in './releases/'.\n\n" + exit +fi + +# Get WIDOCO JAR path. It can be passed by command line as the second argument +# Otherwise it is assumed to be stored in ~/opt/widoco/widoco.jar +WIDOCO_JAR=$2 +if [ -z "$WIDOCO_JAR" ] +then + WIDOCO_JAR="$HOME/opt/widoco/widoco.jar" +fi + +# Check if WIDOCO is working +if ! java -jar $WIDOCO_JAR --version +then + printf "\nWIDOCO JAR file '$WIDOCO_JAR' not found or does not run.\n\n" + printf "1. Please, provide the path to the JAR file via the CLI:\n\n" + printf " build_doc.sh \n\n" + printf "2. Check that Java is configured correctly.\n\n" + exit +fi + +# Create release folder if needed +mkdir -p ./releases + +# Define folders +DOC_FOLDER=$(realpath ./releases/$CURRENT_RELEASE) +SRC_FOLDER=$(realpath ../src) +DOC_SRC_FOLDER=$(realpath ./source) +log_message "Using NEAO sources in '$SRC_FOLDER'" +log_message "Using documentation configuration and source files in '$DOC_SRC_FOLDER'" + +# Clean current build +log_message "Cleaning any previous build in '$DOC_FOLDER'" +rm -rf $DOC_FOLDER +mkdir -p $DOC_FOLDER + +NEAO_MERGED_SRC=$SRC_FOLDER/neao_merge.owl +rm -f $NEAO_MERGED_SRC + +# Build merged source with ROBOT +log_message "Generating merged OWL source" +robot merge --input $SRC_FOLDER/neao.owl --collapse-import-closure true --output $NEAO_MERGED_SRC + +# Run WIDOCO to build the documentation +# A WIDOCO run is done for each module +# The HTML output can be post-processed to tweak the visualization + +# Main page +log_message "Building main documentation page" +java -jar $WIDOCO_JAR -ontFile $NEAO_MERGED_SRC -outFolder $DOC_FOLDER -uniteSections -rewriteAll -confFile $DOC_SRC_FOLDER/config.properties +mv $DOC_FOLDER/doc/* $DOC_FOLDER # Docs are actually put into a subfolder +rmdir $DOC_FOLDER/doc +clean_section "$DOC_FOLDER" "abstract" +clean_section "$DOC_FOLDER" "namespacedeclarations" +clean_section "$DOC_FOLDER" "overview" +clean_section "$DOC_FOLDER" "crossref" +clean_section "$DOC_FOLDER" "references" +insert_html "$DOC_FOLDER" "$DOC_SRC_FOLDER" +rename_files "$DOC_FOLDER" "neao" + + +# Base module +log_message "Building base module documentation" +java -jar $WIDOCO_JAR -ontFile $SRC_FOLDER/base/base.owl -outFolder $DOC_FOLDER/base -webVowl -uniteSections -rewriteAll -includeAnnotationProperties -doNotDisplaySerializations -confFile $DOC_SRC_FOLDER/base/config-base.properties +clean_section "$DOC_FOLDER/base" "abstract" +clean_section "$DOC_FOLDER/base" "references" +clean_annotation_properties "$DOC_FOLDER/base" +insert_html "$DOC_FOLDER/base" "$DOC_SRC_FOLDER/base" +rename_files "$DOC_FOLDER/base" "base" +remove_files "$DOC_FOLDER/base" "base" + + +# Steps module +log_message "Building steps module documentation" +java -jar $WIDOCO_JAR -ontFile $SRC_FOLDER/steps/steps.owl -outFolder $DOC_FOLDER/steps -webVowl -uniteSections -rewriteAll -ignoreIndividuals -doNotDisplaySerializations -confFile $DOC_SRC_FOLDER/steps/config-steps.properties +clean_section "$DOC_FOLDER/steps" "abstract" +clean_section "$DOC_FOLDER/steps" "references" +insert_html "$DOC_FOLDER/steps" "$DOC_SRC_FOLDER/steps" +rename_files "$DOC_FOLDER/steps" "steps" +remove_files "$DOC_FOLDER/steps" "steps" + + +# Data module +log_message "Building data module documentation" +java -jar $WIDOCO_JAR -ontFile $SRC_FOLDER/data/data.owl -outFolder $DOC_FOLDER/data -webVowl -uniteSections -rewriteAll -ignoreIndividuals -doNotDisplaySerializations -confFile $DOC_SRC_FOLDER/data/config-data.properties +clean_section "$DOC_FOLDER/data" "abstract" +clean_section "$DOC_FOLDER/data" "references" +insert_html "$DOC_FOLDER/data" "$DOC_SRC_FOLDER/data" +rename_files "$DOC_FOLDER/data" "data" +remove_files "$DOC_FOLDER/data" "data" + + +# Parameters module +log_message "Building parameters module documentation" +java -jar $WIDOCO_JAR -ontFile $SRC_FOLDER/parameters/parameters.owl -outFolder $DOC_FOLDER/parameters -webVowl -uniteSections -ignoreIndividuals -rewriteAll -doNotDisplaySerializations -confFile $DOC_SRC_FOLDER/parameters/config-parameters.properties +clean_section "$DOC_FOLDER/parameters" "abstract" +clean_section "$DOC_FOLDER/parameters" "references" +insert_html "$DOC_FOLDER/parameters" "$DOC_SRC_FOLDER/parameters" +rename_files "$DOC_FOLDER/parameters" "parameters" +remove_files "$DOC_FOLDER/parameters" "parameters" + + +# Bibliography module +log_message "Building bibliography module documentation" +java -jar $WIDOCO_JAR -ontFile $SRC_FOLDER/bibliography/bibliography.owl -outFolder $DOC_FOLDER/bibliography -uniteSections -rewriteAll -doNotDisplaySerializations -confFile $DOC_SRC_FOLDER/bibliography/config-bibliography.properties +clean_section "$DOC_FOLDER/bibliography" "abstract" +clean_section "$DOC_FOLDER/bibliography" "references" +insert_html "$DOC_FOLDER/bibliography" "$DOC_SRC_FOLDER/bibliography" +rename_files "$DOC_FOLDER/bibliography" "bibliography" +remove_files "$DOC_FOLDER/bibliography" "bibliography" + + +# Copy images +log_message "Copying images" +cp -r $DOC_SRC_FOLDER/images $DOC_FOLDER/images + + +# Cleanup +log_message "Removing temporary and readme files" +rm -rf ./tmp* +find $DOC_FOLDER -name 'readme.md' -delete + +log_message "Documentation for $CURRENT_RELEASE built!" diff --git a/doc/docker/Dockerfile b/doc/docker/Dockerfile new file mode 100644 index 0000000..dd3f08d --- /dev/null +++ b/doc/docker/Dockerfile @@ -0,0 +1,2 @@ +FROM httpd:2.4 +COPY ./doc/ /usr/local/apache2/htdocs/ diff --git a/doc/docker/build.sh b/doc/docker/build.sh new file mode 100755 index 0000000..e59d038 --- /dev/null +++ b/doc/docker/build.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +RELEASE_PATH=$(realpath ../releases/*) + +# Get latest version +if ! ls -ad $RELEASE_PATH +then + printf "\n\nNo documentation found in '$RELEASE_PATH'\n" + printf "Please, build the documentation first with $(realpath ../build_doc.sh)\n\n" + exit +fi +LATEST_VERSION=$(ls -ad $RELEASE_PATH | tail -n 1) + +# Copy updated documentation files +rm -rf ./doc +cp -r $LATEST_VERSION ./doc + +# Remove any running container +docker stop $(docker ps -q --filter "name=neao_doc") >/dev/null 2>&1 +docker container rm $(docker ps -a -q --filter "name=neao_doc") >/dev/null 2>&1 + +# Build docker image with Apache and documentation +docker build --no-cache -t neao_doc . + +# Prune old images +docker image prune -f diff --git a/doc/docker/run.sh b/doc/docker/run.sh new file mode 100755 index 0000000..a54ea94 --- /dev/null +++ b/doc/docker/run.sh @@ -0,0 +1,10 @@ +#!/bin/bash + +# Remove any running container +docker stop $(docker ps -q --filter "name=neao_doc") >/dev/null 2>&1 +docker container rm $(docker ps -a -q --filter "name=neao_doc") >/dev/null 2>&1 + +# Run image +docker run -dit --name neao_doc -p 8080:80 neao_doc + +echo "open http://localhost:8080/ for visualizing the documentation" diff --git a/doc/source/acknowledgements.html b/doc/source/acknowledgements.html new file mode 100644 index 0000000..663ecd6 --- /dev/null +++ b/doc/source/acknowledgements.html @@ -0,0 +1,3 @@ +

    This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centres. This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the European Union’s Horizon Europe Programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project), the Ministry of Culture and Science of the State of North Rhine-Westphalia, Germany (NRW-network "iBehave", grant number: NW21-049), and the Joint Lab "Supercomputing and Modeling for the Human Brain."

    + +

    The authors would like to thank Silvio Peroni for developing LODE, a Live OWL Documentation Environment, which is used for representing the Cross Referencing Section of this document and Daniel Garijo for developing Widoco, the program used to create the template used in this documentation.

    diff --git a/doc/source/base/config-base.properties b/doc/source/base/config-base.properties new file mode 100644 index 0000000..6a024fd --- /dev/null +++ b/doc/source/base/config-base.properties @@ -0,0 +1,47 @@ + +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology - Base classes +ontologyPrefix=neao_base +ontologyNamespaceURI=http://purl.org/neao/base# +ontologyName=NEAO Base classes +thisVersionURI=http://purl.org/neao/0.1.0/base# +latestVersionURI=http://purl.org/neao/base# +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=base.xml +TurtleSerialization=base.ttl +NTSerialization=base.nt +JSONLDSerialization=base.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/base/description.html b/doc/source/base/description.html new file mode 100644 index 0000000..c2c16ea --- /dev/null +++ b/doc/source/base/description.html @@ -0,0 +1,32 @@ +

    An overview of the main relationships is depicted in the diagram below:

    + +

    + +

    The NEAO ontology model is constructed upon the central AnalysisStep class, that represents any process that generates new data entities (e.g., generating artificial spike trains) or performs specific operations aimed at extracting additional information during the analysis using existing data entities. For example, a time series with the raw signal recorded from an electrode can be low-pass filtered to extract the local field potential (LFP) component (the step produces transformed data). Additionaly, this new time series with the LFP data can be used in another step to compute the power spectral density (the step produces new, derived data). Therefore, every stage of the analysis generates new data or applies particular operations to data entities.

    + +

    The inputs and outputs of the analysis steps are represented by the Data class. This represents any entity storing information needed throughout the analysis. It might represent the data created or transformed by an analysis step, or it can represent data from an electrophysiological recording obtained from neural tissue (or comparable data generated by a simulation). For example, the raw signal time series saved by the recording apparatus, the filtered LFP time series, and the array holding the power spectral density estimates are instances of the Data class.

    + +

    An information entity that regulates the behavior of the analysis step is a parameter, and is represented by the AnalysisParameter class. A parameter does not provide data that is used by the step to produce the output. For example, in a low-pass filtering step in the analysis to produce the LFP, the time series with the raw wideband signal is the data input, and the low-pass frequency cutoff frequency value is a parameter.

    + +

    The three core classes are related by the object properties hasInput, hasOutput, and usesParameter. Furthermore, several annotation properties are used to provide clear and unambiguous descriptions of the entities represented by the classes. These include abbreviations (abbreviation property), bibliographic references (hasBibliographicReference), labels (skos:prefLabel and skos:altLabel properties) and the class description (using rdfs:comment property).

    + +

    For the disambiguation, the skos:prefLabel annotation property provides the preferred label to refer to the entity, and the rdfs:comment annotation provides details to understand the entity (e.g., details of the computation, inputs, and outputs involved in an analysis step). Synonyms are provided by the skos:altLabel property. The NEAO abbreviation annotation property provides suggested abbreviations.

    + +

    The specific analysis method used in a step of the analysis can be implemented by different software codes, such as distinct open-source toolboxes that are available to analyze electrophysiology data. The core NEAO model has two classes describing the software implementation of the analysis step:

    + +
      +

    • The main source of the code used to execute the operations involved in the analysis step is represented by the SoftwareImplementation class. This is the code that take any given data input, transform it, and produce the desired results.

      • +

    • The SoftwarePackage class is used to describe collections of software, such as toolboxes with many functionality for neuroelectrophysiology data analysis (i.e., that bundle together different pieces of code).

      • +
    + +

    The relationship between SoftwareImplementation and SoftwarePackage is established with the property isImplementedInPackage. The relationship between the AnalysisStep and SoftwareImplementation is defined using the isImplementedIn property. The details of the individuals of each class are provided by distinct properties. The version property of SoftwareImplementation is used to define the version of the software. The packageName and packageVersion properties of SoftwarePackage specify the package name and version, respectively.

    + +

    Program and Function are two subclasses of SoftwareImplementation that correspond to the two main approaches used to implement the code for an analysis step:

    + +
      +

    • The entity represented by Program is an executable that has been compiled or a complete script that the operating system can call to perform the analysis step (for example, an executable that reads a file, computes the power spectral density using the Welch technique, and saves a file containing the power spectral density).

      • +

    • The entity represented by Function is a small, reusable code that is used as a building block in larger programs that execute several steps in the analysis.

      • +
    + +

    Specific details of these two forms of implementing software are defined using additional properties. The nameInDefinition property in Function specifies the name that is used in the function declaration and in applications that utilize the function code. The programName property of Program specifies the name of the program as published.

    + diff --git a/doc/source/base/introduction.html b/doc/source/base/introduction.html new file mode 100644 index 0000000..509f7fa --- /dev/null +++ b/doc/source/base/introduction.html @@ -0,0 +1 @@ +

    Thist module defines the core classes and properties used in the Neuroelectrophysiology Analysis Ontology (NEAO).

    diff --git a/doc/source/bibliography/config-bibliography.properties b/doc/source/bibliography/config-bibliography.properties new file mode 100644 index 0000000..65bba04 --- /dev/null +++ b/doc/source/bibliography/config-bibliography.properties @@ -0,0 +1,46 @@ +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology - Bibliographic References +ontologyPrefix=neao_bib +ontologyNamespaceURI=http://purl.org/neao/bibliography# +ontologyName=NEAO Bibliography +thisVersionURI=http://purl.org/neao/0.1.0/bibliography# +latestVersionURI=http://purl.org/neao/bibliography# +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=bibliography.xml +TurtleSerialization=bibliography.ttl +NTSerialization=bibliography.nt +JSONLDSerialization=bibliography.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/bibliography/description.html b/doc/source/bibliography/description.html new file mode 100644 index 0000000..54facec --- /dev/null +++ b/doc/source/bibliography/description.html @@ -0,0 +1,3 @@ +

    The individuals defined in this module are supposed to be imported by the steps module when inserting bibliographic reference information into the classes. + +

    Each bibliography item is represented as an individual of the biro:BibliographicReference class that is related to the analysis steps (defined in the steps module) through the hasBibliographicReference annotation property. Each individual has a dcterms:bibliographicCitation property defining a string with the human-readable citation, and also a biro:references property pointing to resolvable URIs used to reach the resource.

    diff --git a/doc/source/bibliography/introduction.html b/doc/source/bibliography/introduction.html new file mode 100644 index 0000000..fca0572 --- /dev/null +++ b/doc/source/bibliography/introduction.html @@ -0,0 +1 @@ +

    This module contains all individuals representing bibliographic references used throughout the Neuroelectrophysiology Analysis Ontology (NEAO).

    diff --git a/doc/source/config.properties b/doc/source/config.properties new file mode 100644 index 0000000..a2e7b8c --- /dev/null +++ b/doc/source/config.properties @@ -0,0 +1,47 @@ + +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology +ontologyPrefix=neao +ontologyNamespaceURI=http://purl.org/neao/ +ontologyName=NEAO +thisVersionURI=http://purl.org/neao/0.1.0/ +latestVersionURI=http://purl.org/neao/ +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=neao.xml +TurtleSerialization=neao.ttl +NTSerialization=neao.nt +JSONLDSerialization=neao.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/data/config-data.properties b/doc/source/data/config-data.properties new file mode 100644 index 0000000..22bef79 --- /dev/null +++ b/doc/source/data/config-data.properties @@ -0,0 +1,47 @@ + +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology - Data +ontologyPrefix=neao_data +ontologyNamespaceURI=http://purl.org/neao/data# +ontologyName=NEAO Data +thisVersionURI=http://purl.org/neao/0.1.0/data# +latestVersionURI=http://purl.org/neao/data# +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=data.xml +TurtleSerialization=data.ttl +NTSerialization=data.nt +JSONLDSerialization=data.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/data/description.html b/doc/source/data/description.html new file mode 100644 index 0000000..7b8433b --- /dev/null +++ b/doc/source/data/description.html @@ -0,0 +1,12 @@ +

    The classes in this module are subclasses of the Data base class (defined in the base module), and are associated with the distinct steps throughout the analysis (defined in the steps module) by the hasInput and hasOutput object properties.

    + +

    Additional information on the data entity can be described by using additional classes and properties defined in the base module:

    + +
      +

    • The electrophysiology recording source associated with the data can be defined with the hasSource object property that points to individuals of the ElectrophysiologySignalSource class.

      • +

    • In addition, information on the structure of the data (e.g., matrix, array) can be provided with the isRepresentedAs object property that points to individuals of the DataRepresentation class.

      • +
    + +

    The objective of NEAO is not to model the sources and formats of the data, but these two classes above can be used as abstractions to align other ontologies that are defined with that purpose.

    + +

    Finally, several data properties are also defined to provide extended information on the data entity (e.g., isNormalized or isArtificial). These properties can be used to define additional semantic groupings to make inferences on the data and analysis (e.g., artificial data is defined by the ArtificialData class).

    diff --git a/doc/source/data/introduction.html b/doc/source/data/introduction.html new file mode 100644 index 0000000..7da768f --- /dev/null +++ b/doc/source/data/introduction.html @@ -0,0 +1 @@ +

    This module in the Neuroelectrophysiology Analysis Ontology (NEAO) contains classes that represent data entities in the analysis of neuroelectrophysiology data.

    diff --git a/doc/source/description.html b/doc/source/description.html new file mode 100644 index 0000000..fb737ab --- /dev/null +++ b/doc/source/description.html @@ -0,0 +1,64 @@ +

    The NEAO design assumes that neuroelectrophysiology data analysis consists of a sequence of small, atomic steps, each performing a specific action to generate, transform, or characterize data.

    + +

    For example, consider the example below illustrating the sequence of steps to plot the power spectral density (PSD) of a local field potential (LFP) time series recorded by an extracellular electrode implanted in a brain area and saved to a data file. + +

    + +

    First, the raw data is loaded from the file into a data structure containing the voltage time series acquired by the recording equipment. The LFP is the low-frequency component of the extracellular signal (e.g., below 250 Hz), so a low-pass filter with a 250 Hz cutoff is applied to obtain the LFP time series. The PSD is then computed from the filtered data, resulting in an array of power density estimates for various frequency values. This power spectrum can be plotted and saved to a file. In this example, each step takes input data, processes it, and produces output data, with parameters (e.g., the low-pass cutoff frequency for filtering) controlling each step.

    + +

    We propose the NEAO to describe such scenarios: + +

    + +

    NEAO is organized around the central AnalysisStep class to model the atomic steps of the analysis. This class represents any process that generates new data entities (e.g., generating artificial LFP data) or performs specific operations to extract additional information from existing data entities. These operations include data transformations (e.g., filtering the raw signal into the LFP) or computations of new, derived data (e.g., obtaining the PSD from the LFP signal). Thus, each analysis step either acts on existing data entities or produces new data.

    + +

    Two additional classes complete the core of the ontology model: Data and AnalysisParameter.

    + +

    The Data class represents any entity containing information used during the analysis and serves as the input and output for analysis steps. It can represent data obtained from biological electrophysiology recordings or data generated or transformed during analysis steps. In the example of computing the PSD from an electrode signal, the raw signal time series, the filtered LFP time series, the resulting PSD estimate array, and the plot are all instances of the Data class.

    + +

    The AnalysisParameter class represents information entities that control the behavior of an analysis step. These parameters do not provide data to the step but influence its output. In the example above, the filter step uses a 250 Hz low-frequency cutoff parameter to set the bandwidth of the output signal. + +

    +

    Solving ambiguities in the description of neuroelectrophysiology data analysis

    + +

    To accurately describe neuroelectrophysiology data analysis, two key aspects must be considered:

    +
      +

    1. Multiple complementary and overlapping analysis methods can be used to understand a feature of brain activity from the recorded data, with each method's strengths and limitations influencing the results. For instance, various algorithms can compute the PSD of recorded signals, each producing similar measures but with different interpretations based on characteristics such as their frequency resolution.

      2. +

    2. The implementation of a specific analysis method can vary across different software tools, potentially leading to subtle differences in results even when using the same underlying method.

      3. +
    + +

    Therefore, a clear and unambiguous description of both the methodology and the software implementations is essential for reliable insights into neuroelectrophysiology data analysis. NEAO aims to address the ambiguities that can be present when describing the steps, data, and parameters during the analysis:

    + +
      +

    • Naming: NEAO introduces a controlled vocabulary for naming classes, which is associated with a clear description, to avoid confusion with similar terms. Moreover, each class has a clear label, alternative labels to account for synonyms, and common abbreviations.

      • +

    • Code: NEAO structures details of the code executing an analysis step ensuring precise identification of software tools and their versions.

      • +

    • Bibliographic References: NEAO links analysis steps to specific publications ensuring clear identification of the method’s version and its detailed description.

      • +
    +
    + +
    +

    Obtaining broader insights on the analyses by semantic groupings

    +

    The main NEAO classes provide detailed descriptions of specific analysis steps, data, and parameters. One example of a specific step is the computation of a PSD using either the Welch or the multitaper method.

    +

    However, to gain broader insights, general information about the analysis must be available. For instance, instead of querying if an analysis step used a specific method for computing a PSD (such as the Welch or multitaper), one may be interested in identifying if the analysis step is part of a category of methods (that includes any method that computes a PSD).

    +

    NEAO provides classes that introduce those semantic groupings, organizing the information in relevant categories and allowing more generalized insights on the analyses.

    +
    + +
    +

    NEAO organization

    +

    The NEAO definitions are divided into submodules, each defining a small ontology associated with a single namespace.

    +

    The core model is defined in a base module, and each of the three main classes (AnalysisStep, Data, and AnalysisParameter) is expanded in additional modules.

    +

    The documentation for each submodule provides extended details on the implementation, and is accessible in the table below:

    +
    + + + + + + + + + + +
    Individual modules defined in NEAO
    RootThe overall module, that contains the main metadata and publishing information of NEAO.neao\\\<http://purl.org/neao/\\\>
    BaseThe top-level classes of the NEAO model that are imported by other modules. It defines the three main classes, the software implementation description, and all the related properties.neao_base\\\<http://purl.org/neao/base#\\\>
    StepsModule to extend the AnalysisStep base class, in order to define the specific analysis steps and their semantic groupings.neao_steps\\\<http://purl.org/neao/steps#\\\>
    DataModule to extend the Data base class, in order to define the specific data entities and their semantic groupings.neao_data\\\<http://purl.org/neao/data#\\\>
    ParametersModule to extend the AnalysisParameter base class, in order to define the specific parameters and their semantic groupings.neao_params\\\<http://purl.org/neao/parameters#\\\>
    BibliographyDefine individuals with the bibliographic references used to annotate AnalysisStep classes.neao_bib\\\<http://purl.org/neao/bibliography#\\\>
    +
    +
    diff --git a/doc/source/images/NEAO.png b/doc/source/images/NEAO.png new file mode 100644 index 0000000..846f0b5 Binary files /dev/null and b/doc/source/images/NEAO.png differ diff --git a/doc/source/images/NEAO_main.png b/doc/source/images/NEAO_main.png new file mode 100644 index 0000000..d3b706e Binary files /dev/null and b/doc/source/images/NEAO_main.png differ diff --git a/doc/source/images/concept.png b/doc/source/images/concept.png new file mode 100644 index 0000000..bcbd97b Binary files /dev/null and b/doc/source/images/concept.png differ diff --git a/doc/source/introduction.html b/doc/source/introduction.html new file mode 100644 index 0000000..0d97370 --- /dev/null +++ b/doc/source/introduction.html @@ -0,0 +1,7 @@ +

    The Neuroelectrophysiology Analysis Ontology (NEAO) defines a controlled vocabulary and conceptual representations of the typical processes involved in the analysis of neural activity data acquired using electrophysiology techniques.

    + +

    Electrophysiology is a branch of physiology that studies the electrical properties of biological entities. The studies involve measurements of electric potentials and/or currents, as well as electrical manipulations (e.g. stimuli with electric pulses). Neuroelectrophysiology is the application of electrophysiology techniques to investigate the function of neural tissue.

    + +

    Neuroelectrophysiology recording is the process of data acquisition, and it usually involves placing electrodes (of several configurations and types) into a preparation of neural tissue. The data will contain a representation of the voltages or currents in the preparation during the recording session, usually as a time series. The analysis requires specific methods to progressively process and transform the recorded data, which generate results and insights.

    + +

    The scope of the NEAO aims to provide a comprehensive representation of neuroelectrophysiology data and analysis processes, standardizing the description of their properties and relationships. The NEAO does not provide a detailed representation of electrophysiology recording techniques, data acquisition methods/equipment, and experimental settings/subjects. The goal is to ensure a common representation of data analysis that can be used by tools to provide a detailed and semantically-enriched description of the processes involved.

    diff --git a/doc/source/parameters/config-parameters.properties b/doc/source/parameters/config-parameters.properties new file mode 100644 index 0000000..9bf40bd --- /dev/null +++ b/doc/source/parameters/config-parameters.properties @@ -0,0 +1,47 @@ + +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology - Analysis Parameters +ontologyPrefix=neao_params +ontologyNamespaceURI=http://purl.org/neao/parameters# +ontologyName=NEAO Analysis Parameters +thisVersionURI=http://purl.org/neao/0.1.0/parameters# +latestVersionURI=http://purl.org/neao/parameters# +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=parameters.xml +TurtleSerialization=parameters.ttl +NTSerialization=parameters.nt +JSONLDSerialization=parameters.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/parameters/description.html b/doc/source/parameters/description.html new file mode 100644 index 0000000..a924169 --- /dev/null +++ b/doc/source/parameters/description.html @@ -0,0 +1,5 @@ +

    The classes in this module are subclasses of the AnalysisParameter base class and are associated with the analysis steps (defined in the steps module) through the usesParameter object property. These classes can provide semantic information to analysis parameters

    + +

    The parameter entities control the behavior of the analysis step. For example, in a low-pass filtering step in the analysis, where a raw wideband signal is transformed to isolate the frequency components smaller than a cutoff frequency, the time series with the raw signal is the data input, and the low-pass frequency cutoff frequency value is a parameter.

    + +

    A parameter is then represented by an individual associated with the usesParameter object property. This provides flexibility to describe a parameter in detail and make inferences based on semantic information. For example, a low pass filter setting of 250 Hz can be represented by two properties: one for the integer value 250 and another for the unit "Hz". The representation adopted by NEAO enhances machine readability compared to a single string value like "250 Hz". NEAO does not predefine specific properties for the AnalysisParameter class, allowing the use of existing ontologies, such as QUDT, to describe physical quantities. Moreover, the value of usesParameter can be associated with the multiple classes derived from AnalysisParameter that are defined in this module, providing semantic meanings (e.g., using the LowPassFrequencyCutoff class for a low-frequency cutoff parameter).

    diff --git a/doc/source/parameters/introduction.html b/doc/source/parameters/introduction.html new file mode 100644 index 0000000..55e791f --- /dev/null +++ b/doc/source/parameters/introduction.html @@ -0,0 +1,3 @@ +

    This module in the Neuroelectrophysiology Analysis Ontology (NEAO) contains classes that represent parameters in the analyses.

    + +

    A parameter is an information entity that controls the behavior of an analysis step, but does not provide data that is used by the step to produce the output.

    diff --git a/doc/source/steps/config-steps.properties b/doc/source/steps/config-steps.properties new file mode 100644 index 0000000..b0f6cda --- /dev/null +++ b/doc/source/steps/config-steps.properties @@ -0,0 +1,47 @@ + +abstract= +ontologyTitle=Neuroelectrophysiology Analysis Ontology - Analysis Steps +ontologyPrefix=neao_steps +ontologyNamespaceURI=http://purl.org/neao/steps# +ontologyName=NEAO Analysis Steps +thisVersionURI=http://purl.org/neao/0.1.0/steps# +latestVersionURI=http://purl.org/neao/steps# +previousVersionURI= +dateCreated=2024-XX-XX +dateModified= +dateIssued=2024-XX-XX +ontologyRevisionNumber=0.1.0 +licenseURI=https://creativecommons.org/licenses/by/4.0/ +licenseName=CC-BY 4.0 International +citeAs= +DOI= +status=Ontology Specification Draft +logo= +description=description.html +introduction=introduction.html +backwardsCompatibleWith= +publisher= +publisherURI= +publisherInstitution= +publisherInstitutionURI= +authors=Cristiano Köhler;Michael Denker +authorsURI=http://orcid.org/0000-0003-0503-5264;http://orcid.org/0000-0003-1255-7300 +authorsInstitution=Forschungszentrum Jülich;Forschungszentrum Jülich +authorsInstitutionURI=https://ror.org/02nv7yv05;https://ror.org/02nv7yv05 +contributors= +contributorsURI= +contributorsInstitution= +contributorsInstitutionURI= +importedOntologyNames= +importedOntologyURIs= +extendedOntologyNames= +extendedOntologyURIs= +RDFXMLSerialization=steps.xml +TurtleSerialization=steps.ttl +NTSerialization=steps.nt +JSONLDSerialization=steps.jsonld +images= +source= +seeAlso= +fundingGrants= +funders= diff --git a/doc/source/steps/description.html b/doc/source/steps/description.html new file mode 100644 index 0000000..1b9a575 --- /dev/null +++ b/doc/source/steps/description.html @@ -0,0 +1,13 @@ +

    The main classes are subclasses of the AnalysisStep class (defined in the base module), and represent the different methods and procedures used in the analysis to generate new data or to perform specific operations aimed to transform or extract additional information from the input(s). The classes are organized in a taxonomy, and the lowest level of the hierarchy represent specific, fine-grained descriptions of a step used in the analysis.

    + +

    Grouping classes are provided to identify analysis steps according to their semantic similarities. This is used to keep the fine-grained descriptions associated with specific analysis methods (e.g., a step that used either the Welch or multitaper method to compute the power spectral density) while providing the ability to identify the steps in a more general nature (e.g., a step that computed a power spectral density). The grouping classes are defined in the taxonomy hierarchy by subclasses, and also as classes inferred by reasoning with the hasPurpose object property (that points to individuals of the AnalysisPurpose class, such as FunctionalConnectivityPurpose) or data properties defining boolean values (e.g., isBivariate). Therefore, groupings across multiple semantic dimensions are available (e.g., bivariate analyses or functional connectivity analyses).

    + +

    The information regarding the inputs and outputs can be associated with each step by the hasInput and hasOutput object properties that point to individuals that represent data entities (using the classes defined in the data module).

    + +

    The parameters used to control the behavior of the analysis step are defined with the usesParameter object property that points to individuals that represent specific parameters (using the classes defined in the parameters module).

    + +

    The relevant bibliographic references are provided with the hasBibliographicReference annotation property that points to individuals of the biro:BibliographicReference class (defined in the bibliography module). This structures the specific information associated with the analysis step represented by the class, and helps to disambiguate the description of the diversity of methods that are available to analyze neuroelectrophysiology data. This would be the case, for example, of different algorithms and assumptions (e.g., computing the power spectral density using either the Welch or multitaper approach), and the evolution/modifications of a method (e.g., different computations of phase lag index estimates).

    + +

    The details of the software code associated with each analysis step can be provided by the isImplementedIn object property, that points to individuals of the SoftwareImplementation class (defined in the base module).

    + +

    Finally, the hasSubstep object property is used to describe compound analyses. These analyses involve the execution of multiple smaller steps (substeps) that are associated with specific parameters and intermediary data inputs and outputs. The main compound process can point to other individuals of the AnalysisStep class using the hasSubstep property. Therefore, the appropriate semantic information and description associated with either the compound analysis or each individual substep can be provided in the ontology.

    diff --git a/doc/source/steps/introduction.html b/doc/source/steps/introduction.html new file mode 100644 index 0000000..2f12c64 --- /dev/null +++ b/doc/source/steps/introduction.html @@ -0,0 +1 @@ +

    This module in the Neuroelectrophysiology Analysis Ontology (NEAO) contains classes that represent (atomic) steps in the analysis of neuroelectrophysiology data.

    diff --git a/src/base/base.owl b/src/base/base.owl new file mode 100644 index 0000000..dd1c381 --- /dev/null +++ b/src/base/base.owl @@ -0,0 +1,272 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix biro: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + vann:preferredNamespacePrefix "neao_base" ; + vann:preferredNamespaceUri "http://purl.org/neao/base#" ; + rdfs:comment """This module contains the core classes of the Neuroelectrophysiology Analysis Ontology (NEAO) model. + +The NEAO ontology model is constructed upon the central AnalysisStep class, that represents any process that generates new data entities (e.g., generating artificial spike trains) or performs specific operations aimed at extracting additional information during the analysis using existing data entities. For example, a time series with the raw signal recorded from an electrode can be low-pass filtered to extract the local field potential (LFP) component (the step produces transformed data). Additionaly, this new time series with the LFP data can be used in another step to compute the power spectral density (the step produces new, derived data). Therefore, every stage of the analysis generates new data or applies particular operations to data entities. + +The inputs and outputs of the analysis steps are represented by the Data class. This represents any entity storing information needed throughout the analysis. It might represent the data created or transformed by an analysis step, or it can represent data from an electrophysiological recording obtained from neural tissue (or comparable data generated by a simulation). For example, the raw signal time series saved by the recording apparatus, the filtered LFP time series, and the array holding the power spectral density estimates are instances of the Data class. + +An information entity that regulates the behavior of the analysis step is a parameter, and is represented by the AnalysisParameter class. A parameter does not provide data that is used by the step to produce the output. For example, in a low-pass filtering step in the analysis to produce the LFP, the time series with the raw wideband signal is the data input, and the low-pass frequency cutoff frequency value is a parameter. + +The three core classes are related by the object properties hasInput, hasOutput, and usesParameter. Furthermore, several annotation properties are used to provide clear and unambiguous descriptions of the entities represented by the classes. These include abbreviations (abbreviation property), bibliographic references (hasBibliographicReference), labels (SKOS prefLabel and altLabel properties) and the class description (using RDFS comment property). + +The specific analysis method used in a step of the analysis can be implemented by different software codes, such as distinct open-source toolboxes that are available to analyze electrophysiology data. The core NEAO model has two classes describing the software implementation of the analysis step. The main source of the code used to execute the operations involved in the analysis step is represented by the SoftwareImplementation class. This is the code that take any given data input, transform it, and produce the desired results. The SoftwarePackage class is used to describe collections of software, such as toolboxes with many functionality for neuroelectrophysiology data analysis (i.e., that bundle together different pieces of code). The relationship between SoftwareImplementation and SoftwarePackage is established with the property isImplementedInPackage. The relationship between the AnalysisStep and SoftwareImplementation is defined using the isImplementedIn property. The details of the individuals of each class are provided by distinct properties. The version property of SoftwareImplementation is used to define the version of the software. The packageName and packageVersion properties of SoftwarePackage specify the package name and version, respectively. + +Program and Function are two subclasses of SoftwareImplementation that correspond to the two main approaches used to implement the code for an analysis step. The entity represented by Program is an executable that has been compiled or a complete script that the operating system can call to perform the analysis step (for example, an executable that reads a file, computes the power spectral density using the Welch technique, and saves a file containing the power spectral density). The entity represented by Function is a small, reusable code that is used as a building block in larger programs that execute several steps in the analysis. Specific details of these two forms of implementing software are defined using additional properties. The nameInDefinition property in Function specifies the name that is used in the function declaration and in applications that utilize the function code. The programName property of Program specifies the name of the program as published. + +Finally, although NEAO does not aim to model the data acquisition or to give more details of the source and format of the data utilized in the analysis, two classes are defined as abstractions to organize additional information on the entities of the Data class. Data source information can be defined with the ElectrophysiologySignalSource class. For example, this could be used to describe the recording channel, the anatomical features, or the recording technique (e.g., extracellular recording or EEG). Details on the data's structure, which are important for understanding the analysis, can described with the DataRepresentation class. For example, when computing the Pearson correlation coefficient between two binned spike trains, one scalar value is obtained if the input is a pair of spike trains. However, if that analysis step takes a set of binned spike trains, the output could contain a matrix with all the pairwise coefficients computed. These classes are related to the AnalysisStep class using the hasSource and isRepresentedAs properties."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology - Base classes"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/dc/terms/created +dcterms:created rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/creator +dcterms:creator rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/license +dcterms:license rdf:type owl:AnnotationProperty . + + +### http://purl.org/neao/base#abbreviation +:abbreviation rdf:type owl:AnnotationProperty ; + rdfs:comment "A string defining an abbreviation that is frequently used to refer to individuals represented by the class."@en ; + skos:prefLabel "abbreviation"@en ; + rdfs:range xsd:string . + + +### http://purl.org/neao/base#hasBibliographicReference +:hasBibliographicReference rdf:type owl:AnnotationProperty ; + rdfs:comment "Defines the bibliographic reference associated with the entity. The bibliographic reference should contain the details represented by the entity (e.g., method description, software publication)."@en ; + skos:prefLabel "has bibliographic reference"@en ; + rdfs:range biro:BibliographicReference . + + +### http://purl.org/vocab/vann/preferredNamespacePrefix +vann:preferredNamespacePrefix rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespaceUri +vann:preferredNamespaceUri rdf:type owl:AnnotationProperty . + + +### http://www.w3.org/2004/02/skos/core#prefLabel +skos:prefLabel rdf:type owl:AnnotationProperty . + + +################################################################# +# Object Properties +################################################################# + +### http://purl.org/neao/base#hasInput +:hasInput rdf:type owl:ObjectProperty ; + rdfs:domain :AnalysisStep ; + rdfs:range :Data ; + rdfs:comment "Defines a data entity that is used to provide information for one analysis step. The analysis step will make use of this information and produce zero or more data entities as outputs."@en ; + skos:prefLabel "has input"@en . + + +### http://purl.org/neao/base#hasOutput +:hasOutput rdf:type owl:ObjectProperty ; + rdfs:domain :AnalysisStep ; + rdfs:range :Data ; + rdfs:comment "Defines a data entity that was produced by an analysis step. This can be newly data generated by the analysis step, or derived data, based on one or more data inputs."@en ; + skos:prefLabel "has output"@en . + + +### http://purl.org/neao/base#hasSource +:hasSource rdf:type owl:ObjectProperty ; + rdfs:domain :Data ; + rdfs:range :ElectrophysiologySignalSource ; + rdfs:comment "Defines the electrophysiology signal source associated with the data entity. One example of use is to define if the data holds a signal obtained by extracellular recordings, or a more specific description involving anatomical structures together with the recording technique."@en ; + skos:prefLabel "has source"@en . + + +### http://purl.org/neao/base#hasSubstep +:hasSubstep rdf:type owl:ObjectProperty ; + rdfs:domain :AnalysisStep ; + rdfs:range :AnalysisStep ; + rdfs:comment "Points to an analysis step that is part of a (larger) compound analysis, represented by the subject with the property."@en ; + skos:prefLabel "has substep"@en . + + +### http://purl.org/neao/base#isImplementedIn +:isImplementedIn rdf:type owl:ObjectProperty ; + rdfs:domain :AnalysisStep ; + rdfs:range :SoftwareImplementation ; + rdfs:comment "Defines the code source that implemented the analysis step."@en ; + skos:prefLabel "is implemented in"@en . + + +### http://purl.org/neao/base#isImplementedInPackage +:isImplementedInPackage rdf:type owl:ObjectProperty ; + rdfs:domain :SoftwareImplementation ; + rdfs:range :SoftwarePackage ; + rdfs:comment "Defines the software package that contains a particular code."@en ; + skos:prefLabel "is implemented in package"@en . + + +### http://purl.org/neao/base#isRepresentedAs +:isRepresentedAs rdf:type owl:ObjectProperty ; + rdfs:domain :Data ; + rdfs:range :DataRepresentation ; + rdfs:comment "Defines how the data entity is represented to be used by software (e.g., data array, list, file)"@en ; + skos:prefLabel "is represented as"@en . + + +### http://purl.org/neao/base#usesParameter +:usesParameter rdf:type owl:ObjectProperty ; + rdfs:domain :AnalysisStep ; + rdfs:range :AnalysisParameter ; + rdfs:comment "Defines a parameter used by the analysis step."@en ; + skos:prefLabel "uses parameter"@en . + + +################################################################# +# Data properties +################################################################# + +### http://purl.org/neao/base#nameInDefinition +:nameInDefinition rdf:type owl:DatatypeProperty ; + rdfs:domain :Function ; + rdfs:range xsd:string ; + rdfs:comment "Specifies the name of the function as it is defined in the code that implemented it. For example, for Python functions, this is what is specified in the 'def' statement (i.e., def function_name(arg), the value of this property is function_name)."@en ; + skos:prefLabel "name in definition"@en . + + +### http://purl.org/neao/base#packageName +:packageName rdf:type owl:DatatypeProperty ; + rdfs:domain :SoftwarePackage ; + rdfs:range rdfs:Literal ; + rdfs:comment "Version of the software package."@en ; + skos:prefLabel "package version"@en . + + +### http://purl.org/neao/base#packageVersion +:packageVersion rdf:type owl:DatatypeProperty ; + rdfs:domain :SoftwarePackage ; + rdfs:range rdfs:Literal ; + rdfs:comment "Specifies the version of the package, as defined by the string used when it was released."@en ; + skos:prefLabel "package version"@en . + + +### http://purl.org/neao/base#programName +:programName rdf:type owl:DatatypeProperty ; + rdfs:domain :Program ; + rdfs:range xsd:string ; + rdfs:comment "Defines the name of the program."@en ; + skos:prefLabel "program name"@en . + + +### http://purl.org/neao/base#version +:version rdf:type owl:DatatypeProperty ; + rdfs:domain :SoftwareImplementation ; + rdfs:range rdfs:Literal ; + rdfs:comment "Version of the code for a specific program or function. This can be used to define a specific version for the code of a single function or program within a software package."@en ; + skos:prefLabel "version"@en . + + +################################################################# +# Classes +################################################################# + +### http://purl.org/neao/base#AnalysisParameter +:AnalysisParameter rdf:type owl:Class ; + rdfs:comment "An information entity that controls the behavior of an analysis step. It does not provide data, but rather changes how the computations operate when transforming or generating new data."@en ; + skos:prefLabel "parameter"@en . + + +### http://purl.org/neao/base#AnalysisStep +:AnalysisStep rdf:type owl:Class ; + rdfs:comment "A process that generates new data entities or performs specific operations (clean, select, transform, or inspect/visualize) using existing data entities. Ultimately, the goal is to obtain useful information from the data. An analysis step can take zero or more data inputs and can generate one or more data outputs. The behavior of the operation can optionally be controlled by one or more parameters. A complete analysis is composed by one or more analysis steps."@en ; + skos:prefLabel "analysis step"@en . + + +### http://purl.org/neao/base#Data +:Data rdf:type owl:Class ; + rdfs:comment "An entity that represents a piece of data, i.e., relevant information, during the analysis. It can be recorded from a biological entity or from a simulation yielding comparable data, or it can be generated/transformed by other analysis step processes."@en ; + skos:prefLabel "data"@en . + + +### http://purl.org/neao/base#DataRepresentation +:DataRepresentation rdf:type owl:Class ; + rdfs:comment "Defines the formal structure to represent a piece of data."@en ; + skos:prefLabel "data representation"@en . + + +### http://purl.org/neao/base#ElectrophysiologySignalSource +:ElectrophysiologySignalSource rdf:type owl:Class ; + rdfs:comment """Defines the source of a recorded piece of electrophysiology data, i.e., the recorded signals. This is a general concept, that can be expanded to incorporate the anatomical/tissue description from where the signal was recorded, as well as the recording technique. + +For example, voltage potentials can be acquired from extracellular electrodes situtated on the scalp (electroencephalogram), directly on the cortical surface (electrocorticogram) or implanted deep into a specific brain region (multielectrode array)."""@en ; + skos:prefLabel "electrophysiology signal source"@en . + + +### http://purl.org/neao/base#Function +:Function rdf:type owl:Class ; + rdfs:subClassOf :SoftwareImplementation ; + rdfs:comment "A reusable set of instructions that can be used when writing computer programs. A function performs specific tasks, and can be implemented as part of a single computer program or in a software package (library) so that it can be reused across several programs."@en ; + skos:prefLabel "function"@en . + + +### http://purl.org/neao/base#Program +:Program rdf:type owl:Class ; + rdfs:subClassOf :SoftwareImplementation ; + rdfs:comment "A set of instructions that is executed by a computer. The instructions of the program can be executed by an interpreter or compiled to machine code. The program instructs the computer to perform tasks and it is called by the operating system."@en ; + skos:prefLabel "program"@en . + + +### http://purl.org/neao/base#SoftwareImplementation +:SoftwareImplementation rdf:type owl:Class ; + owl:disjointWith :SoftwarePackage ; + rdfs:comment "Describes the details for the execution of the analysis step using a computer. The software contains a sequence or set of instructions that is used by the computer to perform all the computations required to execute the analysis step."@en ; + skos:prefLabel "software implementation"@en . + + +### http://purl.org/neao/base#SoftwarePackage +:SoftwarePackage rdf:type owl:Class ; + rdfs:comment "A collection of one or more programs that can be executed by a computer and/or one or more functions that can be imported and used when writing programs."@en ; + skos:prefLabel "software package"@en . + + +### http://purl.org/spar/biro/BibliographicReference +biro:BibliographicReference rdf:type owl:Class . + + +################################################################# +# General axioms +################################################################# + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :AnalysisParameter + :AnalysisStep + :Data + ) +] . + + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi diff --git a/src/bibliography/bibliography.owl b/src/bibliography/bibliography.owl new file mode 100644 index 0000000..38bf366 --- /dev/null +++ b/src/bibliography/bibliography.owl @@ -0,0 +1,1163 @@ +@prefix : . +@prefix doi: . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix biro: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + vann:preferredNamespacePrefix "neao_bib" ; + vann:preferredNamespaceUri "http://purl.org/neao/bibliography#" ; + rdfs:comment """This module contains all individuals representing bibliographic references used throughout the Neuroelectrophysiology Analysis Ontology. + +The individuals defined in this module are supposed to be imported by the steps module when inserting bibliographic reference information into the classes. + +Each bibliography item is represented as an individual of the biro:BibliographicReference class that is related to the analysis steps (defined in the steps module) through the hasBibliographicReference annotation property. Each individual has a dcterms:bibliographicCitation property defining a string with the human-readable citation, and also a biro:references property pointing to resolvable URIs used to reach the resource."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology - Bibliographic References"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/dc/terms/bibliographicCitation +dcterms:bibliographicCitation rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/created +dcterms:created rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/creator +dcterms:creator rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/license +dcterms:license rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespacePrefix +vann:preferredNamespacePrefix rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespaceUri +vann:preferredNamespaceUri rdf:type owl:AnnotationProperty . + + +### http://www.w3.org/2004/02/skos/core#editorialNote +skos:editorialNote rdf:type owl:AnnotationProperty . + + +### http://www.w3.org/2004/02/skos/core#prefLabel +skos:prefLabel rdf:type owl:AnnotationProperty . + + +################################################################# +# Object Properties +################################################################# + +### http://purl.org/spar/biro/references +biro:references rdf:type owl:ObjectProperty . + + +################################################################# +# Classes +################################################################# + +### http://purl.org/spar/biro/BibliographicReference +biro:BibliographicReference rdf:type owl:Class . + + +################################################################# +# Individuals +################################################################# + +### http://purl.org/neao/bibliography#Aertsen1987_1 +:Aertsen1987_1 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Aertsen, T. Bonhoeffer, & J. Krüger, \"Coherent activity in neuronal populations: analysis and interpretation,\" in E. R. Caianiello (ed) Physics of Cognitive Processes, pp. 1-34, World Scientific, 1987" ; + skos:prefLabel "Aertsen et al. (1987)"@en . + + +### http://purl.org/neao/bibliography#Baccala2001_463 +:Baccala2001_463 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "L. Baccalá & K. Sameshima, \"Partial directed coherence: a new concept in neural structure determination,\" Biol. Cyb., vol. 84, pp. 463-474, 2001" ; + skos:prefLabel "Baccalá & Sameshima (2001)"@en . + + +### http://purl.org/neao/bibliography#Bartlett1950_1 +:Bartlett1950_1 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M.S. Bartlett, \"Periodogram Analysis and Continuous Spectra,\" Biometrika, vol. 37, pp. 1-16, 1950" ; + skos:prefLabel "Bartlett (1950)"@en . + + +### http://purl.org/neao/bibliography#Bokil2010_146 +:Bokil2010_146 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "H. Bokil, P. Andrews, J. Kulkarni, S. Mehta, & P. Mitra, \"Chronux: a platform for analyzing neural signals,\" J. Neurosci. Meth., vol. 192, pp. 146-151, 2010" ; + skos:prefLabel "Bokil et al. (2010)"@en . + + +### http://purl.org/neao/bibliography#Brovelli2004_9849 +:Brovelli2004_9849 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Brovelli, M. Ding, A. Ledberg, Y. Chen, R. Nakamura, & S. Bressler, \"Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by granger causality,\" Proc. Natl. Acad. Sci. U.S.A., vol. 101, pp. 9849-9854, 2004" ; + skos:prefLabel "Brovelli et al. (2004)"@en . + + +### http://purl.org/neao/bibliography#Brown2004_456 +:Brown2004_456 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "E. Brown, R. Kass, & P. Mitra, \"Multiple neural spike train data analysis: state-of-the-art and future challenges,\" Nat. Neurosci., vol. 7, pp. 456-461, 2004" ; + skos:prefLabel "Brown et al. (2004)"@en . + + +### http://purl.org/neao/bibliography#Bruna2018_056011 +:Bruna2018_056011 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "E. P. R. Bruña & F. Maestú, \"Phase locking value revisited: teaching new tricks to an old dog,\" J. Neural Eng., vol. 15, no. 5, p. 056011, 2018" ; + skos:prefLabel "Bruña & Maestú (2018)"@en . + + +### http://purl.org/neao/bibliography#Canolty2006_1626 +:Canolty2006_1626 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "R. Canolty, E. Edwards, S. Dalal, M. Soltani, S. Nagarajan, H. Kirsch, M. Berger, N. Barbaro, & R. Knight, \"High gamma power is phase-locked to theta oscillations in human neocortex,\" Science, vol. 313, pp. 1626-1628, 2006" ; + skos:prefLabel "Canolty et al. (2006)"@en . + + +### http://purl.org/neao/bibliography#Carter1987_236 +:Carter1987_236 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "G. Carter, \"Coherence and time delay estimation,\" Proc. IEEE, vol. 75, pp. 236-255, 1987" ; + skos:prefLabel "Carter (1987)"@en . + + +### http://purl.org/neao/bibliography#Ciba2018_136 +:Ciba2018_136 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Ciba, T. Isomura, Y. Jimbo, A. Bahmer, & C. Thielemann, \"Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony,\" J. Neurosci. Meth., vol. 293, pp. 136-143, 2018" ; + skos:prefLabel "Ciba et al. (2018)"@en . + + +### http://purl.org/neao/bibliography#Cohen2011_811 +:Cohen2011_811 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Cohen & A. Kohn, \"Measuring and interpreting neuronal correlations,\" Nat. Neurosci., vol. 14, pp. 811-819, 2011" ; + skos:prefLabel "Cohen & Kohn (2011)"@en . + + +### http://purl.org/neao/bibliography#Cover2012 +:Cover2012 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Cover & J. Thomas, \"Elements of Information Theory,\" John Wiley and Sons, 2012" ; + skos:prefLabel "Cover & Thomas (2012)"@en . + + +### http://purl.org/neao/bibliography#Cowley2017_242 +:Cowley2017_242 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "B. Cowley, J. Semedo, A. Zandvakili, M. Smith, A. Kohn, & B. Yu, \"Distance Covariance Analysis,\" in A. Singh & J. Zhu (eds) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 54, pp. 242-251, 2017" ; + skos:prefLabel "Cowley et al. (2017)"@en . + + +### http://purl.org/neao/bibliography#Cutts2014_14288 +:Cutts2014_14288 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. S. Cutts & S. J. Eglen, \"Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves,\" J. Neurosci., vol. 34, no. 43, pp. 14288-14303, 2014" ; + skos:prefLabel "Cutts & Eglen (2014)"@en . + + +### http://purl.org/neao/bibliography#Deger2012_443 +:Deger2012_443 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Deger, M. Helias, C. Boucsein, & S. Rotter, \"Statistical properties of superimposed stationary spike trains,\" J. Comput. Neurosci., vol. 32, no. 3, pp. 443-463, 2012" ; + skos:prefLabel "Deger et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Ding2006_0608035 +:Ding2006_0608035 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Ding, Y. Chen, & S. L. Bressler, \"Granger causality: basic theory and application to neuroscience,\" arXiv, q-bio/0608035, 2006. " ; + skos:prefLabel "Ding et al. (2006)"@en . + + +### http://purl.org/neao/bibliography#Eggermont2010_77 +:Eggermont2010_77 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. J. Eggermont, \"Pair-correlation in the time and frequency domain,\" in S. Grün & S. Rotter (eds) Analysis of Parallel Spike Trains, pp. 77-102, Springer, 2010" ; + skos:prefLabel "Eggermont (2010)"@en . + + +### http://purl.org/neao/bibliography#Ewald2012_476 +:Ewald2012_476 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Ewald, L. Marzetti, F. Zappasodi, F. C. Meinecke, & G. Nolte, \"Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space,\" NeuroImage, vol. 60, no. 1, pp. 476-488, 2012" ; + skos:prefLabel "Ewald et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Farge1992_395 +:Farge1992_395 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Farge, \"Wavelet transforms and their applications to turbulence,\" Ann. Rev. Fluid Mech., vol. 24, no. 1, pp. 395-457, 1992" ; + skos:prefLabel "Farge (1992)"@en . + + +### http://purl.org/neao/bibliography#Freeman1975_369 +:Freeman1975_369 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. A. Freeman & C. Nicholson, \"Experimental optimization of current source-density technique for anuran cerebellum,\" J. Neurophysiol., vol. 38, no. 2, pp. 369-382, 1975" ; + skos:prefLabel "Freeman & Nicholson (1975)"@en . + + +### http://purl.org/neao/bibliography#Fries2001_1560 +:Fries2001_1560 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "P. Fries, J. Reynolds, A. Rorie, & R. Desimone, \"Modulation of oscillatory neuronal synchronization by selective visual attention,\" Science, vol. 291, pp. 1560-1563, 2001" ; + skos:prefLabel "Fries et al. (2001)"@en . + + +### http://purl.org/neao/bibliography#Georgopoulos1983_327 +:Georgopoulos1983_327 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. P. Georgopoulos, R. Caminiti, J. F. Kalaska, & J. T. Massey, \"Spatial coding of movement: A hypothesis concerning the coding of movement direction by motor cortical populations,\" Exp. Brain Res., vol. 49, pp. 327-336, 1983" ; + skos:prefLabel "Georgopoulos et al. (1983)"@en . + + +### http://purl.org/neao/bibliography#Geweke1982_304 +:Geweke1982_304 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. Geweke, \"Measurement of linear dependence and feedback between multiple time series,\" J. Am. Stat. Assoc., vol. 77, pp. 304-313, 1982" ; + skos:prefLabel "Geweke (1982)"@en . + + +### http://purl.org/neao/bibliography#Gruen1999_67 +:Gruen1999_67 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, M. Diesmann, F. Grammont, A. Riehle, & A. Aertsen, \"Detecting unitary events without discretization of time,\" J. Neurosci. Meth., vol. 94, no. 1, pp. 67-79, 1999" ; + skos:prefLabel "Grün et al. (1999)"@en . + + +### http://purl.org/neao/bibliography#Gruen2002_43 +:Gruen2002_43 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, M. Diesmann, & A. Aertsen, \"Unitary events in multiple single-neuron spiking activity: I. detection and significance,\" Neural Comp., vol. 14, no. 1, pp. 43-80, 2002" ; + skos:prefLabel "Grün et al. (2002a)"@en . + + +### http://purl.org/neao/bibliography#Gruen2002_81 +:Gruen2002_81 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, M. Diesmann, & A. Aertsen, \"Unitary events in multiple single-neuron spiking activity: II. nonstationary data,\" Neural Comp., vol. 14, no. 1, pp. 81-119, 2002" ; + skos:prefLabel "Grün et al. (2002b)"@en . + + +### http://purl.org/neao/bibliography#Gruen2003_335 +:Gruen2003_335 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, A. Riehle, & M. Diesmann, \"Effects across trial non-stationarity on joint-spike events,\" Biol. Cyb., vol. 88, no. 5, pp. 335-51, 2003" ; + skos:prefLabel "Grün et al. (2003)"@en . + + +### http://purl.org/neao/bibliography#Gruen2007_96 +:Gruen2007_96 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, M. Abeles, & M. Diesmann, \"Impact of higher-order correlations on coincidence distributions of massively parallel data,\" in M. Marinaro, S. Scarpetta, & Y. Yamaguchi (eds) Dynamic Brain - from Neural Spikes to Behaviors. NN 2007. LNCS, vol. 5286, pp. 96-114, Springer, 2007" ; + skos:prefLabel "Grün et al. (2007)"@en . + + +### http://purl.org/neao/bibliography#Gruen2009_1126 +:Gruen2009_1126 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Grün, \"Data-driven significance estimation of precise spike correlation,\" J. Neurophysiol., no. 101, pp. 1126-1140, 2009" ; + skos:prefLabel "Grün (2009)"@en . + + +### http://purl.org/neao/bibliography#Hafner2008_215 +:Hafner2008_215 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. Hafner & H. Herwartz, \"Testing for causality in variance using multivariate GARCH models,\" Ann. Econ. Statist., vol. 89, pp. 215-241, 2008" ; + skos:prefLabel "Hafner & Herwartz (2008)"@en . + + +### http://purl.org/neao/bibliography#Hipp2012_884 +:Hipp2012_884 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. F. Hipp, D. J. Hawellek, M. Corbetta, M. Siegel, & A. K. Engel, \"Large-scale cortical correlation structure of spontaneous oscillatory activity,\" Nat. Neurosci., vol. 15, no. 6, pp. 884-890, 2012" ; + skos:prefLabel "Hipp et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Jutten1991_1 +:Jutten1991_1 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. Jutten & J. Herault, \"Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture,\" Signal Process., vol. 24, no. 1, pp. 1-10, 1991" ; + skos:prefLabel "Jutten & Herault (1991)"@en . + + +### http://purl.org/neao/bibliography#Kaminski1991_203 +:Kaminski1991_203 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Kaminski & K. Blinowska, \"A new method of the description of the information flow in the brain structures,\" Biol. Cyb., vol. 65, pp. 203-210, 1991" ; + skos:prefLabel "Kaminski & Blinowska (1991)"@en . + + +### http://purl.org/neao/bibliography#Kobak2016_e10989 +:Kobak2016_e10989 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "D. Kobak, W. Brendel, C. Constantinidis, C. E. Feierstein, A. Kepecs, Z. F. Mainen, X.-L. Qi, R. Romo, N. Uchida, & C. K. Machens, \"Demixed principal component analysis of neural population data,\" Elife, vol. 5, p. e10989, 2016" ; + skos:prefLabel "Kobak et al. (2016)"@en . + + +### http://purl.org/neao/bibliography#Kreuz2007_151 +:Kreuz2007_151 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Kreuz, J. Haas, A. Morelli, H. Abarbanel, & A. Politi, \"Measuring spike train synchrony,\" J. Neurosci. Meth., vol. 165, pp. 151-161, 2007" ; + skos:prefLabel "Kreuz et al. (2007)"@en . + + +### http://purl.org/neao/bibliography#Kreuz2012_1457 +:Kreuz2012_1457 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Kreuz, D. Chicharro, C. Houghton, R. Andrzejak, & F. Mormann, \"Monitoring spike train synchrony,\" J. Neurophysiol., vol. 109, pp. 1457-1472, 2012" ; + skos:prefLabel "Kreuz et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Kreuz2015_3432 +:Kreuz2015_3432 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Kreuz, M. Mulansky, & N. Bozanic, \"SPIKY: a graphical user interface for monitoring spike train synchrony,\" J. Neurophysiol., vol. 113, pp. 3432-3445, 2015" ; + skos:prefLabel "Kreuz et al. (2015)"@en . + + +### http://purl.org/neao/bibliography#Kuhn2003_67 +:Kuhn2003_67 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Kuhn, A. Aertsen, & S. Rotter, \"Higher-order statistics of input ensembles and the response of simple model neurons,\" Neural comp., vol 15, no. 1, pp. 67-101, 2003" ; + skos:prefLabel "Kuhn et al. (2003)"@en . + + +### http://purl.org/neao/bibliography#Lachaux1999_194 +:Lachaux1999_194 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + dcterms:bibliographicCitation "J.-P. Lachaux, E. Rodriguez, J. Martinerie, & F. J. Varela, \"Measuring phase synchrony in brain signals,\" Hum. Brain Mapp., vol. 8, no. 4, pp. 194-208, 1999" ; + rdfs:comment "https://doi.org/10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c" ; + skos:editorialNote "DOI URL is not compatible. Added as rdfs:comment instead of biro:references."@en ; + skos:prefLabel "Lachaux et al. (1999)"@en . + + +### http://purl.org/neao/bibliography#LeVanQuyen2001_83 +:LeVanQuyen2001_83 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Le Van Quyen, J. Foucher, J.-P. Lachaux, E. Rodriguez, A. Lutz, J. Martinerie, and F. J. Varela, \"Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony,\" J. Neurosci. Meth., vol. 111, no. 2, pp. 83-98, 2001" ; + skos:prefLabel "Le Van Quyen et al. (2001)"@en . + + +### http://purl.org/neao/bibliography#Leski2007_207 +:Leski2007_207 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Łęski, D. K. Wójcik, J. Tereszczuk, D. A. Świejkowski, E. Kublik, & A. Wróbel, \"Inverse current-source density method in 3D: reconstruction fidelity, boundary effects, and influence of distant sources,\" Neuroinformatics, vol. 5, no. 4, pp. 207-222, 2007" ; + skos:prefLabel "Łęski et al. (2007)"@en . + + +### http://purl.org/neao/bibliography#Leski2011_401 +:Leski2011_401 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Łęski, K. H. Pettersen, B. Tunstall, G. T. Einevoll, J. Gigg, & D. K. Wójcik, \"Inverse Current Source Density method in two dimensions: Inferring neural activation from multielectrode recordings,\" Neuroinformatics, vol. 9, no. 4, pp. 401-425, 2011" ; + skos:prefLabel "Łęski et al. (2011)"@en . + + +### http://purl.org/neao/bibliography#Loader2006 +:Loader2006 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. Loader, \"Local Regression and Likelihood,\" Springer Science and Business Media, 2006" ; + skos:prefLabel "Loader (2006)"@en . + + +### http://purl.org/neao/bibliography#Louis2010_127 +:Louis2010_127 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Louis, G. L. Gerstein, S. Grün, & M. Diesmann, \"Surrogate spike train generation through dithering in operational time,\" Front. Comput. Neurosci., vol. 4, p. 127, 2010" ; + skos:prefLabel "Louis et al. (2010a)"@en . + + +### http://purl.org/neao/bibliography#Louis2010_359 +:Louis2010_359 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Louis, C. Borgelt, & S. Grün, \"Generation and selection of surrogate methods for correlation analysis,\" in S. Grün & S. Rotter (eds) Analysis of Parallel Spike Trains, vol. 7, pp. 359-382, Springer, 2010" ; + skos:prefLabel "Louis et al. (2010b)"@en . + + +### http://purl.org/neao/bibliography#Messer2014_2027 +:Messer2014_2027 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Messer, M. Kirchner, J. Schiemann, J. Roeper, R. Neininger, & G. Schneider, \"A multiple filter test for the detection of rate changes in renewal processes with varying variance,\" Ann. Appl. Stat., vol. 8, no. 4, pp. 2027-2067, 2014" ; + skos:prefLabel "Messer et al. (2014)"@en . + + +### http://purl.org/neao/bibliography#Mewett2004_524 +:Mewett2004_524 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "D. Mewett, K. Reynolds, & H. Nazeran, \"Reducing power line interference in digitised electromyogram recordings by spectrum interpolation,\" Med. Biol. Eng. Comput., vol. 42, pp. 524-531, 2004 " ; + skos:prefLabel "Mewett et al. (2004)"@en . + + +### http://purl.org/neao/bibliography#Nawrot2008_374 +:Nawrot2008_374 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. P. Nawrot, C. Boucsein, V. R. Molina, A. Riehle, A. Aertsen, & S. Rotter, \"Measurement of variability dynamics in cortical spike trains,\" J. Neurosci. Meth., vol. 169, no. 2, pp. 374-390, 2008" ; + skos:prefLabel "Nawrot et al. (2008)"@en . + + +### http://purl.org/neao/bibliography#Nolte2004_2292 +:Nolte2004_2292 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "G. Nolte, O. Bai, L. Wheaton, Z. Mari, S. Vorbach, & M. Hallett, \"Identifying true brain interaction from EEG data using the imaginary part of coherency,\" Clin. Neurophysiol., vol. 115, pp. 2292-2307, 2004" ; + skos:prefLabel "Nolte et al. (2004)"@en . + + +### http://purl.org/neao/bibliography#Nolte2008_234101 +:Nolte2008_234101 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "G. Nolte, A. Ziehe, V. V. Nikulin, A. Schlögl, N. Krämer, T. Brismar, & K.-R. Müller, \"Robustly estimating the flow direction of information in complex physical systems,\" Phys. Rev. Lett., vol. 100, p. 234101, 2008" ; + skos:prefLabel "Nolte et al. (2008)"@en . + + +### http://purl.org/neao/bibliography#Ozkurt2011_438 +:Ozkurt2011_438 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Özkurt & A. Schnitzler, \"A critical note on the definition of phase–amplitude cross-frequency coupling,\" J. Neurosci. Meth., vol. 201, pp. 438-443, 2011" ; + skos:prefLabel "Özkurt & Schnitzler (2011)"@en . + + +### http://purl.org/neao/bibliography#Pettersen2006_116 +:Pettersen2006_116 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "K. H. Pettersen, A. Devor, I. Ulbert, A. M. Dale, & G. T. Einevoll, \"Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities,\" J. Neurosci. Meth., vol. 154, pp. 116-133, 2006" ; + skos:prefLabel "Pettersen et al. (2006)"@en . + + +### http://purl.org/neao/bibliography#Potworowski2012_541 +:Potworowski2012_541 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. Potworowski, W. Jakuczun, S. Łęski, & D. K. Wójcik, \"Kernel Current Source Density Method,\" Neural Comp., vol. 24, pp. 541-575, 2012" ; + skos:prefLabel "Potworowski et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Quaglio2017_41 +:Quaglio2017_41 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "P. Quaglio, A. Yegenoglu, E. Torre, D. M. Endres, & S. Grün, \"Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data With SPADE,\" Front. Comput. Neurosci., vol. 11, p. 41, 2017" ; + skos:prefLabel "Quaglio et al. (2017)"@en . + + +### http://purl.org/neao/bibliography#Riehle1997_1950 +:Riehle1997_1950 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Riehle, S. Grün, M. Diesmann, & A. Aertsen, \"Spike synchronization and rate modulation differentially involved in motor cortical function,\" Science, vol. 278, no. 5345, pp. 1950-1953, 1997" ; + skos:prefLabel "Riehle et al. (1997)"@en . + + +### http://purl.org/neao/bibliography#Rosenberg1989_1 +:Rosenberg1989_1 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. R. Rosenberg, A. M. Amjad, P. Breeze, D. R. Brillinger, & D. M. Halliday, \"The Fourier approach to the identification of functional coupling between neuronal spike trains,\" Prog. Biophys. Mol. Biol., vol. 53, no. 1, pp. 1-31, 1989" ; + skos:prefLabel "Rosenberg et al. (1989)"@en . + + +### http://purl.org/neao/bibliography#Rosenberg1998_57 +:Rosenberg1998_57 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. Rosenberg, D. Halliday, P. Breeze, & B. Conway, \"Identification of patterns of neuronal connectivity partial spectra, partial coherence, and neuronal interactions,\" J. Neurosci. Meth., vol. 83, pp. 57-72, 1998" ; + skos:prefLabel "Rosenberg et al. (1998)"@en . + + +### http://purl.org/neao/bibliography#Rossum2001_751 +:Rossum2001_751 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. C. van Rossum, \"A novel spike distance,\" Neural Comp., vol. 13, no. 4, pp. 751-763, 2001" ; + skos:prefLabel "van Rossum (2001)"@en . + + +### http://purl.org/neao/bibliography#Russo2017_e19428 +:Russo2017_e19428 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "E. Russo & D. Durstewitz, \"Cell assemblies at multiple time scales with arbitrary lag constellations,\" Elife, vol. 6, p. e19428, 2017" ; + skos:prefLabel "Russo & Durstewitz (2017)"@en . + + +### http://purl.org/neao/bibliography#Schreiber2000_461 +:Schreiber2000_461 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "T. Schreiber, \"Measuring information transfer,\" Phys. Rev. Lett., vol. 85, pp. 461-464, 2000" ; + skos:prefLabel "Schreiber (2000)"@en . + + +### http://purl.org/neao/bibliography#Scott1979_605 +:Scott1979_605 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "D. Scott, \"On optimal and data-based histograms,\" Biometrika, vol. 66, pp. 605-610, 1979" ; + skos:prefLabel "Scott (1979)"@en . + + +### http://purl.org/neao/bibliography#Shimazaki2010_171 +:Shimazaki2010_171 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "H. Shimazaki & S. Shinomoto, \"Kernel bandwidth optimization in spike rate estimation,\" J. Comput. Neurosci., vol. 29, no. 1-2, pp. 171-182, 2010" ; + skos:prefLabel "Shimazaki & Shinomoto (2010)"@en . + + +### http://purl.org/neao/bibliography#Shinomoto2003_2823 +:Shinomoto2003_2823 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Shinomoto, K. Shima, & J. Tanji, \"Differences in spiking patterns among cortical neurons,\" Neural Comp., vol. 15, no. 12, pp. 2823-2842, 2003" ; + skos:prefLabel "Shinomoto et al. (2003)"@en . + + +### http://purl.org/neao/bibliography#Shinomoto2009_e1000433 +:Shinomoto2009_e1000433 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Shinomoto, H. Kim, T. Shimokawa, N. Matsuno, S. Funahashi, K. Shima, I. Fujita, H. Tamura, T. Doi, K. Kawano, N. Inaba, K. Fukushima, S. Kurkin, K. Kurata, M. Taira, K.-I. Tsutsui, H. Komatsu, T. Ogawa, K. Koida, J. Tanji, K. Toyama, \"Relating neuronal firing patterns to functional differentiation of cerebral cortex,\" PLoS Comput. Biol., vol. 5, no. 7, p. e1000433, 2009" ; + skos:prefLabel "Shinomoto et al. (2009)"@en . + + +### http://purl.org/neao/bibliography#Sorensen2013_228 +:Sorensen2013_228 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Sørensen & L. De Lathauwer, \"Coupled tensor decompositions for applications in array signal processing,\" 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 228-231, 2013" ; + skos:prefLabel "Sørensen & De Lathauwer (2013)"@en . + + +### http://purl.org/neao/bibliography#Stam2007_1178 +:Stam2007_1178 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. J. Stam, G. Nolte, & A. Daffertshofer, \"Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources,\" Hum. Brain Mapp., vol. 28, no. 11, pp. 1178-1193, 2007" ; + skos:prefLabel "Stam et al. (2007)"@en . + + +### http://purl.org/neao/bibliography#Stam2012_1415 +:Stam2012_1415 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. J. Stam & E. C. W. van Straaten, \"Go with the flow: use of a directed phase lag index (dpli) to characterize patterns of phase relations in a large-scale model of brain dynamics,\" NeuroImage, vol. 62, no. 3, pp. 1415-1428, 2012" ; + skos:prefLabel "Stam & van Straaten (2012)"@en . + + +### http://purl.org/neao/bibliography#Staude2010_327 +:Staude2010_327 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "B. Staude, S. Rotter, & S. Grün, \"CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains,\" J. Comput. Neurosci., vol. 29, no. 1-2, pp. 327-350, 2010" ; + skos:prefLabel "Staude et al. (2010)"@en . + + +### http://purl.org/neao/bibliography#Stella2019_104022 +:Stella2019_104022 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Stella, P. Quaglio, E. Torre, & S. Grün, \"3d-SPADE: Significance evaluation of spatio-temporal patterns of various temporal extents,\" Biosystems, vol. 185, p. 104022, 2019" ; + skos:prefLabel "Stella et al. (2019)"@en . + + +### http://purl.org/neao/bibliography#Stella2022_ENEURO +:Stella2022_ENEURO rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Stella, P. Bouss, G. Palm, & S. Grün, \"Comparing Surrogates to Evaluate Precisely Timed Higher-Order Spike Correlations,\" eNeuro, vol. 9, no. 3, p. ENEURO.0505-21.2022, 2022" ; + skos:prefLabel "Stella et al. (2022)"@en . + + +### http://purl.org/neao/bibliography#Stockwell1996_998 +:Stockwell1996_998 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "R. G. Stockwell, L. Mansinha, R. P. Lowe, \"Localization of the complex spectrum: the S transform,\" IEEE Trans. Signal Process., vol. 44, no. 4, pp. 998-1001, 1996" ; + skos:prefLabel "Stockwell et al. (1996)"@en . + + +### http://purl.org/neao/bibliography#Stoica2005 +:Stoica2005 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "P. Stoica & R. L. Moses, \"Spectral analysis of signals,\" Prentice Hall, 2005" ; + skos:prefLabel "Stoica & Moses (2005)"@en . + + +### http://purl.org/neao/bibliography#TallonBaudry1997_722 +:TallonBaudry1997_722 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. Tallon-Baudry, O. Bertrand, C. Delpuech, & J. Pernier, \"Oscillatory γ-band (30-70 hz) activity induced by a visual search task in humans,\" J. Neurosci., vol. 17, pp. 722-734, 1997" ; + skos:prefLabel "Tallon-Baudry et al. (1997)"@en . + + +### http://purl.org/neao/bibliography#Thomson1982_1055 +:Thomson1982_1055 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "D. Thomson, \"Spectrum estimation and harmonic analysis,\" Proc. IEEE, vol. 70, no. 9, pp. 1055-1096, 1982 " ; + skos:prefLabel "Thomson (1982)"@en . + + +### http://purl.org/neao/bibliography#Tipping1999_611 +:Tipping1999_611 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. E. Tipping & C. M. Bishop, \"Probabilistic Principal Component Analysis,\" J. R. Stat. Soc. Ser. B Stat. Method., vol. 61, no. 3, pp. 611-622, 1999" ; + skos:prefLabel "Tipping & Bishop (1999)"@en . + + +### http://purl.org/neao/bibliography#Torre2016_e1004939 +:Torre2016_e1004939 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "E. Torre, C. Canova, M. Denker, G. Gerstein, M. Helias, & S. Grün, \"ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains,\" PLoS Comput. Biol., vol. 12, no. 7, p. e1004939, 2016" ; + skos:prefLabel "Torre et al. (2016)"@en . + + +### http://purl.org/neao/bibliography#Tort2010_1195 +:Tort2010_1195 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. Tort, R. Komorowski, H. Eichenbaum, & N. Kopell, \"Measuring phase-amplitude coupling between neuronal oscillations of different frequencies,\" J. Neurophysiol., vol. 104, pp. 1195-1210, 2010" ; + skos:prefLabel "Tort et al. (2010)"@en . + + +### http://purl.org/neao/bibliography#Vaknin1988_131 +:Vaknin1988_131 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "G. Vaknin, P. G. DiScenna, & T. J. Teyler, \"A method for calculating current source density (CSD) analysis without resorting to recording sites outside the sampling volume,\" J. Neurosci. Meth., vol. 24, no. 2, pp. 131-135, 1988" ; + skos:prefLabel "Vaknin et al. (1988)"@en . + + +### http://purl.org/neao/bibliography#Victor1996_1310 +:Victor1996_1310 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "J. Victor & K. Purpura, \"Nature and precision of temporal coding in visual cortex: a metric-space analysis,\" J. Neurophysiol., vol. 76, pp. 1310-1326, 1996" ; + skos:prefLabel "Victor & Purpura (1996)"@en . + + +### http://purl.org/neao/bibliography#Vidaurre2019_116009 +:Vidaurre2019_116009 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "C. Vidaurre, G. Nolte, I. E. J. de Vries, M. Gómez, T. W. Boonstra, K.-R. Müller, A. Villringer, & V. V. Nikulin, \"Canonical maximization of coherence: a novel tool for investigation of neuronal interactions between two datasets,\" NeuroImage, vol. 201, p. 116009, 2019" ; + skos:prefLabel "Vidaurre et al. (2019)"@en . + + +### http://purl.org/neao/bibliography#Vinck2010_112 +:Vinck2010_112 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Vinck, M. van Wingerden, T. Womelsdorf, P. Fries, & C. Pennartz, \"The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization,\" NeuroImage, vol. 51, pp. 112-122, 2010" ; + skos:prefLabel "Vinck et al. (2010)"@en . + + +### http://purl.org/neao/bibliography#Vinck2011_1548 +:Vinck2011_1548 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Vinck, R. Oostenveld, M. van Wingerden, F. Battaglia, & C. Pennartz, \"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias,\" NeuroImage, vol. 55, no. 4, pp. 1548-1565, 2011" ; + skos:prefLabel "Vinck et al. (2011)"@en . + + +### http://purl.org/neao/bibliography#Vinck2012_53 +:Vinck2012_53 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "M. Vinck, F. P. Battaglia, T. Womelsdorf, & C. Pennartz, \"Improved measures of phase-coupling between spikes and the Local Field Potential,\" J. Comput. Neurosci., vol. 33, pp. 53-75, 2012" ; + skos:prefLabel "Vinck et al. (2012)"@en . + + +### http://purl.org/neao/bibliography#Welch1967_70 +:Welch1967_70 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "P. Welch, \"The use of the fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,\" IEEE Trans. Audio Electroacoust., vol. 15, pp. 70-73, 1967" ; + skos:prefLabel "Welch (1967)"@en . + + +### http://purl.org/neao/bibliography#Wen2013_20110610 +:Wen2013_20110610 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "X. Wen, G. Rangarajan, & M. Ding, \"Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix,\" Phil. Trans. R. Soc. A, vol. 371, no. 1997, p. 20110610, 2013" ; + skos:prefLabel "Wen et al. (2013)"@en . + + +### http://purl.org/neao/bibliography#Wieland2015_040901 +:Wieland2015_040901 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "S. Wieland, D. Bernardi, T. Schwalger, & B. Lindner, \"Slow fluctuations in recurrent networks of spiking neurons,\" Phys. Rev. E, vol. 92, no. 4, p. 040901, 2015" ; + skos:prefLabel "Wieland et al. (2015)"@en . + + +### http://purl.org/neao/bibliography#Williams2018_1099 +:Williams2018_1099 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "A. H. Williams, T. H. Kim, F. Wang, S. Vyas, S. I. Ryu, K. V. Shenoy, M. Schnitzer, T. G. Kolda, & S. Ganguli, \"Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis,\" Neuron, vol. 98, no. (6), pp. 1099-1115.e8, 2018" ; + skos:prefLabel "Williams et al. (2018)"@en . + + +### http://purl.org/neao/bibliography#Yu2009_614 +:Yu2009_614 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + biro:references ; + dcterms:bibliographicCitation "B. M. Yu, J. P. Cunningham, G. Santhanam, S. Ryu, K. V. Shenoy, & M. Sahani, \"Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity,\" J. Neurophysiol., vol. 102, no. 1, pp. 614-635, 2009" ; + skos:prefLabel "Yu et al. (2009)"@en . + + +### http://purl.org/neao/bibliography#Dhamala2008_354 +:Dhamala2008_354 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + skos:prefLabel "Dhamala et al. (2008)"@en ; + biro:references ; + dcterms:bibliographicCitation "M. Dhamala, G. Rangarajan, & M. Ding, \"Analyzing information flow in brain networks with nonparametric Granger causality,\" NeuroImage, vol. 41, no. 2, pp. 354-362, 2008" . + + +### http://purl.org/neao/bibliography#Wilson1972_420 +:Wilson1972_420 rdf:type owl:NamedIndividual , + biro:BibliographicReference ; + skos:prefLabel "Wilson (1972)"@en ; + biro:references ; + dcterms:bibliographicCitation "G. T. Wilson, \"The Factorization of Matricial Spectral Densities,\" SIAM J. Appl. Math., vol. 23, no. 4, pp. 420-426, 1972" . + + +### https://doi.org/10.1016/j.neuroimage.2008.02.020 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1137/0123044 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1002/hbm.20346 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/BF00198091 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/BF02350994 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/PL00007990 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/b98858 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s00422-002-0386-2 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s10827-009-0180-4 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s10827-009-0195-x + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s10827-011-0362-8 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s10827-011-0374-4 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s12021-007-9000-z + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/s12021-011-9111-4 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/978-1-4419-5675-0_17 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/978-1-4419-5675-0_5 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/978-3-540-88853-6_8 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1007/978-3-642-68915-4_34 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.biosystems.2019.104022 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.clinph.2004.04.029 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2005.12.005 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2007.05.031 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2007.10.013 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2010.06.020 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2011.08.014 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.jneumeth.2017.09.008 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuroimage.2010.01.073 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuroimage.2011.01.055 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuroimage.2011.11.084 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuroimage.2012.05.050 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuroimage.2019.116009 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/j.neuron.2018.05.015 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/0079-6107(89)90004-7 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/0165-0270(88)90056-8 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/0165-1684(91)90079-X + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/S0165-0270(01)00372-7 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/S0165-0270(98)00061-2 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1016/S0165-0270(99)00126-0 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1038/nn.2842 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1038/nn.3101 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1038/nn1228 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1073/pnas.0308538101 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1080/01621459.1982.10477803 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1088/1741-2552/aacfe4 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1093/biomet/37.1-2.1 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1093/biomet/66.3.605 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1098/rsta.2011.0610 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1103/PhysRevE.92.040901 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1103/PhysRevLett.100.234101 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1103/PhysRevLett.85.461 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1109/CAMSAP.2013.6714049 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1109/PROC.1982.12433 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1109/PROC.1987.13723 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1109/TAU.1967.1161901 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1109/78.492555 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1111/1467-9868.00196 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1126/science.1055465 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1126/science.1128115 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1126/science.278.5345.1950 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1146/annurev.fl.24.010192.002143 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.00093.2008 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.00106.2010 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.00848.2014 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.00873.2012 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.1975.38.2.369 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.1996.76.2.1310 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1152/jn.90941.2008 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/NECO_a_00236 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/089976601300014321 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/089976602753284455 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/089976602753284464 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/089976603321043702 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1162/089976603322518759 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1214/14-AOAS782 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1371/journal.pcbi.1000433 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1371/journal.pcbi.1004939 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1523/ENEURO.0505-21.2022 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1523/JNEUROSCI.17-02-00722.1997 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.1523/JNEUROSCI.2767-14.2014 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.2307/27715168 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.3389/fncom.2010.00127 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.3389/fncom.2017.00041 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.48550/arXiv.q-bio/0608035 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.7554/eLife.10989 + rdf:type owl:NamedIndividual . + + +### https://doi.org/10.7554/eLife.19428 + rdf:type owl:NamedIndividual . + + +### https://isbndb.com/book/9780131139565 + rdf:type owl:NamedIndividual . + + +### https://isbndb.com/book/9781118585771 + rdf:type owl:NamedIndividual . + + +### https://isbndb.com/book/9789971502553#Aertsen_1 + rdf:type owl:NamedIndividual . + + +### https://proceedings.mlr.press/v54/cowley17a.html + rdf:type owl:NamedIndividual . + + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi diff --git a/src/catalog-v001.xml b/src/catalog-v001.xml new file mode 100644 index 0000000..541bcbf --- /dev/null +++ b/src/catalog-v001.xml @@ -0,0 +1,9 @@ + + + + + + + + + diff --git a/src/data/catalog-v001.xml b/src/data/catalog-v001.xml new file mode 100644 index 0000000..2c647c7 --- /dev/null +++ b/src/data/catalog-v001.xml @@ -0,0 +1,6 @@ + + + + + + diff --git a/src/data/data.owl b/src/data/data.owl new file mode 100644 index 0000000..134eeff --- /dev/null +++ b/src/data/data.owl @@ -0,0 +1,1368 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@prefix neao_base: . +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + owl:imports neao_base: ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + vann:preferredNamespacePrefix "neao_data" ; + vann:preferredNamespaceUri "http://purl.org/neao/data#" ; + rdfs:comment """This module in the Neuroelectrophysiology Analysis Ontology contains classes that represent data entities in the analysis of neuroelectrophysiology data. + +The classes in this module are subclasses of the Data base class, and are associated with the distinct steps throughout the analysis (defined in the steps module) by the hasInput and hasOutput object properties. + +Additional information on the data entity can be described. The electrophysiology recording source associated with the data can be defined with the hasSource object property that points to individuals of the ElectrophysiologySignalSource class. In addition, information on the structure of the data (e.g., matrix, array) can be provided with the isRepresentedAs object property that points to individuals of the DataRepresentation class. The objective of NEAO is not to model the sources and formats of the data, but these classes can be used as abstractions to align other ontologies that are defined with that purpose. These properties and classes are defined in the base module. + +Finally, several data properties are also defined to provide extended information on the data entity (e.g., isNormalized or isArtificial). These properties can be used to define additional semantic groupings to make inferences on the data and analysis (e.g., artificial data is defined by the ArtificialData class)."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology - Data"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/neao/base#abbreviation +neao_base:abbreviation rdf:type owl:AnnotationProperty . + + +### http://purl.org/neao/base#hasBibliographicReference +neao_base:hasBibliographicReference rdf:type owl:AnnotationProperty . + + +### http://www.w3.org/2004/02/skos/core#altLabel +skos:altLabel rdf:type owl:AnnotationProperty . + + +################################################################# +# Data properties +################################################################# + +### http://purl.org/neao/data#isArtificial +:isArtificial rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the data is artificially-generated (true) or obtained from experimental recordings (false)."@en ; + skos:prefLabel "is artificial"@en . + + +### http://purl.org/neao/data#isAveraged +:isAveraged rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the data is the result of an aggregation to obtain an average estimate from other data (true)."@en ; + skos:prefLabel "is averaged"@en . + + +### http://purl.org/neao/data#isDetrended +:isDetrended rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a detrending transformation was applied to the data (true)."@en ; + skos:prefLabel "is detrended"@en . + + +### http://purl.org/neao/data#isDimensionalityReduction +:isDimensionalityReduction rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the data is a result of a dimensionality reduction transformation (true)."@en ; + skos:prefLabel "is dimensionality reduction"@en . + + +### http://purl.org/neao/data#isDownsampled +:isDownsampled rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :isResampled ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a downsampling transformation was applied to the data (true)."@en ; + skos:prefLabel "is downsampled"@en . + + +### http://purl.org/neao/data#isFiltered +:isFiltered rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a digital filter was applied to the data (true)."@en ; + skos:prefLabel "is filtered"@en . + + +### http://purl.org/neao/data#isInterpolated +:isInterpolated rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a interpolation data transformation was applied to the data (true)."@en ; + skos:prefLabel "is interpolated"@en . + + +### http://purl.org/neao/data#isNormalized +:isNormalized rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a data normalization transformation was applied to the data (true)."@en ; + skos:prefLabel "is normalized"@en . + + +### http://purl.org/neao/data#isRectified +:isRectified rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a rectification transformation was applied to the data (true)."@en ; + skos:prefLabel "is rectified"@en . + + +### http://purl.org/neao/data#isRereferenced +:isRereferenced rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a transformation to change the reference in the recorded data was applied (true)."@en ; + skos:prefLabel "is rereferenced"@en . + + +### http://purl.org/neao/data#isResampled +:isResampled rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a resampling transformation was applied to the data (true)."@en ; + skos:prefLabel "is resampled"@en . + + +### http://purl.org/neao/data#isSmoothed +:isSmoothed rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if a data smoothing transformation was applied to the data (true)."@en ; + skos:prefLabel "is smoothed"@en . + + +### http://purl.org/neao/data#isUpsampled +:isUpsampled rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf :isResampled ; + rdfs:domain neao_base:Data ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if an upsampling transformation was applied to the data (true)."@en ; + skos:prefLabel "is upsampled"@en . + + +################################################################# +# Classes +################################################################# + +### http://purl.org/neao/base#Data +neao_base:Data rdf:type owl:Class . + + +### http://purl.org/neao/data#ASSETAnalysisMatrix +:ASSETAnalysisMatrix rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a matrix computed by one of the ASSET analysis substeps, as part of the execution of the ASSET analysis for the detection of neuronal activity patterns."@en ; + skos:prefLabel "ASSET analysis matrix"@en . + + +### http://purl.org/neao/data#ASSETClusterMatrix +:ASSETClusterMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisMatrix ; + neao_base:abbreviation "CMAT"@en ; + rdfs:comment "The cluster matrix (CMAT) computed in the ASSET analysis."@en ; + skos:prefLabel "ASSET cluster matrix"@en . + + +### http://purl.org/neao/data#ASSETIntersectionMatrix +:ASSETIntersectionMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisMatrix ; + neao_base:abbreviation "IMAT"@en ; + rdfs:comment "The intersection matrix (IMAT) computed in the ASSET analysis."@en ; + skos:prefLabel "ASSET intersection matrix"@en . + + +### http://purl.org/neao/data#ASSETJointProbabilityMatrix +:ASSETJointProbabilityMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisMatrix ; + neao_base:abbreviation "JMAT"@en ; + rdfs:comment "The joint probability matrix (JMAT) computed in the ASSET analysis."@en ; + skos:prefLabel "ASSET joint probability matrix"@en . + + +### http://purl.org/neao/data#ASSETMaskMatrix +:ASSETMaskMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisMatrix ; + neao_base:abbreviation "MMAT"@en ; + rdfs:comment "The mask matrix (MMAT) computed in the ASSET analysis."@en ; + skos:prefLabel "ASSET mask matrix"@en . + + +### http://purl.org/neao/data#ASSETPattern +:ASSETPattern rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPattern ; + rdfs:comment "A neuronal activity pattern obtained by the ASSET analysis. The ASSET patterns are the output of the last substep of the ASSET analysis that identifies the neurons present in each repeated sequence, based on the diagonal structures in the cluster matrix."@en ; + skos:prefLabel "ASSET pattern"@en . + + +### http://purl.org/neao/data#ASSETProbabilityMatrix +:ASSETProbabilityMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisMatrix ; + neao_base:abbreviation "PMAT"@en ; + rdfs:comment "The probability matrix (PMAT) computed in the ASSET analysis."@en ; + skos:prefLabel "ASSET probability matrix"@en . + + +### http://purl.org/neao/data#AnalyticSignal +:AnalyticSignal rdf:type owl:Class ; + rdfs:subClassOf :TimeSeries ; + rdfs:comment "The analytic signal obtained from a time series using the Hilbert transform."@en ; + skos:prefLabel "analytic signal"@en . + + +### http://purl.org/neao/data#AngularMeanSpikePhases +:AngularMeanSpikePhases rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed angular mean of the spike phases."@en ; + skos:prefLabel "angular mean of spike phases"@en . + + +### http://purl.org/neao/data#ArtificialData +:ArtificialData rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isArtificial ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that is generated programmatically rather than obtained from experimental recordings."@en ; + skos:prefLabel "artificial data"@en . + + +### http://purl.org/neao/data#AutocorrelationFunction +:AutocorrelationFunction rdf:type owl:Class ; + rdfs:subClassOf :AutocorrelationMeasure ; + neao_base:abbreviation "ACF"@en ; + rdfs:comment "The computed estimator of the autocorrelation function."@en ; + skos:prefLabel "autocorrelation function"@en . + + +### http://purl.org/neao/data#AutocorrelationMeasure +:AutocorrelationMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of autocorrelation."@en ; + skos:prefLabel "autocorrelation measure"@en . + + +### http://purl.org/neao/data#BinnedSpikeTrain +:BinnedSpikeTrain rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The output of applying binning to the input spike train data."@en ; + skos:prefLabel "binned spike train"@en . + + +### http://purl.org/neao/data#CV +:CV rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "CV"@en ; + rdfs:comment "The computed coefficient of variation (CV)."@en ; + skos:prefLabel "coefficient of variation"@en . + + +### http://purl.org/neao/data#CV2 +:CV2 rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityMeasure ; + rdfs:comment "The computed CV2 measure."@en ; + skos:prefLabel "CV2"@en . + + +### http://purl.org/neao/data#CVInterspikeIntervals +:CVInterspikeIntervals rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityMeasure ; + neao_base:abbreviation "CV ISIs"@en ; + rdfs:comment "The computed coefficient of variation (CV) of the interspike intervals (ISIs)."@en ; + skos:prefLabel "coefficient of variation of the interspike intervals"@en . + + +### http://purl.org/neao/data#CanonicalCoherence +:CanonicalCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceMeasure ; + neao_base:abbreviation "caCOH"@en ; + rdfs:comment "The computed canonical coherence (caCOH)."@en ; + skos:prefLabel "canonical coherence"@en . + + +### http://purl.org/neao/data#CellAssemblyDetectionPattern +:CellAssemblyDetectionPattern rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPattern ; + neao_base:abbreviation "CAD pattern"@en ; + rdfs:comment "A neuronal activity pattern obtained by the Cell Assembly Detection (CAD) analysis."@en ; + skos:prefLabel "Cell Assembly Detection pattern"@en . + + +### http://purl.org/neao/data#ChangePoint +:ChangePoint rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "A change point identified by the rate change detection multiple filter test."@en ; + skos:prefLabel "change point"@en . + + +### http://purl.org/neao/data#Coherence +:Coherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceMeasure ; + rdfs:comment "The computed magnitude squared coherence. This is the typical output of a computation estimating coherence, and it is frequently referred as just coherence."@en ; + skos:altLabel "magnitude squared coherence"@en ; + skos:prefLabel "coherence"@en . + + +### http://purl.org/neao/data#CoherenceMeasure +:CoherenceMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of coherence."@en ; + skos:prefLabel "coherence measure"@en . + + +### http://purl.org/neao/data#Coherency +:Coherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyMeasure ; + rdfs:comment "The computed coherency."@en ; + skos:altLabel "complex coherency"@en ; + skos:prefLabel "coherency"@en . + + +### http://purl.org/neao/data#CoherencyMeasure +:CoherencyMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of coherency."@en ; + skos:prefLabel "coherency measure"@en . + + +### http://purl.org/neao/data#ComplexityDistribution +:ComplexityDistribution rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyMeasure ; + rdfs:comment "The computed complexity distribution across the spike trains."@en ; + skos:prefLabel "complexity distribution"@en . + + +### http://purl.org/neao/data#ConditionalGrangerCausality +:ConditionalGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityMeasure ; + rdfs:comment "The computed conditional Granger causality."@en ; + skos:prefLabel "conditional Granger causality"@en . + + +### http://purl.org/neao/data#ConfidenceInterval +:ConfidenceInterval rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalMeasure ; + neao_base:abbreviation "CI"@en ; + rdfs:comment "The computed confidence interval (CI), with the lower and upper limits."@en ; + skos:prefLabel "confidence interval"@en . + + +### http://purl.org/neao/data#ConfidenceIntervalLowerLimit +:ConfidenceIntervalLowerLimit rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalMeasure ; + rdfs:comment "The lower limit value of the computed confidence interval."@en ; + skos:prefLabel "lower limit of confidence interval"@en . + + +### http://purl.org/neao/data#ConfidenceIntervalMeasure +:ConfidenceIntervalMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the description of a confidence interval (CI)."@en ; + skos:altLabel "CI measure"@en ; + skos:prefLabel "confidence interval measure"@en . + + +### http://purl.org/neao/data#ConfidenceIntervalUpperLimit +:ConfidenceIntervalUpperLimit rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalMeasure ; + rdfs:comment "The upper limit value of the computed confidence interval."@en ; + skos:prefLabel "upper limit of confidence interval"@en . + + +### http://purl.org/neao/data#CorrectedImaginaryPhaseLockingValue +:CorrectedImaginaryPhaseLockingValue rdf:type owl:Class ; + rdfs:subClassOf :PhaseLockingValueMeasure ; + neao_base:abbreviation "ciPLV"@en ; + rdfs:comment "The computed corrected imaginary phase locking value (ciPLV)."@en ; + skos:prefLabel "corrected imaginary phase locking value"@en . + + +### http://purl.org/neao/data#CorrelationMeasure +:CorrelationMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of correlation."@en ; + skos:prefLabel "correlation measure"@en . + + +### http://purl.org/neao/data#Covariance +:Covariance rdf:type owl:Class ; + rdfs:subClassOf :CovarianceMeasure ; + rdfs:comment "The computed covariance values between the inputs."@en ; + skos:prefLabel "covariance"@en . + + +### http://purl.org/neao/data#CovarianceMeasure +:CovarianceMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of covariance."@en ; + skos:prefLabel "covariance measure"@en . + + +### http://purl.org/neao/data#CrossCorrelationFunction +:CrossCorrelationFunction rdf:type owl:Class ; + rdfs:subClassOf :CrossCorrelationMeasure ; + neao_base:abbreviation "CCF"@en ; + rdfs:comment "The computed estimator of the cross-correlation function."@en ; + skos:prefLabel "cross-correlation function"@en . + + +### http://purl.org/neao/data#CrossCorrelationMeasure +:CrossCorrelationMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of cross-correlation."@en ; + skos:prefLabel "cross-correlation measure"@en . + + +### http://purl.org/neao/data#CrossPowerSpectralDensity +:CrossPowerSpectralDensity rdf:type owl:Class ; + rdfs:subClassOf :SpectralDensity ; + neao_base:abbreviation "CPSD"@en ; + rdfs:comment "The computed cross power spectral density (CPSD)."@en ; + skos:altLabel "cross-spectrum"@en ; + skos:prefLabel "cross power spectral density"@en . + + +### http://purl.org/neao/data#CrossSpectrogram +:CrossSpectrogram rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed cross-spectrogram."@en ; + skos:prefLabel "cross-spectrogram"@en . + + +### http://purl.org/neao/data#CubicAnalysisResult +:CubicAnalysisResult rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyMeasure ; + rdfs:comment "The result of the CuBIC analysis."@en ; + skos:prefLabel "CuBIC analysis result"@en . + + +### http://purl.org/neao/data#CurrentSourceDensity +:CurrentSourceDensity rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "CSD"@en ; + rdfs:comment "The profile of current densities estimated by a current source density analysis."@en ; + skos:prefLabel "current source density"@en . + + +### http://purl.org/neao/data#DebiasedSquaredWeightedPhaseLagIndex +:DebiasedSquaredWeightedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexMeasure ; + neao_base:abbreviation "debiased squared WPLI"@en ; + rdfs:comment "The computed debiased squared weighted phase lag index (WPLI)."@en ; + skos:prefLabel "debiased squared weighted phase lag index"@en . + + +### http://purl.org/neao/data#DimensionalityReductionOutput +:DimensionalityReductionOutput rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isDimensionalityReduction ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains an output of applying a dimensionality reduction transformation to the input data."@en ; + skos:prefLabel "dimensionality reduction output"@en . + + +### http://purl.org/neao/data#DirectedPhaseLagIndex +:DirectedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexMeasure ; + neao_base:abbreviation "DPLI"@en ; + rdfs:comment "The computed directed phase lag index (DPLI)."@en ; + skos:prefLabel "directed phase lag index"@en . + + +### http://purl.org/neao/data#DirectedTransferFunction +:DirectedTransferFunction rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "DTF"@en ; + rdfs:comment "The computed directed transfer function (DTF)."@en ; + skos:prefLabel "directed transfer function"@en . + + +### http://purl.org/neao/data#DirectionalGrangerCausality +:DirectionalGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityMeasure ; + rdfs:comment "The computed directional Granger causality from one input to the other."@en ; + skos:prefLabel "directional Granger causality"@en . + + +### http://purl.org/neao/data#DiscreteFourierTransform +:DiscreteFourierTransform rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "DFT"@en ; + rdfs:comment "The complex sinusoid coefficients obtained by applying the Discrete Fourier Transform (DFT) to the input data (e.g., using the FFT algorithm)."@en ; + skos:prefLabel "discrete Fourier transform"@en . + + +### http://purl.org/neao/data#DistanceCovarianceDimension +:DistanceCovarianceDimension rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReductionOutput ; + rdfs:comment "A dimension identified by applying distance covariance analysis (DCA) to the input data."@en ; + skos:prefLabel "distance covariance dimension"@en . + + +### http://purl.org/neao/data#Epoch +:Epoch rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that represents a finite time interval (window). For example, the interval between the start and end of a behavioral trial in the recording session."@en ; + skos:prefLabel "epoch"@en . + + +### http://purl.org/neao/data#Event +:Event rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + owl:disjointWith :SpikeTrain , + :SpikeTrainSurrogate , + :TimeSeries ; + rdfs:comment "Data that contains the time(s) when an event of interest occurred (e.g., a certain behavior or stimulus in an experimental trial)."@en ; + skos:prefLabel "event"@en . + + +### http://purl.org/neao/data#EventRelatedPotential +:EventRelatedPotential rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "ERP"@en ; + rdfs:comment "The computed event-related potential (ERP)."@en ; + skos:prefLabel "event-related potential"@en . + + +### http://purl.org/neao/data#EventTriggeredAverage +:EventTriggeredAverage rdf:type owl:Class ; + rdfs:subClassOf :TriggeredAverageMeasure ; + rdfs:comment "The computed event-triggered average."@en ; + skos:prefLabel "event-triggered average"@en . + + +### http://purl.org/neao/data#EvokedPotential +:EvokedPotential rdf:type owl:Class ; + rdfs:subClassOf :EventRelatedPotential ; + neao_base:abbreviation "EP"@en ; + rdfs:comment "The computed evoked potential (EP)."@en ; + skos:prefLabel "evoked potential"@en . + + +### http://purl.org/neao/data#FiringRate +:FiringRate rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed firing rate."@en ; + skos:prefLabel "firing rate"@en . + + +### http://purl.org/neao/data#FisherLinearDiscriminant +:FisherLinearDiscriminant rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReductionOutput ; + rdfs:comment "A Fisher linear discriminant obtained by applying linear discrimination analysis (LDA) to the input data."@en ; + skos:prefLabel "Fisher linear discriminant"@en . + + +### http://purl.org/neao/data#GrangerCausalityMeasure +:GrangerCausalityMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure of Granger causality."@en ; + skos:prefLabel "Granger causality measure"@en . + + +### http://purl.org/neao/data#GrangerTotalInterdependence +:GrangerTotalInterdependence rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityMeasure ; + rdfs:comment "The computed total interdependence in the Granger causality analysis."@en ; + skos:prefLabel "Granger total interdependence"@en . + + +### http://purl.org/neao/data#ISIDistance +:ISIDistance rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDistance ; + rdfs:comment "The computed ISI-distance."@en ; + skos:prefLabel "ISI-distance"@en . + + +### http://purl.org/neao/data#ImaginaryCoherency +:ImaginaryCoherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyMeasure ; + rdfs:comment "The computed imaginary part of the coherency."@en ; + skos:prefLabel "imaginary coherency"@en . + + +### http://purl.org/neao/data#IndependentComponent +:IndependentComponent rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains an independent component produced by applying independent component analysis (ICA) to the input data."@en ; + skos:prefLabel "independent component"@en . + + +### http://purl.org/neao/data#InstantaneousFiringRate +:InstantaneousFiringRate rdf:type owl:Class ; + rdfs:subClassOf :FiringRate ; + rdfs:comment "The computed instantaneous firing rate."@en ; + skos:prefLabel "instantaneous firing rate"@en . + + +### http://purl.org/neao/data#InstantaneousGrangerCausality +:InstantaneousGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityMeasure ; + rdfs:comment "The computed instantaneous Granger causality."@en ; + skos:prefLabel "instantaneous Granger causality"@en . + + +### http://purl.org/neao/data#InterquartileRange +:InterquartileRange rdf:type owl:Class ; + rdfs:subClassOf :InterquartileRangeMeasure ; + neao_base:abbreviation "IQR"@en ; + rdfs:comment "The computed interquartile range, with the difference between the 75th and 25th percentile values."@en ; + skos:prefLabel "interquartile range"@en . + + +### http://purl.org/neao/data#InterquartileRangeLowerLimit +:InterquartileRangeLowerLimit rdf:type owl:Class ; + rdfs:subClassOf :InterquartileRangeMeasure ; + rdfs:comment "The lower limit of the computed interquartile range, corresponding to the 25th percentile."@en ; + skos:prefLabel "lower limit of interquartile range"@en . + + +### http://purl.org/neao/data#InterquartileRangeMeasure +:InterquartileRangeMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "IQR measure"@en ; + rdfs:comment "Data that contains the description of an interquartile range (IQR)."@en ; + skos:prefLabel "interquartile range measure"@en . + + +### http://purl.org/neao/data#InterquartileRangeUpperLimit +:InterquartileRangeUpperLimit rdf:type owl:Class ; + rdfs:subClassOf :InterquartileRangeMeasure ; + rdfs:comment "The upper limit of the computed interquartile range, corresponding to the 75th percentile."@en ; + skos:prefLabel "upper limit of interquartile range"@en . + + +### http://purl.org/neao/data#InterspikeIntervalHistogram +:InterspikeIntervalHistogram rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "ISIH"@en ; + rdfs:comment "The computed interspike interval histogram (ISIH)."@en ; + skos:prefLabel "interspike interval histogram"@en . + + +### http://purl.org/neao/data#InterspikeIntervalVariabilityMeasure +:InterspikeIntervalVariabilityMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure describing the variability of the interspike intervals."@en ; + skos:prefLabel "interspike interval variability measure"@en . + + +### http://purl.org/neao/data#InterspikeIntervals +:InterspikeIntervals rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "ISIs"@en ; + rdfs:comment "The computed interspike intervals (ISIs)."@en ; + skos:prefLabel "interspike intervals"@en . + + +### http://purl.org/neao/data#JointPeristimulusTimeHistogramMatrix +:JointPeristimulusTimeHistogramMatrix rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "JPSTH matrix"@en ; + rdfs:comment "The computed joint peristimulus time histogram (JPSTH) matrix."@en ; + skos:prefLabel "joint peristimulus time histogram matrix"@en . + + +### http://purl.org/neao/data#LV +:LV rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityMeasure ; + rdfs:comment "The computed local variation (LV) measure."@en ; + skos:prefLabel "LV"@en . + + +### http://purl.org/neao/data#LVR +:LVR rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityMeasure ; + rdfs:comment "The computed revised local variation (LvR) measure."@en ; + skos:prefLabel "LvR"@en . + + +### http://purl.org/neao/data#MagnitudeCoherence +:MagnitudeCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceMeasure ; + rdfs:comment "The computed magnitude coherence."@en ; + skos:prefLabel "magnitude coherence"@en . + + +### http://purl.org/neao/data#MaximizedImaginaryCoherency +:MaximizedImaginaryCoherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyMeasure ; + neao_base:abbreviation "MIC"@en ; + rdfs:comment "The computed maximized imaginary coherency (MIC)."@en ; + skos:prefLabel "maximized imaginary coherency"@en . + + +### http://purl.org/neao/data#Mean +:Mean rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed mean."@en ; + skos:prefLabel "mean"@en . + + +### http://purl.org/neao/data#MeanFiringRate +:MeanFiringRate rdf:type owl:Class ; + rdfs:subClassOf :FiringRate ; + neao_base:abbreviation "MFR"@en ; + rdfs:comment "The computed mean firing rate."@en ; + skos:prefLabel "mean firing rate"@en . + + +### http://purl.org/neao/data#MeanPhaseVector +:MeanPhaseVector rdf:type owl:Class ; + rdfs:subClassOf :MeanPhaseVectorMeasure ; + rdfs:comment "The computed mean phase vector (i.e., the angle and length)."@en ; + skos:prefLabel "mean phase vector"@en . + + +### http://purl.org/neao/data#MeanPhaseVectorAngle +:MeanPhaseVectorAngle rdf:type owl:Class ; + rdfs:subClassOf :MeanPhaseVectorMeasure ; + rdfs:comment "The angle of the computed mean phase vector."@en ; + skos:prefLabel "mean phase vector angle"@en . + + +### http://purl.org/neao/data#MeanPhaseVectorLength +:MeanPhaseVectorLength rdf:type owl:Class ; + rdfs:subClassOf :MeanPhaseVectorMeasure ; + rdfs:comment "The length of the computed mean phase vector."@en ; + skos:prefLabel "mean phase vector length"@en . + + +### http://purl.org/neao/data#MeanPhaseVectorMeasure +:MeanPhaseVectorMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the description or elements of the mean phase vector."@en ; + skos:prefLabel "mean phase vector measure"@en . + + +### http://purl.org/neao/data#MeanVectorLength +:MeanVectorLength rdf:type owl:Class ; + rdfs:subClassOf :PhaseAmplitudeCouplingMeasure ; + neao_base:abbreviation "MVL"@en ; + rdfs:comment "The computed mean vector length (MVL)."@en ; + skos:prefLabel "mean vector length"@en . + + +### http://purl.org/neao/data#Median +:Median rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed median."@en ; + skos:prefLabel "median"@en . + + +### http://purl.org/neao/data#ModulationIndex +:ModulationIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseAmplitudeCouplingMeasure ; + neao_base:abbreviation "MI"@en ; + rdfs:comment "The computed modulation index (MI)."@en ; + skos:prefLabel "modulation index"@en . + + +### http://purl.org/neao/data#MorletWavelet +:MorletWavelet rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the generated Morlet wavelet values for a sequence of time points."@en ; + skos:prefLabel "Morlet wavelet"@en . + + +### http://purl.org/neao/data#MultivariateInteractionMeasure +:MultivariateInteractionMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "MIM"@en ; + rdfs:comment "The computed multivariate interaction measure (MIM)."@en ; + skos:prefLabel "multivariate interaction measure"@en . + + +### http://purl.org/neao/data#MutualInformation +:MutualInformation rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "MI"@en ; + rdfs:comment "The computed mutual information (MI)."@en ; + skos:prefLabel "mutual information"@en . + + +### http://purl.org/neao/data#NeuralTrajectory +:NeuralTrajectory rdf:type owl:Class ; + rdfs:subClassOf :TimeSeries , + [ rdf:type owl:Restriction ; + owl:onProperty :isDimensionalityReduction ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A neural trajectory obtained by applying Gaussian process factor analysis (GPFA) to the input data containing multitrial spiking activity of a neuronal population."@en ; + skos:prefLabel "neural trajectory"@en . + + +### http://purl.org/neao/data#NeuronalActivityPattern +:NeuronalActivityPattern rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the description of a neuronal activity pattern, which is obtained from a neuronal activity pattern detection analysis."@en ; + skos:altLabel "cell assembly"@en , + "neuronal assembly"@en ; + skos:prefLabel "neuronal activity pattern"@en . + + +### http://purl.org/neao/data#NeuronalPopulationVector +:NeuronalPopulationVector rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed neuronal population vector."@en ; + skos:altLabel "population vector"@en ; + skos:prefLabel "neuronal population vector"@en . + + +### http://purl.org/neao/data#NoiseCorrelations +:NoiseCorrelations rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationMeasure ; + neao_base:abbreviation "NC"@en ; + rdfs:comment "The computed noise correlations (NC)."@en ; + skos:prefLabel "noise correlations"@en . + + +### http://purl.org/neao/data#NoiseCovariance +:NoiseCovariance rdf:type owl:Class ; + rdfs:subClassOf :CovarianceMeasure ; + rdfs:comment "The computed noise covariance."@en ; + skos:prefLabel "noise covariance"@en . + + +### http://purl.org/neao/data#OrthogonalizedPowerEnvelopeCorrelation +:OrthogonalizedPowerEnvelopeCorrelation rdf:type owl:Class ; + rdfs:subClassOf :CorrelationMeasure ; + rdfs:comment "The computed orthogonalized power envelope correlation."@en ; + skos:prefLabel "orthogonalized power envelope correlation"@en . + + +### http://purl.org/neao/data#PairwisePhaseConsistency +:PairwisePhaseConsistency rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PPC"@en ; + rdfs:comment "The computed pairwise phase consistency (PPC) measure."@en ; + skos:prefLabel "pairwise phase consistency"@en . + + +### http://purl.org/neao/data#PartialCoherence +:PartialCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceMeasure ; + rdfs:comment "The computed partial coherence."@en ; + skos:prefLabel "partial coherence"@en . + + +### http://purl.org/neao/data#PartialDirectedCoherence +:PartialDirectedCoherence rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PDC"@en ; + rdfs:comment "The computed partial directed coherence (PDC)."@en ; + skos:prefLabel "partial directed coherence"@en . + + +### http://purl.org/neao/data#PeristimulusCoincidenceHistogram +:PeristimulusCoincidenceHistogram rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed peristimulus coincidence histogram."@en ; + skos:prefLabel "peristimulus coincidence histogram"@en . + + +### http://purl.org/neao/data#PeristimulusTimeHistogram +:PeristimulusTimeHistogram rdf:type owl:Class ; + rdfs:subClassOf :TimeHistogram ; + neao_base:abbreviation "PSTH"@en ; + rdfs:comment "The computed peristimulus time histogram (PSTH). The stimulus or event of interest can occur at any time point within the interval analyzed. However, if the histogram represents the neuronal activity after the stimulus presentation or event occurrence, this can be referred as post-stimulus time histogram. Conversely, if the histogram shows the activity of the neuron before the stimulus presentation or event, this can be referred as prestimulus time histogram. Finally, if the histogram considers a behavioral event (or an event that is not an externally presented stimulus), the histogram can be referred to as perievent time histogram (PETH)."@en ; + skos:altLabel "perievent time histogram"@en , + "post-stimulus time histogram"@en , + "prestimulus time histogram"@en ; + skos:prefLabel "peristimulus time histogram"@en . + + +### http://purl.org/neao/data#PhaseAmplitudeCouplingMeasure +:PhaseAmplitudeCouplingMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PAC measure"@en ; + rdfs:comment "Data that contains a measure of phase-amplitude coupling (PAC)."@en ; + skos:prefLabel "phase amplitude coupling measure"@en . + + +### http://purl.org/neao/data#PhaseAngles +:PhaseAngles rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "A measure of phase defining the angle on the unit circle that corresponds to the current position within the waveform's cycle. The range of angles in a full cycle is from 0 to 2*pi radians or 0 to 360 degrees."@en ; + skos:prefLabel "phase angles"@en . + + +### http://purl.org/neao/data#PhaseLagIndex +:PhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexMeasure ; + neao_base:abbreviation "PLI"@en ; + rdfs:comment "The computed phase lag index (PLI)."@en ; + skos:prefLabel "phase lag index"@en . + + +### http://purl.org/neao/data#PhaseLagIndexMeasure +:PhaseLagIndexMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PLI measure"@en ; + rdfs:comment "Data that contains a measure computed by a phase-lag index analysis."@en ; + skos:prefLabel "phase lag index measure"@en . + + +### http://purl.org/neao/data#PhaseLockingValue +:PhaseLockingValue rdf:type owl:Class ; + rdfs:subClassOf :PhaseLockingValueMeasure ; + neao_base:abbreviation "PLV"@en ; + rdfs:comment "The computed phase locking value (PLV)."@en ; + skos:prefLabel "phase locking value"@en . + + +### http://purl.org/neao/data#PhaseLockingValueMeasure +:PhaseLockingValueMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PLV measure"@en ; + rdfs:comment "Data that contains a phase locking value (PLV) measure."@en ; + skos:prefLabel "phase locking value measure"@en . + + +### http://purl.org/neao/data#PhaseSlopeIndex +:PhaseSlopeIndex rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "PSI"@en ; + rdfs:comment "The computed phase slope index (PSI)."@en ; + skos:prefLabel "phase slope index"@en . + + +### http://purl.org/neao/data#Plot +:Plot rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "A graphical representation of data values. It is used to visually communicate quantitative information, providing intuitive insights that can complement numerical analysis. It is often produced by later steps in the analysis after the computation of measures, to provide the visual representation of the results."@en ; + skos:prefLabel "plot"@en . + + +### http://purl.org/neao/data#PopulationHistogram +:PopulationHistogram rdf:type owl:Class ; + rdfs:subClassOf :TimeHistogram ; + rdfs:comment "The computed population histogram."@en ; + skos:prefLabel "population histogram"@en . + + +### http://purl.org/neao/data#PowerSpectralDensity +:PowerSpectralDensity rdf:type owl:Class ; + rdfs:subClassOf :SpectralDensity ; + neao_base:abbreviation "PSD"@en ; + rdfs:comment "The computed power spectral density (PSD)."@en ; + skos:altLabel "spectrum"@en ; + skos:prefLabel "power spectral density"@en . + + +### http://purl.org/neao/data#PrincipalComponent +:PrincipalComponent rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReductionOutput ; + rdfs:comment "A principal component obtained by applying principal component analysis (PCA) to the input data."@en ; + skos:prefLabel "principal component"@en . + + +### http://purl.org/neao/data#RectifiedAreaUnderCurve +:RectifiedAreaUnderCurve rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "RAUC"@en ; + rdfs:comment "The computed rectified area under the curve (RAUC)."@en ; + skos:prefLabel "rectified area under the curve"@en . + + +### http://purl.org/neao/data#RegularizedCovariance +:RegularizedCovariance rdf:type owl:Class ; + rdfs:subClassOf :Covariance ; + rdfs:comment "The covariance values computed with regularization techniques for numerical stability."@en ; + skos:prefLabel "regularized covariance"@en . + + +### http://purl.org/neao/data#SPADEPattern +:SPADEPattern rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPattern ; + rdfs:comment "A neuronal activity pattern obtained by the Spatio-temporal PAttern Detection and Evaluation (SPADE) analysis."@en ; + skos:prefLabel "SPADE pattern"@en . + + +### http://purl.org/neao/data#SPIKEDistance +:SPIKEDistance rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDistance ; + rdfs:comment "The computed SPIKE distance."@en ; + skos:prefLabel "SPIKE distance"@en . + + +### http://purl.org/neao/data#SPIKESynchronization +:SPIKESynchronization rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDistance ; + rdfs:comment "The computed SPIKE synchronization distance."@en ; + skos:prefLabel "SPIKE synchronization"@en . + + +### http://purl.org/neao/data#ShortTimeFourierTransform +:ShortTimeFourierTransform rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "STFT"@en ; + rdfs:comment "The complex sinusoid coefficients obtained by applying the short-time Fourier transform (STFT) to the input data."@en ; + skos:prefLabel "short-time Fourier transform"@en . + + +### http://purl.org/neao/data#SpectralDensity +:SpectralDensity rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the density of a measure of the input(s) over the frequency spectrum."@en ; + skos:prefLabel "spectral density"@en . + + +### http://purl.org/neao/data#Spectrogram +:Spectrogram rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed spectrogram."@en ; + skos:prefLabel "spectrogram"@en . + + +### http://purl.org/neao/data#SpikeContrast +:SpikeContrast rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyMeasure ; + rdfs:comment "The computed Spike-contrast."@en ; + skos:prefLabel "Spike-contrast"@en . + + +### http://purl.org/neao/data#SpikeFieldCoherence +:SpikeFieldCoherence rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "SFC"@en ; + rdfs:comment "The computed spike-field coherence (SFC)."@en ; + skos:prefLabel "spike-field coherence"@en . + + +### http://purl.org/neao/data#SpikeTimeTilingCoefficient +:SpikeTimeTilingCoefficient rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationMeasure ; + neao_base:abbreviation "STTC"@en ; + rdfs:comment "The computed spike time tiling coefficient (STTC)."@en ; + skos:prefLabel "spike time tiling coefficient"@en . + + +### http://purl.org/neao/data#SpikeTrain +:SpikeTrain rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + owl:disjointWith :SpikeTrainSurrogate , + :TimeSeries ; + rdfs:comment "Data that contains the sequence of time points when an action potential (spike) occurs. A spike train can be obtained by electrophysiological recordings from the neural tissue (i.e., the data contains the times when spikes occur naturally in response to various stimuli or during spontaneous activity) or artificially-generated using specific statistical procedures or simulations of neuronal activity."@en ; + skos:prefLabel "spike train"@en . + + +### http://purl.org/neao/data#SpikeTrainAutocorrelationHistogram +:SpikeTrainAutocorrelationHistogram rdf:type owl:Class ; + rdfs:subClassOf :AutocorrelationMeasure ; + rdfs:comment "The computed spike train autocorrelation histogram."@en ; + skos:prefLabel "spike train autocorrelation histogram"@en . + + +### http://purl.org/neao/data#SpikeTrainAutocorrelationTimeScale +:SpikeTrainAutocorrelationTimeScale rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed spike train autocorrelation time scale."@en ; + skos:prefLabel "spike train autocorrelation time scale"@en . + + +### http://purl.org/neao/data#SpikeTrainCorrelationMeasure +:SpikeTrainCorrelationMeasure rdf:type owl:Class ; + rdfs:subClassOf :CorrelationMeasure ; + rdfs:comment "Data that contains a measure estimating the correlation between spike trains."@en ; + skos:prefLabel "correlation measure"@en . + + +### http://purl.org/neao/data#SpikeTrainCrossCorrelationHistogram +:SpikeTrainCrossCorrelationHistogram rdf:type owl:Class ; + rdfs:subClassOf :CrossCorrelationMeasure ; + neao_base:abbreviation "CCH"@en ; + rdfs:comment "The computed spike train cross-correlation histogram."@en ; + skos:altLabel "correlogram"@en , + "cross-correlogram"@en ; + skos:prefLabel "spike train cross-correlation histogram"@en . + + +### http://purl.org/neao/data#SpikeTrainDistance +:SpikeTrainDistance rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a spike train distance measure."@en ; + skos:prefLabel "spike train distance"@en . + + +### http://purl.org/neao/data#SpikeTrainFanoFactor +:SpikeTrainFanoFactor rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "FF"@en ; + rdfs:comment "The computed Fano factor (FF) for a set of input spike trains."@en ; + skos:prefLabel "spike train Fano factor"@en . + + +### http://purl.org/neao/data#SpikeTrainPearsonCorrelationCoefficient +:SpikeTrainPearsonCorrelationCoefficient rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationMeasure ; + rdfs:comment "The computed Pearson correlation coefficient between two spike trains."@en ; + skos:prefLabel "spike train Pearson correlation coefficient"@en . + + +### http://purl.org/neao/data#SpikeTrainSurrogate +:SpikeTrainSurrogate rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + owl:disjointWith :TimeSeries ; + rdfs:comment "Data that contains a spike train surrogate. It is generated from spike train inputs using distinct methods to alter the original spike times."@en ; + skos:prefLabel "spike train surrogate"@en . + + +### http://purl.org/neao/data#SpikeTrainSynchronyMeasure +:SpikeTrainSynchronyMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a measure describing synchronization in two or more spike trains containing the activity of different neurons."@en ; + skos:prefLabel "spike train synchrony measure"@en . + + +### http://purl.org/neao/data#SpikeTrainTimeHistogram +:SpikeTrainTimeHistogram rdf:type owl:Class ; + rdfs:subClassOf :TimeHistogram ; + rdfs:comment "The computed spike train time histogram."@en ; + skos:prefLabel "spike train time histogram"@en . + + +### http://purl.org/neao/data#SpikeTriggeredAverage +:SpikeTriggeredAverage rdf:type owl:Class ; + rdfs:subClassOf :TriggeredAverageMeasure ; + neao_base:abbreviation "STA"@en ; + rdfs:comment "The computed spike-triggered average (STA)."@en ; + skos:prefLabel "spike-triggered average"@en . + + +### http://purl.org/neao/data#SpikeTriggeredLFPAverage +:SpikeTriggeredLFPAverage rdf:type owl:Class ; + rdfs:subClassOf :SpikeTriggeredAverage ; + rdfs:comment "The computed spike-triggered average of the local field potential (LFP) signal."@en ; + skos:prefLabel "spike-triggered local field potential average"@en . + + +### http://purl.org/neao/data#SpikeTriggeredPhase +:SpikeTriggeredPhase rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed spike-triggered phase angles."@en ; + skos:prefLabel "spike-triggered phase"@en . + + +### http://purl.org/neao/data#SpikeWaveform +:SpikeWaveform rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the characteristic voltage change over time observed in the electrical recording of a neuron's action potential (spike). This waveform represents the rapid depolarization and repolarization of the neuron's membrane potential during the action potential. The data is acquired in a finite number of samples that correspond to a time window around the spike time (i.e., with periods before and after the action potential onset)."@en ; + skos:prefLabel "spike waveform"@en . + + +### http://purl.org/neao/data#SpikeWaveformAverage +:SpikeWaveformAverage rdf:type owl:Class ; + rdfs:subClassOf :SpikeWavevormStatistic ; + rdfs:comment "The computed average across the spike waveforms."@en ; + skos:prefLabel "spike waveform average"@en . + + +### http://purl.org/neao/data#SpikeWaveformSNR +:SpikeWaveformSNR rdf:type owl:Class ; + rdfs:subClassOf :SpikeWavevormStatistic ; + neao_base:abbreviation "spike waveform SNR"@en ; + rdfs:comment "The computed signal-to-noise ratio of the spike waveforms."@en ; + skos:prefLabel "spike waveform signal-to-noise ratio"@en . + + +### http://purl.org/neao/data#SpikeWaveformVariance +:SpikeWaveformVariance rdf:type owl:Class ; + rdfs:subClassOf :SpikeWavevormStatistic ; + rdfs:comment "The computed variance across the spike waveforms."@en ; + skos:prefLabel "spike waveform variance"@en . + + +### http://purl.org/neao/data#SpikeWaveformWidth +:SpikeWaveformWidth rdf:type owl:Class ; + rdfs:subClassOf :SpikeWavevormStatistic ; + rdfs:comment "The computed spike waveform width."@en ; + skos:prefLabel "spike waveform width"@en . + + +### http://purl.org/neao/data#SpikeWavevormStatistic +:SpikeWavevormStatistic rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains measures used to describe specific characteristics or make inferences from spike waveforms."@en ; + skos:prefLabel "spike waveform statistic"@en . + + +### http://purl.org/neao/data#StandardDeviation +:StandardDeviation rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "SD"@en ; + rdfs:comment "The computed standard deviation (SD)."@en ; + skos:prefLabel "standard deviation"@en . + + +### http://purl.org/neao/data#StandardErrorMean +:StandardErrorMean rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "SEM"@en ; + rdfs:comment "The computed standard error of the mean (SEM)."@en ; + skos:prefLabel "standard error of the mean"@en . + + +### http://purl.org/neao/data#StockwellTransform +:StockwellTransform rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The result of computing the Stockwell transform (S transform)."@en ; + skos:altLabel "S transform"@en ; + skos:prefLabel "Stockwell transform"@en . + + +### http://purl.org/neao/data#TensorComponent +:TensorComponent rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReductionOutput ; + rdfs:comment "A tensor component obtained by applying tensor component analysis (TCA) to the input data."@en ; + skos:prefLabel "tensor component"@en . + + +### http://purl.org/neao/data#TimeHistogram +:TimeHistogram rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains a time histogram, i.e., a histogram that shows the distribution of a measure across specified time intervals (bins)."@en ; + skos:prefLabel "time histogram"@en . + + +### http://purl.org/neao/data#TimeSeries +:TimeSeries rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains values of a specific quantity (e.g., voltage) acquired as a series of successive samples over time. Typically, there is a fixed interval between the samples (sampling period), with each sample representing a distinct time point in the series."@en ; + skos:prefLabel "time series"@en . + + +### http://purl.org/neao/data#TransferEntropy +:TransferEntropy rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + neao_base:abbreviation "TE"@en ; + rdfs:comment "The computed transfer entropy (TE)."@en ; + skos:prefLabel "transfer entropy"@en . + + +### http://purl.org/neao/data#TriggeredAverageMeasure +:TriggeredAverageMeasure rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the output of a triggered average analysis."@en ; + skos:prefLabel "triggered average measure"@en . + + +### http://purl.org/neao/data#TuningCurve +:TuningCurve rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed tuning curve."@en ; + skos:prefLabel "tuning curve"@en . + + +### http://purl.org/neao/data#UnbiasedSquaredPhaseLagIndex +:UnbiasedSquaredPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexMeasure ; + neao_base:abbreviation "unbiased squared PLI"@en ; + rdfs:comment "The computed unbiased squared phase lag index (PLI)."@en ; + skos:prefLabel "unbiased squared phase lag index"@en . + + +### http://purl.org/neao/data#UnitaryEventPattern +:UnitaryEventPattern rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPattern ; + neao_base:abbreviation "UE pattern"@en ; + rdfs:comment "A neuronal activity pattern obtained by the Unitary Event (UE) analysis."@en ; + skos:prefLabel "Unitary Event pattern"@en . + + +### http://purl.org/neao/data#VanRossumDistance +:VanRossumDistance rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDistance ; + rdfs:comment "The computed van Rossum distance."@en ; + skos:prefLabel "van Rossum distance"@en . + + +### http://purl.org/neao/data#Variance +:Variance rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "The computed variance."@en ; + skos:prefLabel "variance"@en . + + +### http://purl.org/neao/data#VictorPurpuraDistance +:VictorPurpuraDistance rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDistance ; + rdfs:comment "The computed Victor-Purpura distance."@en ; + skos:prefLabel "Victor-Purpura distance"@en . + + +### http://purl.org/neao/data#WaveletTransform +:WaveletTransform rdf:type owl:Class ; + rdfs:subClassOf neao_base:Data ; + rdfs:comment "Data that contains the result of a wavelet transform analysis, i.e., decomposing a time series using a mother wavelet function."@en ; + skos:altLabel "scaleogram"@en ; + skos:prefLabel "wavelet transform"@en . + + +### http://purl.org/neao/data#WeightedPhaseLagIndex +:WeightedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexMeasure ; + neao_base:abbreviation "WPLI"@en ; + rdfs:comment "The computed weighted phase lag index (WPLI)."@en ; + skos:prefLabel "weighted phase lag index"@en . + + +################################################################# +# General axioms +################################################################# + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :DebiasedSquaredWeightedPhaseLagIndex + :DirectedPhaseLagIndex + :PhaseLagIndex + :UnbiasedSquaredPhaseLagIndex + :WeightedPhaseLagIndex + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :InterquartileRange + :InterquartileRangeLowerLimit + :InterquartileRangeUpperLimit + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :MeanPhaseVector + :MeanPhaseVectorAngle + :MeanPhaseVectorLength + ) +] . + + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi diff --git a/src/neao.owl b/src/neao.owl new file mode 100644 index 0000000..be0fb93 --- /dev/null +++ b/src/neao.owl @@ -0,0 +1,63 @@ +@prefix : . +@prefix dc: . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix biro: . +@prefix rdfs: . +@prefix vann: . +@prefix dcterms: . +@prefix neao_bib: . +@prefix neao_base: . +@prefix neao_data: . +@prefix neao_params: . +@prefix neao_steps: . + +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + owl:imports neao_steps: , + neao_params: ; + vann:preferredNamespacePrefix "neao" ; + vann:preferredNamespaceUri "http://purl.org/neao/" ; + rdfs:comment """The Neuroelectrophysiology Analysis Ontology (NEAO) defines a controlled vocabulary and conceptual representations of the typical processes involved in the analysis of neural activity data acquired using electrophysiology techniques. + +Electrophysiology is a branch of physiology that studies the electrical properties of biological entities. The studies involve measurements of electric potentials and/or currents, as well as electrical manipulations (e.g. stimuli with electric pulses). Neuroelectrophysiology is the application of electrophysiology techniques to investigate the function of neural tissue. + +Neuroelectrophysiology recording is the process of data acquisition, and it usually involves placing electrodes (of several configurations and types) into a preparation of neural tissue. The data will contain a representation of the voltages or currents in the preparation during the recording session, usually as a time series. The analysis requires specific methods to progressively process and transform the recorded data, which generate results and insights. + +The scope of the NEAO aims to provide a comprehensive representation of neuroelectrophysiology data and analysis processes, standardizing the description of their properties and relationships. The NEAO does not provide a detailed representation of electrophysiology recording techniques, data acquisition methods/equipment, and experimental settings/subjects. The goal is to ensure a common representation of data analysis that can be used by tools to provide a detailed and semantically-enriched description of the processes involved."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/dc/terms/created +dcterms:created rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/creator +dcterms:creator rdf:type owl:AnnotationProperty . + + +### http://purl.org/dc/terms/license +dcterms:license rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespacePrefix +vann:preferredNamespacePrefix rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespaceUri +vann:preferredNamespaceUri rdf:type owl:AnnotationProperty . + + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi diff --git a/src/parameters/catalog-v001.xml b/src/parameters/catalog-v001.xml new file mode 100644 index 0000000..6875bd0 --- /dev/null +++ b/src/parameters/catalog-v001.xml @@ -0,0 +1,4 @@ + + + + diff --git a/src/parameters/parameters.owl b/src/parameters/parameters.owl new file mode 100644 index 0000000..c23a73e --- /dev/null +++ b/src/parameters/parameters.owl @@ -0,0 +1,438 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@prefix neao_base: . +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + owl:imports neao_base: ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + vann:preferredNamespacePrefix "neao_params" ; + vann:preferredNamespaceUri "http://purl.org/neao/parameters#" ; + rdfs:comment """This module in the Neuroelectrophysiology Analysis Ontology contains classes that represent parameters in the analyses. A parameter is an information entity that controls the behavior of an analysis step, but does not provide data that is used by the step to produce the output. + +For example, in a low-pass filtering step in the analysis, where a raw wideband signal is transformed to isolate the frequency components smaller than a cutoff frequency, the time series with the raw signal is the data input, and the low-pass frequency cutoff frequency value is a parameter. + +The classes in this module are subclasses of the AnalysisParameter base class and are associated with the analysis steps (defined in the steps module) through the usesParameter object property."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology - Analysis Parameters"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/dc/terms/created +dcterms:created rdf:type owl:AnnotationProperty . + + +### http://purl.org/neao/base#abbreviation +neao_base:abbreviation rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespacePrefix +vann:preferredNamespacePrefix rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespaceUri +vann:preferredNamespaceUri rdf:type owl:AnnotationProperty . + + +### http://www.w3.org/2004/02/skos/core#altLabel +skos:altLabel rdf:type owl:AnnotationProperty . + + +################################################################# +# Classes +################################################################# + +### http://purl.org/neao/base#AnalysisParameter +neao_base:AnalysisParameter rdf:type owl:Class . + + +### http://purl.org/neao/parameters#ASSETAnalysisParameter +:ASSETAnalysisParameter rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "A parameter used by the Analysis of Sequences of Synchronous Events (ASSET) method, used to identify neuronal activity patterns in spike data."@en ; + skos:prefLabel "ASSET analysis parameter"@en . + + +### http://purl.org/neao/parameters#ASSETClusteringDistanceStretchFactor +:ASSETClusteringDistanceStretchFactor rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisParameter ; + rdfs:comment "When computing the elliptical distance measure used by the DBSCAN algorithm to cluster the mask matrix entries to find the diagonal structures, the parameter value is used to stretch angular coefficients deviating from 45 degrees. This reflects into the shape of the neighborhood around a point in the DBSCAN procedure. A large stretch factor will produce neighborhoods that are narrower and closer to a line in the 45 degree direction. Smaller stretch factors will produce neighborhoods that are more elliptical, with the major axis in the 45 degree direction, and the minor axis in the 135 degree direction. Therefore, the smaller stretch factor increases the minor axis of this ellipsis. Together with the DBSCAN neighborhood radius parameter, this should be used to tweak the neighborhood ellipsis such that it is contained in the rectangular kernel used to filter the probability matrix entries when computing the joint probability matrix."@en ; + skos:prefLabel "distance stretch factor in ASSET clustering"@en . + + +### http://purl.org/neao/parameters#ASSETJointProbabilityMatrixFilterShape +:ASSETJointProbabilityMatrixFilterShape rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisParameter ; + rdfs:comment "A tuple of integers that determines the width and length of a rectangular kernel used to cover a diagonal structure in the intersection matrix. The kernel is centered in a point in the probability matrix, and defines a neighborhood around that point. Inside the kernel, a chosen number of largest neighbors in the probability matrix is used to compute the joint probability value for the center point. The kernel is rotated in 45 degree angle."@en ; + skos:altLabel "kernel shape in ASSET joint probability matrix"@en ; + skos:prefLabel "filter shape in ASSET joint probability matrix"@en . + + +### http://purl.org/neao/parameters#ASSETJointProbabilityMatrixNumberLargestNeighbors +:ASSETJointProbabilityMatrixNumberLargestNeighbors rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisParameter ; + rdfs:comment "Defines the number of elements inside the filter kernel that are used to compute the joint probability value for a matrix entry. The probability matrix values inside the kernel for that entry are ordered from the largest to the lowest value, and the first N elements corresponding to the parameter value are taken."@en ; + skos:prefLabel "number of largest neighbors in ASSET joint probability matrix"@en . + + +### http://purl.org/neao/parameters#ASSETMaskMatrixSignificanceThresholds +:ASSETMaskMatrixSignificanceThresholds rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisParameter ; + rdfs:comment "Real values (range 0-1) used as thresholds to determine if the entries in the probability matrix and joint probability matrices are significant. Entry significance is defined as a probability value greater than the threshold values specified by the parameter. Significant entries in both probability and joint probability matrices are defined as 1 in the mask matrix."@en ; + skos:prefLabel "significance thresholds in ASSET mask matrix"@en . + + +### http://purl.org/neao/parameters#BartlettSmoothingKernel +:BartlettSmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf :SmoothingKernel ; + rdfs:comment "The kernel function has a triangular shape with end points at zero."@en ; + skos:prefLabel "Bartlett smoothing kernel"@en . + + +### http://purl.org/neao/parameters#BinSize +:BinSize rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "When discretizing continuous data into smaller intervals (bins), the parameter value determines the width of the intervals (in the unit of the data being discretized). Bins are adjacent and can be of different sizes."@en ; + skos:altLabel "bin width"@en ; + skos:prefLabel "bin size"@en . + + +### http://purl.org/neao/parameters#BlackmanWindowFunction +:BlackmanWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + rdfs:comment "Window function that uses the first three terms of a summation of cosines, minimizing spectral leakage. It was proposed by Ralph Beebe Blackman. The coefficients are an approximation of the ones used in the exact Blackman window function. Therefore, this window does not remove the third and fourth side lobes but produces smoother edges."@en ; + skos:prefLabel "Blackman window function"@en . + + +### http://purl.org/neao/parameters#BoxcarSmoothingKernel +:BoxcarSmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf :SmoothingKernel ; + owl:disjointWith :ExponentialSmoothingKernel ; + rdfs:comment "The kernel function is zero over the entire interval except for a single (smaller) interval where it has a constant value. This non-zero interval is the total width of the kernel."@en ; + skos:prefLabel "Boxcar smoothing kernel"@en . + + +### http://purl.org/neao/parameters#DBSCANMinimumNeighbors +:DBSCANMinimumNeighbors rdf:type owl:Class ; + rdfs:subClassOf :DBSCANParameter ; + owl:disjointWith :DBSCANNeighborhoodRadius ; + rdfs:comment "Integer value that determines the minimum number of points that must be present in the neighborhood of a sample point such that it is considered a core point. The parameter value includes the sample point being evaluated."@en ; + skos:altLabel "DBSCAN minimum samples"@en ; + skos:prefLabel "DBSCAN minimum number of neighbors"@en . + + +### http://purl.org/neao/parameters#DBSCANNeighborhoodRadius +:DBSCANNeighborhoodRadius rdf:type owl:Class ; + rdfs:subClassOf :DBSCANParameter ; + rdfs:comment "Determines the radius around a sample point used to define its neighborhood. The parameter defines the maximum distance between two sample points such that they are considered to be in the same neighborhood."@en ; + skos:altLabel "DBSCAN epsilon"@en , + "DBSCAN maximum distance"@en ; + skos:prefLabel "DBSCAN neighborhood radius"@en . + + +### http://purl.org/neao/parameters#DBSCANParameter +:DBSCANParameter rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "A parameter used by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) data clustering method."@en ; + skos:prefLabel "DBSCAN parameter"@en . + + +### http://purl.org/neao/parameters#DPSSWindowFunction +:DPSSWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + neao_base:abbreviation "DPSS window function"@en ; + rdfs:comment "A set of orthogonal sequences optimized to simultaneously achieve maximum concentration of energy within a defined frequency band and minimum leakage into neighboring frequency bands. They are frequently used as tapers in multiple applications (e.g., multitaper power spectral density estimation). The first sequence (order 0 or Slepian sequence) has maximal energy concentration in the main lobe."@en ; + skos:altLabel "Slepian sequences window function"@en ; + skos:prefLabel "discrete prolate spheroidal sequences window function"@en . + + +### http://purl.org/neao/parameters#DitheringTime +:DitheringTime rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "A time interval that defines a dithering distribution used to draw random numbers to displace individual spike times in a spike train."@en ; + skos:prefLabel "dithering time"@en . + + +### http://purl.org/neao/parameters#DownsampleFactor +:DownsampleFactor rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "A real value that indicates the factor by which the sampling period of a sampled time series is reduced in a downsampling operation."@en ; + skos:altLabel "decimation factor"@en ; + skos:prefLabel "downsample factor"@en . + + +### http://purl.org/neao/parameters#ExactBlackmanWindowFunction +:ExactBlackmanWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + rdfs:comment "Window function that uses the first three terms of a summation of cosines. It was proposed by Ralph Beebe Blackman. The coefficients are selected to remove the third and fourth side lobes, but the edges are discontinuous."@en ; + skos:prefLabel "exact Blackman window"@en . + + +### http://purl.org/neao/parameters#ExponentialSmoothingKernel +:ExponentialSmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf :SmoothingKernel ; + rdfs:comment "The kernel function is zero for points before the kernel center point, maximum at the center point, and decay exponentially for points greater than the center point. This kernel is asymmetric."@en ; + skos:prefLabel "exponential smoothing kernel"@en . + + +### http://purl.org/neao/parameters#FilterOrder +:FilterOrder rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "Integer value specifying the rate at which the filter response in the transition band falls. A higher filter order corresponds to a faster filter response decay."@en ; + skos:prefLabel "filter order"@en . + + +### http://purl.org/neao/parameters#FiringRate +:FiringRate rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The parameter value determines the average number of spikes fired by a neuron per time unit. It is used to control the behavior of steps that require a firing rate to control the output (e.g., a target mean firing rate when generating an artificial spike train)."@en ; + skos:prefLabel "firing rate"@en . + + +### http://purl.org/neao/parameters#FrequencyResolution +:FrequencyResolution rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "In analyses producing estimates in the frequency domain, the parameter determines the width of the frequency bins in the output, i.e., the resolution of the output on the frequency axis."@en ; + skos:prefLabel "frequency resolution"@en . + + +### http://purl.org/neao/parameters#HammingSmoothingKernel +:HammingSmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf :SmoothingKernel ; + rdfs:comment "The kernel function corresponds to a raised cosine (i.e., a single period of a cosine function \"raised\") with positive endpoints."@en ; + skos:prefLabel "Hamming smoothing kernel"@en . + + +### http://purl.org/neao/parameters#HammingWindowFunction +:HammingWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + rdfs:comment "A window function that corresponds to a raised cosine (i.e., a single period of a cosine function \"raised\") but with positive endpoints. Therefore, the function does not eliminate the discontinuities in the signal. It has better cancellation of the nearest side lobe, and a poorer cancellation of the others, with a wide main lobe. It is named after Richard Wesley Hamming."@en ; + skos:prefLabel "Hamming window function"@en . + + +### http://purl.org/neao/parameters#HannSmoothingKernel +:HannSmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf :SmoothingKernel ; + rdfs:comment "The kernel function corresponds to a raised cosine (i.e., a single period of a cosine function \"raised\" such that the negative troughs are zero). The endpoints reach zero smoothly at the boundaries."@en ; + skos:prefLabel "Hann smoothing kernel"@en . + + +### http://purl.org/neao/parameters#HannWindowFunction +:HannWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + rdfs:comment "A window function that corresponds to a raised cosine (i.e., a single period of a cosine function \"raised\" such that the negative troughs are zero). Therefore, the function reaches zero smoothly at the boundaries and eliminates all discontinuities in the signal. It has a wide main lobe and low side lobes. It is named after Julius von Hann."@en , + "raised-cosine window function"@en , + "von Hann window function"@en ; + skos:altLabel "hanning window function"@en ; + skos:prefLabel "Hann window function"@en . + + +### http://purl.org/neao/parameters#HighPassFrequencyCutoff +:HighPassFrequencyCutoff rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "When applying a filter to a time series, the frequency components below this parameter value will be attenuated. Usually, the parameter value corresponds to the frequency component attenuated by 3 dB in the filter transition band."@en ; + skos:prefLabel "high-pass frequency cutoff"@en . + + +### http://purl.org/neao/parameters#KaiserWindowFunction +:KaiserWindowFunction rdf:type owl:Class ; + rdfs:subClassOf :WindowFunction ; + rdfs:comment "This window function approximates the DPSS window function using Bessel functions. It is easier to compute than the DPSS window function. It was developed by James Kaiser."@en ; + skos:altLabel "Kaiser-Bessel window function"@en ; + skos:prefLabel "Kaiser window function"@en . + + +### http://purl.org/neao/parameters#KernelWidth +:KernelWidth rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The total width of a smoothing kernel."@en ; + skos:prefLabel "kernel width"@en . + + +### http://purl.org/neao/parameters#LowPassFrequencyCutoff +:LowPassFrequencyCutoff rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "When applying a filter to a time series, the frequency components above this parameter value will be attenuated. Usually, the parameter value corresponds to the frequency component attenuated by 3 dB in the filter transition band."@en ; + skos:prefLabel "low-pass frequency cutoff"@en . + + +### http://purl.org/neao/parameters#NumberFFTSamples +:NumberFFTSamples rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "Length of the time series considered by application of a fast Fourier transform (FFT). The value determines the frequency bin size based on the sampling frequency of the input data, and the length of the vector representing the result of the discrete Fourier transform."@en ; + skos:altLabel "FFT points"@en ; + skos:prefLabel "number of FFT samples"@en . + + +### http://purl.org/neao/parameters#PeakResolution +:PeakResolution rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The parameter defines the separation of individual peaks in the output."@en ; + skos:prefLabel "peak resolution"@en . + + +### http://purl.org/neao/parameters#SamplingFrequency +:SamplingFrequency rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The parameter determines the number of samples per time unit in sampled data. It is the inverse of the sampling period, i.e., the interval between two consecutive samples."@en ; + skos:altLabel "sampling rate"@en ; + skos:prefLabel "sampling frequency"@en . + + +### http://purl.org/neao/parameters#ShapeFactor +:ShapeFactor rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "When determining a probability distribution, the parameter affects the shape of the distribution. This is in contrast to other parameters that affect the location (e.g., mean) or scale (e.g., variance) of the distribution."@en ; + skos:prefLabel "shape factor"@en . + + +### http://purl.org/neao/parameters#SmoothingKernel +:SmoothingKernel rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "A smoothing kernel can be used in non-parametric smoothing techniques such as kernel smoothing or kernel density estimation. It represents a weighting function that assigns weights to neighboring data points based on their distance from a given point (the kernel center). Closer points are given higher weights. The weighted average of the data points within the kernel is computed to produce a smoothed estimate of the underlying data (e.g., a signal or probability density function). Smoothing kernels are characterized by their shape (e.g., Boxcar, exponential) and bandwidth. The bandwidth determines the extent of influence of neighboring data points on the smoothed estimate."@en ; + skos:prefLabel "smoothing kernel"@en . + + +### http://purl.org/neao/parameters#TemporalResolution +:TemporalResolution rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The parameter determines the smallest time interval that can be detected or produced by the method. In sampled time series data, this equals to the sampling period, i.e, the time interval between two consecutive samples."@en ; + skos:prefLabel "temporal resolution"@en . + + +### http://purl.org/neao/parameters#WaveletCenterFrequency +:WaveletCenterFrequency rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "The value of the center frequency of a wavelet. This is the frequency at which the wavelet oscillates most strongly, and it influences how effectively the wavelet can capture features of a signal at different frequencies. Wavelets with higher center frequencies are better suited for analyzing high-frequency components of a signal, while those with lower center frequencies are more effective for capturing low-frequency components."@en ; + skos:prefLabel "wavelet center frequency"@en . + + +### http://purl.org/neao/parameters#WindowFunction +:WindowFunction rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment """A mathematical function that is zero-valued outside a defined interval. When multiplied by another function or sampled data, the values outside the window interval will be transformed to zero, and the values inside the window will be weighted by the window function values. + +Window functions are used to select and modify a finite segment of a signal. This is useful for applying the fast Fourier transform (FFT) to a finite set of data where the length is not an integer number of periods of the signal. In this situation, there will be discontinuities at the boundaries of the signal, and the data in the frequency domain produced by the FFT will have frequency components not present in the original signal (spectral leakage). The window function can be used to reduce this discontinuities and mitigate spectral leakage when performing spectral analysis. + +Several types of window function exist. They will vary in shape in the time domain and will have distinct frequency characteristics in the frequency domain, with a main lobe and several side lobes. The main lobe is centered at each frequency component in the time domain, and the side lobes approach zero. These characteristics can be used to control the effect of spectral leakage when performing the analysis."""@en ; + skos:altLabel "apodization function"@en , + "tapering function"@en ; + skos:prefLabel "window function"@en . + + +### http://purl.org/neao/parameters#WindowLengthSamples +:WindowLengthSamples rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "Integer value that defines the number of samples encompassed by a window in sampled data, i.e., the window length. The window corresponds to a finite interval in the sampled data (e.g., a time interval in sampled time series data)."@en ; + skos:prefLabel "window length in samples"@en . + + +### http://purl.org/neao/parameters#WindowOverlapFactor +:WindowOverlapFactor rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "Real value (range 0-1) that determines the proportion of overlap between two adjacent windows. The window is a finite interval in the data (e.g., a time interval in time series data)."@en ; + skos:prefLabel "window overlap factor"@en . + + +### http://purl.org/neao/parameters#WindowOverlapSamples +:WindowOverlapSamples rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisParameter ; + rdfs:comment "Integer value (range 0 to the window length in samples) that determines the number of samples where two adjacent windows overlap. The window corresponds to a finite interval in sampled data (e.g., a time interval in sampled time series data)."@en ; + skos:prefLabel "window overlap in samples"@en . + + +################################################################# +# General axioms +################################################################# + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ASSETAnalysisParameter + :BinSize + :DBSCANParameter + :DitheringTime + :DownsampleFactor + :FilterOrder + :FiringRate + :FrequencyResolution + :HighPassFrequencyCutoff + :KernelWidth + :LowPassFrequencyCutoff + :NumberFFTSamples + :PeakResolution + :SamplingFrequency + :ShapeFactor + :SmoothingKernel + :TemporalResolution + :WaveletCenterFrequency + :WindowFunction + :WindowLengthSamples + :WindowOverlapFactor + :WindowOverlapSamples + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ASSETClusteringDistanceStretchFactor + :ASSETJointProbabilityMatrixFilterShape + :ASSETJointProbabilityMatrixNumberLargestNeighbors + :ASSETMaskMatrixSignificanceThresholds + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :BinSize + :DitheringTime + :DownsampleFactor + :FilterOrder + :FiringRate + :FrequencyResolution + :HighPassFrequencyCutoff + :LowPassFrequencyCutoff + :PeakResolution + :SamplingFrequency + :ShapeFactor + :WindowFunction + :WindowLengthSamples + :WindowOverlapFactor + :WindowOverlapSamples + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :BlackmanWindowFunction + :DPSSWindowFunction + :ExactBlackmanWindowFunction + :HammingWindowFunction + :HannWindowFunction + :KaiserWindowFunction + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :BlackmanWindowFunction + :DPSSWindowFunction + :HammingWindowFunction + :HannWindowFunction + :KaiserWindowFunction + ) +] . + + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi diff --git a/src/steps/catalog-v001.xml b/src/steps/catalog-v001.xml new file mode 100644 index 0000000..19ee375 --- /dev/null +++ b/src/steps/catalog-v001.xml @@ -0,0 +1,7 @@ + + + + + + + diff --git a/src/steps/steps.owl b/src/steps/steps.owl new file mode 100644 index 0000000..774238f --- /dev/null +++ b/src/steps/steps.owl @@ -0,0 +1,3304 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@prefix neao_bib: . +@prefix neao_base: . +@prefix neao_data: . +@base . + + rdf:type owl:Ontology ; + owl:versionIRI ; + owl:imports neao_base: , + neao_bib: , + neao_data: ; + dcterms:created "2022-01-19" ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + vann:preferredNamespacePrefix "neao_steps" ; + vann:preferredNamespaceUri "http://purl.org/neao/steps#" ; + rdfs:comment """This module in the Neuroelectrophysiology Analysis Ontology contains classes that represent (atomic) steps in the analysis of neuroelectrophysiology data. + +The main classes are subclasses of the AnalysisStep class (defined in the base module), and represent the different methods and procedures used in the analysis to generate new data or to perform specific operations aimed to transform or extract additional information from the input(s). The classes are organized in a taxonomy, and the lowest level of the hierarchy represent specific, fine-grained descriptions of a step used in the analysis. + +Grouping classes are provided to identify analysis steps according to their semantic similarities. This is used to keep the fine-grained descriptions associated with specific analysis methods (e.g., a step that used either the Welch or multitaper method to compute the power spectral density) while providing the ability to identify the steps in a more general nature (e.g., a step that computed a power spectral density). The grouping classes are defined in the taxonomy hierarchy by subclasses, and also as classes inferred by reasoning with the hasPurpose object property (that points to individuals of the AnalysisPurpose class, such as FunctionalConnectivityPurpose) or data properties defining boolean values (e.g., isBivariate). Therefore, groupings across multiple semantic dimensions are available (e.g., bivariate analyses or functional connectivity analyses). + +The information regarding the inputs and outputs can be associated with each step by the hasInput and hasOutput object properties that point to individuals that represent data entities (using the classes defined in the data module). + +The parameters used to control the behavior of the analysis step are defined with the usesParameter object property that points to individuals that represent specific parameters (using the classes defined in the parameters module). + +The relevant bibliographic references are provided with the hasBibliographicReference annotation property that points to individuals of the BiRO BibliographicReference class (defined in the bibliography module). This structures the specific information associated with the analysis step represented by the class, and helps to disambiguate the description of the diversity of methods that are available to analyze neuroelectrophysiology data. This would be the case, for example, of different algorithms and assumptions (e.g., computing the power spectral density using either the Welch or multitaper approach), and the evolution/modifications of a method (e.g., different computations of phase lag index estimates). + +The details of the software code associated with each analysis step can be provided by the isImplementedIn object property, that points to individuals of the SoftwareImplementation class (defined in the base module). + +Finally, the hasSubstep object property is used to describe compound analyses. These analyses involve the execution of multiple smaller steps (substeps) that are associated with specific parameters and intermediary data inputs and outputs. The main compound process can point to other individuals of the AnalysisStep class using the hasSubstep property. Therefore, the appropriate semantic information and description associated with either the compound analysis or each individual substep can be provided in the ontology."""@en ; + rdfs:label "Neuroelectrophysiology Analysis Ontology - Analysis Steps"@en ; + owl:versionInfo "0.1.0" . + +################################################################# +# Annotation properties +################################################################# + +### http://purl.org/vocab/vann/preferredNamespacePrefix +vann:preferredNamespacePrefix rdf:type owl:AnnotationProperty . + + +### http://purl.org/vocab/vann/preferredNamespaceUri +vann:preferredNamespaceUri rdf:type owl:AnnotationProperty . + + +################################################################# +# Object Properties +################################################################# + +### http://purl.org/neao/base#hasInput +neao_base:hasInput rdf:type owl:ObjectProperty . + + +### http://purl.org/neao/base#hasOutput +neao_base:hasOutput rdf:type owl:ObjectProperty . + + +### http://purl.org/neao/steps#hasPurpose +:hasPurpose rdf:type owl:ObjectProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range :AnalysisPurpose ; + rdfs:comment "Defines an analysis purpose for the analysis step. This property is used to group analysis steps according to their similarity."@en ; + skos:prefLabel "has purpose"@en . + + +### http://purl.org/spar/biro/references + rdfs:domain . + + +################################################################# +# Data properties +################################################################# + +### http://purl.org/neao/steps#isBivariate +:isBivariate rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the analysis step uses two data inputs (true)."@en ; + skos:prefLabel "is bivariate"@en . + + +### http://purl.org/neao/steps#isDirected +:isDirected rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the analysis step is used to perform an analysis providing information on the direction of the association between the inputs (true) opposed to no direction information (false)."@en ; + skos:prefLabel "is directed"@en . + + +### http://purl.org/neao/steps#isFrequencyDomain +:isFrequencyDomain rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the analysis in the step is performed in the frequency domain (true)."@en ; + skos:prefLabel "is frequency domain"@en . + + +### http://purl.org/neao/steps#isModelBased +:isModelBased rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the step performs a model-based (true) or model-free analysis (false)."@en ; + skos:prefLabel "is model-based"@en . + + +### http://purl.org/neao/steps#isMultivariate +:isMultivariate rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the analysis step uses three or more data inputs (true)."@en ; + skos:prefLabel "is multivariate"@en . + + +### http://purl.org/neao/steps#isTimeDomain +:isTimeDomain rdf:type owl:DatatypeProperty ; + rdfs:subPropertyOf owl:topDataProperty ; + rdfs:domain neao_base:AnalysisStep ; + rdfs:range xsd:boolean ; + rdfs:comment "Defines if the analysis in the step is performed in the time domain (true)."@en ; + skos:prefLabel "is time domain"@en . + + +################################################################# +# Classes +################################################################# + +### http://purl.org/neao/base#AnalysisStep +neao_base:AnalysisStep rdf:type owl:Class . + + +### http://purl.org/neao/data#ComplexityDistribution +neao_data:ComplexityDistribution rdf:type owl:Class . + + +### http://purl.org/neao/data#CurrentSourceDensity +neao_data:CurrentSourceDensity rdf:type owl:Class . + + +### http://purl.org/neao/data#InterspikeIntervals +neao_data:InterspikeIntervals rdf:type owl:Class . + + +### http://purl.org/neao/data#LVR +neao_data:LVR rdf:type owl:Class . + + +### http://purl.org/neao/data#NeuronalActivityPattern +neao_data:NeuronalActivityPattern rdf:type owl:Class . + + +### http://purl.org/neao/data#SpikeTrain +neao_data:SpikeTrain rdf:type owl:Class . + + +### http://purl.org/neao/data#SpikeWaveform +neao_data:SpikeWaveform rdf:type owl:Class . + + +### http://purl.org/neao/data#SpikeWaveformSNR +neao_data:SpikeWaveformSNR rdf:type owl:Class . + + +### http://purl.org/neao/data#SpikeWaveformWidth +neao_data:SpikeWaveformWidth rdf:type owl:Class . + + +### http://purl.org/neao/steps#ASSETAnalysisProbabilityMatrixSubstep +:ASSETAnalysisProbabilityMatrixSubstep rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis substep that computes the probability matrix (PMAT). The probability matrix contains, for each entry in the intersection matrix, the cumulative probability representing the probability of having the overlap in the IMAT under the assumption that the spike trains are independent. If an entry in the PMAT is large, the null hypothesis of independence is rejected, and the alternative hypothesis that the observed overlap reflects active synchronization between the involved neurons at the time bins of the intersection is accepted. The PMAT computation can be done with either an analytical or Monte Carlo approach."@en ; + skos:prefLabel "ASSET analysis probability matrix substep"@en . + + +### http://purl.org/neao/steps#ASSETAnalysisSubstep +:ASSETAnalysisSubstep rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "An analysis step that is an individual part of the ASSET analysis method to identify neuronal activity patterns in spike train data."@en ; + skos:prefLabel "ASSET analysis substep"@en . + + +### http://purl.org/neao/steps#AnalysisPurpose +:AnalysisPurpose rdf:type owl:Class ; + rdfs:comment "Analysis purpose refers to the specific objective or goal that an analysis is intended to achieve. It outlines what the analysis step aims to discover, understand, or demonstrate. This class is intended for grouping steps that perform analyses with similar goals/outputs but with distinct methodological or algorithmic approaches. It is used as a normalization class via the hasPurpose object property."@en ; + skos:prefLabel "analysis purpose"@en . + + +### http://purl.org/neao/steps#ApplyAdaptiveKernelSmoothing +:ApplyAdaptiveKernelSmoothing rdf:type owl:Class ; + rdfs:subClassOf :KernelSmoothing ; + rdfs:comment "A kernel smoothing that uses a variable-width kernel. The kernel width varies adaptively depending on the local density of the data. This method takes into consideration local variations in data density, resulting in a more accurate representation of the underlying patterns and structures."@en ; + skos:prefLabel "apply adaptive kernel smoothing"@en . + + +### http://purl.org/neao/steps#ApplyAnalyticSignalConversion +:ApplyAnalyticSignalConversion rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that uses the Hilbert transform of a real-valued input time series to construct the analytic signal, a complex-valued time series where the real part is the original real-valued signal and the imaginary part is the Hilbert transform. The analytic signal does not have negative frequency components."@en ; + skos:prefLabel "apply analytic signal conversion"@en . + + +### http://purl.org/neao/steps#ApplyButterworthFilter +:ApplyButterworthFilter rdf:type owl:Class ; + rdfs:subClassOf :InfiniteImpulseResponseFiltering ; + rdfs:comment "An infinite impulse response filtering that uses a Butterworth type filter, i.e., a filter designed to have a maximally flat frequency response in the passband (no ripples). The frequency response gradually decreases to zero in the stopband, and the steepness of the decrease (roll-off) is controlled by the order of the filter."@en ; + skos:altLabel "apply maximally flat magnitude filter"@en ; + skos:prefLabel "apply Butterworth filter"@en . + + +### http://purl.org/neao/steps#ApplyCanonicalPolyadicTensorDecomposition +:ApplyCanonicalPolyadicTensorDecomposition rdf:type owl:Class ; + rdfs:subClassOf :TensorComponentAnalysis ; + neao_base:abbreviation "apply CPD"@en ; + neao_base:hasBibliographicReference neao_bib:Williams2018_1099 ; + rdfs:comment "A tensor component analysis (TCA) that expresses the input high-dimensional tensor as a sum of rank-one tensors (tensor components). Each dimension in the tensor component corresponds to a dimension in the input high-dimensional tensor. For neural data, a tensor could be used to represent trial-by-trial spiking activity, with neurons, time, and trials as dimensions. Therefore, each tensor component produced from that input will have a rank-one tensor for the neurons, time, and trial, which describes both within- and between-trial changes."@en ; + skos:altLabel "apply tensor rank decomposition"@en ; + skos:prefLabel "apply canonical polyadic tensor decomposition"@en . + + +### http://purl.org/neao/steps#ApplyCoupledCanonicalPolyadicTensorDecomposition +:ApplyCoupledCanonicalPolyadicTensorDecomposition rdf:type owl:Class ; + rdfs:subClassOf :TensorComponentAnalysis ; + neao_base:abbreviation "apply CCPD"@en ; + neao_base:hasBibliographicReference neao_bib:Sorensen2013_228 ; + rdfs:comment "A tensor component analysis (TCA) that expresses multiple high-dimensional input tensors as a sum of rank one tensors (tensor components). The output tensor components have shared vectors that summarize an input dimension across all input tensors. For example, if analyzing two tensors, each representing trial-by-trial spiking activity obtained from a distinct experimental subject (neurons X time X trials), the coupled canonical polyadic tensor decomposition (CCPD) could produce tensor components for each dataset where the vector for the trial and time dimensions are the same, but the neuron dimension is unique for each dataset (hence, each subject). Therefore, CCPD is useful for scenarios with multiple and related datasets, allowing for the exploitation of shared information to enhance the decomposition results."@en ; + skos:prefLabel "apply coupled canonical polyadic tensor decomposition"@en . + + +### http://purl.org/neao/steps#ApplyDFTNoiseRemoval +:ApplyDFTNoiseRemoval rdf:type owl:Class ; + rdfs:subClassOf :LineNoiseRemoval ; + neao_base:abbreviation "apply DFT noise removal"@en ; + rdfs:comment "A line noise removal that uses a discrete Fourier transform (DFT) filter and estimates the power line component amplitude in the input data by fitting a sine and cosine at a user-specified line noise frequency (e.g., 50 Hz), followed by the subtraction of those components from the input."@en ; + skos:prefLabel "apply discrete Fourier transform noise removal"@en . + + +### http://purl.org/neao/steps#ApplyDataConcatenation +:ApplyDataConcatenation rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that joins two or more data inputs into a single data element. For example, in a multitrial experimental session, where data was acquired separately as one epoch per trial, a continuous data segment for the session can be constructed by concatenating all trial epochs together."@en ; + skos:prefLabel "apply data concatenation"@en . + + +### http://purl.org/neao/steps#ApplyDemixedPrincipalComponentAnalysis +:ApplyDemixedPrincipalComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PrincipalComponentAnalysis ; + neao_base:abbreviation "apply dPCA"@en ; + neao_base:hasBibliographicReference neao_bib:Kobak2016_e10989 ; + rdfs:comment "A principal component analysis (PCA) that uses a modified version of the standard PCA for neural activity data analysis. Demixed PCA (dPCA) not only obtains a low-dimensional representation of the input data, but it also demixes the dependencies of the population activity on the task parameters. Therefore, dPCA can show the dependence of the neural representation on parameters such as stimuli, subject decisions, or rewards."@en ; + skos:prefLabel "apply demixed principal component analysis"@en . + + +### http://purl.org/neao/steps#ApplyDiscreteFourierTransform +:ApplyDiscreteFourierTransform rdf:type owl:Class ; + rdfs:subClassOf :FrequencyDomainTransformation ; + rdfs:comment "A frequency-domain transformation that applies the discrete Fourier transform (DFT) to an input time series acquired in equally-spaced samples. The DFT is used to obtain the frequency representation of the time-domain input. The DFT output is a sequence of coefficients of complex sinusoids, each representing a frequency component in the input signal. Each frequency component consists of an interval (or bin), and the width of the bin determines the frequency resolution of the DFT. The number of frequency components and the frequency resolution is determined by the length of the input signal (number of samples) and the sampling frequency. For large datasets, the computation is computationally expensive (O(N^2) complexity)."@en ; + skos:altLabel "apply DFT"@en ; + skos:prefLabel "apply discrete Fourier transform"@en . + + +### http://purl.org/neao/steps#ApplyDistanceCovarianceAnalysis +:ApplyDistanceCovarianceAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReduction ; + neao_base:abbreviation "apply DCA"@en ; + neao_base:hasBibliographicReference neao_bib:Cowley2017_242 ; + rdfs:comment "A dimensionality reduction that identifies linear and nonlinear relationships between multiple input datasets. The method identifies linear projections (DCA dimensions) that maximize the distance covariance statistic (an Euclidean-based correlational statistic). For example, for recordings from different brain regions (two neuronal populations), the distance covariance analysis (DCA) can identify the dimensions in the population activity in the different brain areas that are related to each other. The dimensionality reduction can also take other dependent variables into account (e.g., stimulus or behavioral variables)."@en ; + skos:prefLabel "apply distance covariance analysis"@en . + + +### http://purl.org/neao/steps#ApplyDownsampling +:ApplyDownsampling rdf:type owl:Class ; + rdfs:subClassOf :Resampling ; + owl:disjointWith :ApplyUpsampling ; + rdfs:comment "A resampling that reduces the number of samples in the input data (i.e., reduces the sampling frequency). This is often accomplished after applying an anti-aliasing filter (i.e., to remove frequencies above half the value of the new sampling frequency)."@en ; + skos:altLabel "apply decimation"@en , + "apply downscaling"@en ; + skos:prefLabel "apply downsampling"@en . + + +### http://purl.org/neao/steps#ApplyFastFourierTransform +:ApplyFastFourierTransform rdf:type owl:Class ; + rdfs:subClassOf :ApplyDiscreteFourierTransform ; + rdfs:comment "A frequency-domain transformation that uses the fast Fourier transform (FFT) algorithm to compute the discrete Fourier transform (DFT). The computation of the DFT is computationally expensive for large datasets. The FFT reduces the number of computations significantly by using a divide-and-conquer approach, leveraging the symmetry and periodicity properties of the DFT. The complexity is O(N log N). The output of the method is similar to the DFT, i.e., a sequence of coefficients of complex sinusoids, each representing a frequency component in the input signal. The FFT takes a size parameter, that refers to the number of points in the input data sequence that is used for the computation. The FFT size determines the frequency resolution, and, for maximal efficiency, should be a multiple of 2."@en ; + skos:altLabel "apply FFT"@en ; + skos:prefLabel "apply fast Fourier transform"@en . + + +### http://purl.org/neao/steps#ApplyFiniteImpulseResponseFilter +:ApplyFiniteImpulseResponseFilter rdf:type owl:Class ; + rdfs:subClassOf :FiniteImpulseResponseFiltering ; + neao_base:abbreviation "apply FIR filter"@en ; + rdfs:comment "A finite impulse response (FIR) filtering where a custom-designed FIR filter is applied to the input data."@en ; + skos:prefLabel "apply finite impulse response filter"@en . + + +### http://purl.org/neao/steps#ApplyFiniteImpulseResponseFilterKaiserWindow +:ApplyFiniteImpulseResponseFilterKaiserWindow rdf:type owl:Class ; + rdfs:subClassOf :FiniteImpulseResponseFiltering ; + neao_base:abbreviation "apply FIR filter with Kaiser window"@en ; + rdfs:comment "A finite impulse response (FIR) filtering that uses a FIR filter whose impulse response is controlled by applying a Kaiser window function."@en ; + skos:prefLabel "apply finite impulse response filter with Kaiser window"@en . + + +### http://purl.org/neao/steps#ApplyFixedKernelSmoothing +:ApplyFixedKernelSmoothing rdf:type owl:Class ; + rdfs:subClassOf :KernelSmoothing ; + rdfs:comment "A kernel smoothing that uses a fixed-width kernel. The kernel type and kernel width can be specified as parameters."@en ; + skos:prefLabel "apply fixed kernel smoothing"@en . + + +### http://purl.org/neao/steps#ApplyGeneralLinearModelPolynomialDetrending +:ApplyGeneralLinearModelPolynomialDetrending rdf:type owl:Class ; + rdfs:subClassOf :Detrending ; + rdfs:comment "A detrending that uses a general linear model to fit a polynomial from the input data and remove the mean and linear trend."@en ; + skos:prefLabel "apply general linear model polynomial detrending"@en . + + +### http://purl.org/neao/steps#ApplyHilbertTransform +:ApplyHilbertTransform rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that applies the Hilbert transform to a real-valued time series. The Hilbert transform shifts the phase of each frequency component of the signal by 90 degrees: positive frequencies are shifted by -90 degrees, and negative frequencies are shifted by +90 degrees. This can be used to construct the analytic signal."@en ; + skos:prefLabel "apply Hilbert transform"@en . + + +### http://purl.org/neao/steps#ApplyIndependentComponentAnalysis +:ApplyIndependentComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + neao_base:abbreviation "apply ICA"@en ; + neao_base:hasBibliographicReference neao_bib:Jutten1991_1 ; + rdfs:comment "A data transformation that separates a multivariate input signal into additive subcomponents (independent components). The independent components are statistically independent from each other."@en ; + skos:prefLabel "apply independent component analysis"@en . + + +### http://purl.org/neao/steps#ApplyInfiniteImpulseResponseFilter +:ApplyInfiniteImpulseResponseFilter rdf:type owl:Class ; + rdfs:subClassOf :InfiniteImpulseResponseFiltering ; + neao_base:abbreviation "apply IIR filter"@en ; + rdfs:comment "An infinite impulse response (IIR) filtering where a custom-designed IIR filter is applied to the input data."@en ; + skos:prefLabel "apply infinite impulse response filter"@en . + + +### http://purl.org/neao/steps#ApplyInterpolation +:ApplyInterpolation rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that estimates new (intermediate) values between the known values in the input data. This can be accomplished using several methods, such as linear interpolation (i.e., estimating the values along a straight line connecting adjacent points), polynomial interpolation (i.e., using polynomials to estimate the values between points), or spline Interpolation (i.e., using piecewise polynomials that pass through the known data points and provide a smooth curve)."@en ; + skos:prefLabel "apply interpolation"@en . + + +### http://purl.org/neao/steps#ApplyLinearDiscriminantAnalysis +:ApplyLinearDiscriminantAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReduction ; + neao_base:abbreviation "apply LDA"@en ; + rdfs:comment "A dimensionality reduction that finds a linear combination of features that separates two or more classes defined in the input data. The input data must have a variable that defines the class for each observation, and continuous variables that are used for the linear discriminant analysis (LDA). LDA finds an optimal projection vector that maximizes the distance between the means of the different classes, and minimizes the variance within each class. LDA projects the input data into the lower-dimensional space, therefore reducing the number of features while retaining the information needed for classification. The optimal projection vectors found by LDA are the Fisher linear discriminants."@en ; + skos:prefLabel "apply linear discriminant analysis"@en . + + +### http://purl.org/neao/steps#ApplyLocalLinearRegressionDetrending +:ApplyLocalLinearRegressionDetrending rdf:type owl:Class ; + rdfs:subClassOf :Detrending ; + neao_base:hasBibliographicReference neao_bib:Bokil2010_146 ; + rdfs:comment "A detrending that removes a running line fit using local linear regression. Local linear regression estimates a function by fitting a low-order polynomial to data within a sliding window (local neighborhood) across the input data."@en ; + skos:prefLabel "apply local linear regression detrending"@en . + + +### http://purl.org/neao/steps#ApplyLocalRegressionAndLikelihoodSmoothing +:ApplyLocalRegressionAndLikelihoodSmoothing rdf:type owl:Class ; + rdfs:subClassOf :DataSmoothing ; + neao_base:hasBibliographicReference neao_bib:Bokil2010_146 , + neao_bib:Loader2006 ; + rdfs:comment "A data smoothing that estimates a low-order polynomial in a local neighborhood (window) of any value in the input data. Polynomial coefficients are estimated using the least mean squares method. Contrary to kernel smoothing methods, this is a non-parametric approach and has reduced bias at the boundaries of the input data."@en ; + skos:prefLabel "apply local regression and likelihood smoothing"@en . + + +### http://purl.org/neao/steps#ApplyMedianRescaling +:ApplyMedianRescaling rdf:type owl:Class ; + rdfs:subClassOf :DataNormalization ; + rdfs:comment "A data normalization that uses the median and interquartile range (IQR) to rescale the values of the input data. This method is less sensitive to outliers (compared to the z-score transform), and therefore is known as robust scaling."@en ; + skos:altLabel "apply robust scaling"@en ; + skos:prefLabel "apply median rescaling"@en . + + +### http://purl.org/neao/steps#ApplyMinMaxNormalization +:ApplyMinMaxNormalization rdf:type owl:Class ; + rdfs:subClassOf :DataNormalization ; + rdfs:comment "A data normalization that adjusts the range and distribution of values in the data input such that they fall within a fixed range, based on the minimum and maximum values in the input data. In the typical case, the values are normalized to the [0, 1] interval."@en ; + skos:altLabel "apply rescaling"@en ; + skos:prefLabel "apply min-max normalization"@en . + + +### http://purl.org/neao/steps#ApplyMovementArtifactRemoval +:ApplyMovementArtifactRemoval rdf:type owl:Class ; + rdfs:subClassOf :ArtifactRemoval ; + rdfs:comment "An artifact removal that identifies and removes artifacts originating from movements of the experimental subject (e.g., eye movement, head movement)."@en ; + skos:prefLabel "apply movement artifact removal"@en . + + +### http://purl.org/neao/steps#ApplyNeuralTrajectoryGaussianProcessFactorAnalysis +:ApplyNeuralTrajectoryGaussianProcessFactorAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReduction , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :LatentDynamicsPurpose + ] ; + neao_base:abbreviation "apply GPFA"@en ; + neao_base:hasBibliographicReference neao_bib:Yu2009_614 ; + rdfs:comment "A dimensionality reduction that uses the Gaussian process factor analysis (GPFA) method described by Yu et al. (2009). GPFA extracts smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials over time. The input is a set of spike trains representing multitrial activity of multiple neurons recorded in parallel. The input spike trains are binned, and factor analysis is applied to reduce the dimensionality while smoothing the resulting low-dimensional trajectories by fitting a Gaussian process (GP) model to them. The identified trajectories are called neural trajectories, and show the evolution of the activity of the population of neurons over time."@en ; + skos:prefLabel "apply neural trajectory Gaussian process factor analysis"@en . + + +### http://purl.org/neao/steps#ApplyNonNegativeTensorComponentAnalysis +:ApplyNonNegativeTensorComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :TensorComponentAnalysis ; + rdfs:comment "A tensor component analysis (TCA) where a non-negative constraint is applied to the decomposition. This is desirable when the underlying components have physical interpretation and negative values are not possible."@en ; + skos:prefLabel "apply non-negative tensor component analysis"@en . + + +### http://purl.org/neao/steps#ApplyNotchFilter +:ApplyNotchFilter rdf:type owl:Class ; + rdfs:subClassOf :InfiniteImpulseResponseFiltering ; + rdfs:comment "An infinite impulse response filtering that uses a filter designed to attenuate or eliminate a narrow band of frequencies in the input data (stopband) while allowing other frequencies to pass through relatively unaffected."@en ; + skos:prefLabel "apply notch filter"@en . + + +### http://purl.org/neao/steps#ApplyNotchFilterNoiseRemoval +:ApplyNotchFilterNoiseRemoval rdf:type owl:Class ; + rdfs:subClassOf :LineNoiseRemoval ; + rdfs:comment "A line noise removal that employs a notch filter to remove line noise. A notch filter is designed to attenuate or eliminate a narrow band of frequencies in the input data (stopband) while allowing other frequencies to pass through relatively unaffected. The notch filter stop band is usually centered at the power line frequency (i.e., 50 Hz or 60 Hz depending on the location)."@en ; + skos:prefLabel "apply notch filter noise removal"@en . + + +### http://purl.org/neao/steps#ApplyOutlierRemoval +:ApplyOutlierRemoval rdf:type owl:Class ; + rdfs:subClassOf :ArtifactRemoval ; + rdfs:comment "An artifact removal that identifies and removes values in the input data that differs significantly from other values (outliers). Outliers may arise from the variability in the measurement or be the result of experimental error."@en ; + skos:prefLabel "apply outlier removal"@en . + + +### http://purl.org/neao/steps#ApplyPadding +:ApplyPadding rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that adds extra data (often zeros or other predefined values) to the beginning, end, or both sides of the input data."@en ; + skos:prefLabel "apply padding"@en . + + +### http://purl.org/neao/steps#ApplyProbabilisticPrincipalComponentAnalysis +:ApplyProbabilisticPrincipalComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PrincipalComponentAnalysis ; + neao_base:abbreviation "apply pPCA"@en ; + neao_base:hasBibliographicReference neao_bib:Tipping1999_611 ; + rdfs:comment "A principal component analysis (PCA) that assumes a probabilistic model for the generation of the observed data, according to Tipping & Bishop (1999). The model assumes that the values in the data input are generated from the lower-dimensional subspace of latent variables (principal components) plus an additive Gaussian noise. This generalizes the standard PCA for the case where the noise covariance approaches zero. The probabilistic PCA (pPCA) allows for uncertainty estimation and modeling of the data generation process, and can be employed when there are missing values in the input data."@en ; + skos:prefLabel "apply probabilistic principal component analysis"@en . + + +### http://purl.org/neao/steps#ApplyRectification +:ApplyRectification rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that computes the absolute value of the input data (rectification)."@en ; + skos:prefLabel "apply rectification"@en . + + +### http://purl.org/neao/steps#ApplyRereference +:ApplyRereference rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that changes the reference point of the data recorded from an electrode. This can be performed by calculating the average across the data obtained from all electrodes and subtracting it from each individual electrode’s data (reducing common noise) or referencing each electrode to its nearest neighbor or a defined pair, subtracting one signal from another (bipolar referencing)."@en ; + skos:prefLabel "apply rereference"@en . + + +### http://purl.org/neao/steps#ApplySpectrumInterpolationNoiseRemoval +:ApplySpectrumInterpolationNoiseRemoval rdf:type owl:Class ; + rdfs:subClassOf :LineNoiseRemoval ; + neao_base:hasBibliographicReference neao_bib:Mewett2004_524 ; + rdfs:comment "A line noise removal that uses spectral interpolation to remove power line noise. After obtaining the discrete Fourier transform (DFT) of the input signal with noise, the original frequency component at the power line oscillation frequency can be estimated by interpolating the amplitude spectrum (obtained from the DFT) at the power line frequency (e.g., 50 Hz), followed by the inverse DFT to reconstruct the signal."@en ; + skos:prefLabel "apply spectrum interpolation noise removal"@en . + + +### http://purl.org/neao/steps#ApplySpikeExtractionFromTimeSeries +:ApplySpikeExtractionFromTimeSeries rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that obtains a series of spike times (i.e., a spike train) from an input time series (e.g., voltages recorded from an electrode). The spike times can be estimated, for example, by taking all the time points where the values in the input data are greater or lower than a threshold value."@en ; + skos:prefLabel "apply spike extraction from time series"@en . + + +### http://purl.org/neao/steps#ApplySpikeTrainBinarization +:ApplySpikeTrainBinarization rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that takes an input spike train and returns an array of boolean values indicating if at least one spike occurred at individual time points."@en ; + skos:prefLabel "apply spike train binarization"@en . + + +### http://purl.org/neao/steps#ApplySpikeTrainBinning +:ApplySpikeTrainBinning rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that performs a binning operation on the input spike train data. The transformation discretizes the duration of the input spike train(s) into smaller time intervals (bins), and obtains the number of spikes occurring into each bin (binned spike train). Additionally, the occurrence of spikes into each bin can be converted into a binary form (i.e., bins with or without spikes). This is known as clipping. The width of the bin interval is specified by a parameter (bin size)."@en ; + skos:prefLabel "apply spike train binning"@en . + + +### http://purl.org/neao/steps#ApplySpikeWaveformInterpolation +:ApplySpikeWaveformInterpolation rdf:type owl:Class ; + rdfs:subClassOf :ApplyInterpolation ; + rdfs:comment "A data transformation that estimates additional (unknown) values between sample points of a spike waveform input."@en ; + skos:prefLabel "apply spike waveform interpolation"@en . + + +### http://purl.org/neao/steps#ApplySpikeWaveformOutlierRejection +:ApplySpikeWaveformOutlierRejection rdf:type owl:Class ; + rdfs:subClassOf :ArtifactRemoval ; + rdfs:comment "An artifact removal that identifies and removes spike waveforms that differ significantly from the other spike waveforms in the input. This usually involves identifying waveforms with too late peaks, or in which the rising phase of the potential does not align with the peaks of all other waveforms in the input."@en ; + skos:prefLabel "apply spike waveform outlier rejection"@en . + + +### http://purl.org/neao/steps#ApplySpikeWaveformPeakAlignment +:ApplySpikeWaveformPeakAlignment rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that modifies input spike waveforms in order to align their peak values in time."@en ; + skos:prefLabel "apply spike waveform peak alignment"@en . + + +### http://purl.org/neao/steps#ApplyStandardPrincipalComponentAnalysis +:ApplyStandardPrincipalComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PrincipalComponentAnalysis ; + neao_base:abbreviation "apply standard PCA"@en ; + rdfs:comment "A principal component analysis (PCA) that operates by computing the covariance matrix of the input data, which is then decomposed into its eigenvectors and eigenvalues. The eigenvectors, corresponding to the principal components (PCs), are sorted by the magnitude of their associated eigenvalues. The eigenvectors with the largest eigenvalues explain the most variance in the data and thus form the primary PCs."@en ; + skos:altLabel "apply classical principal component analysis"@en ; + skos:prefLabel "apply standard principal component analysis"@en . + + +### http://purl.org/neao/steps#ApplyStimulationArtifactRemoval +:ApplyStimulationArtifactRemoval rdf:type owl:Class ; + rdfs:subClassOf :ArtifactRemoval ; + rdfs:comment "An artifact removal that identifies and removes artifacts originating from presenting a stimulus during the recording (e.g., electrical stimulation)."@en ; + skos:prefLabel "apply stimulation artifact removal"@en . + + +### http://purl.org/neao/steps#ApplySum +:ApplySum rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that performs the addition of two or more data inputs to obtain a sum."@en ; + skos:prefLabel "apply sum"@en . + + +### http://purl.org/neao/steps#ApplySynchronousSpikeRemoval +:ApplySynchronousSpikeRemoval rdf:type owl:Class ; + rdfs:subClassOf :ArtifactRemoval ; + rdfs:comment "An artifact removal that identifies and removes spikes across two or more spike train inputs that occurred simultaneously within a temporal precision specified by a parameter. The temporal precision is usually the sampling rate used by the recording equipment: if different neurons fired within an interval equal to or smaller than the sampling period, this suggests that this synchronous activity does not come from temporal synchronization of the neurons but rather due to an interference in the recording (e.g., electrical noise picked simultaneously by multiple channels)."@en ; + skos:prefLabel "apply synchronous spike removal"@en . + + +### http://purl.org/neao/steps#ApplyThomsonRegressionNoiseRemoval +:ApplyThomsonRegressionNoiseRemoval rdf:type owl:Class ; + rdfs:subClassOf :LineNoiseRemoval ; + neao_base:hasBibliographicReference neao_bib:Thomson1982_1055 ; + rdfs:comment "A line noise removal that uses Thomson's regression method (1982) for detecting sinusoids, that identifies and removes significant sine waves from the input data. The desired frequencies can be specified by parameter, or determined using an F-statistic."@en ; + skos:prefLabel "apply Thomson regression noise removal"@en . + + +### http://purl.org/neao/steps#ApplyTrialExtraction +:ApplyTrialExtraction rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that extracts trial segments from an input data entity storing a longer and continuous data stream. A trial is a single instance of a repeated experimental procedure. For example, during an electrophysiology experiment, a visual stimulus might be presented several times, with neural activity recorded each time. Each presentation of the stimulus defines a trial. Since the data is recorded continuously, this data transformation identifies and isolates the segments of data corresponding to each individual stimulus presentation, returning them as separate data entities."@en ; + skos:prefLabel "apply trial extraction"@en . + + +### http://purl.org/neao/steps#ApplyUpsampling +:ApplyUpsampling rdf:type owl:Class ; + rdfs:subClassOf :Resampling ; + rdfs:comment "A resampling that increases the number of samples in the data (i.e., increases the sampling frequency). This is often accomplished by adding new (zero-valued) samples between existing ones followed by applying a lowpass filter to replace the zeros and smooth out the discontinuities."@en ; + skos:altLabel "apply upscaling"@en ; + skos:prefLabel "apply upsampling"@en . + + +### http://purl.org/neao/steps#ApplyWindowedSincFilter +:ApplyWindowedSincFilter rdf:type owl:Class ; + rdfs:subClassOf :FiniteImpulseResponseFiltering ; + rdfs:comment "A finite impulse response filtering that employs the convolution with a sinc function kernel multiplied by a window function (e.g., Blackman or Hamming). The kernel is obtained by evaluating the sinc function for the cutoff frequencies specified as parameters, followed by truncation of the filter skirt, and applying the window to reduce the artifacts introduced from the truncation. The windowed-sinc filter is stable and very efficient to separate one band of frequencies from another."@en ; + skos:prefLabel "apply windowed-sinc filter"@en . + + +### http://purl.org/neao/steps#ApplyZscoreTransform +:ApplyZscoreTransform rdf:type owl:Class ; + rdfs:subClassOf :DataNormalization ; + rdfs:comment "A data normalization that transforms the values of the input data to have zero mean and unit variance."@en ; + skos:altLabel "apply standardization"@en ; + skos:prefLabel "apply z-score transform"@en . + + +### http://purl.org/neao/steps#ArtifactRemoval +:ArtifactRemoval rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that aims to identify and remove artifacts from the input data. Artifacts are unwanted disturbances that distorts the data. In an electrophysiology experiment, they can arise from various sources: environmental interference (e.g., electromagnetic interference from nearby equipment), physiological processes (e.g., eye movement, heart beat), and technical instrumentation issues (e.g., baseline drift of the recorded potentials)."@en ; + skos:prefLabel "artifact removal"@en . + + +### http://purl.org/neao/steps#ArtificialDataGeneration +:ArtificialDataGeneration rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty neao_base:hasOutput ; + owl:someValuesFrom neao_data:ArtificialData + ] ; + rdfs:subClassOf :DataGeneration ; + rdfs:comment "A data generation that produces artificial data. Artificial data refers to data that is generated programmatically rather than obtained from experimental recordings (e.g., neural simulations or by using specific statistical procedures). The artificial data generation procedure does not take experimentally-recorded data as input, and the generation of the output data depends only on parameters to the method employed. The isArtificial data property is defined as True for outputs of artificial data generation steps."@en ; + skos:prefLabel "artificial data generation"@en . + + +### http://purl.org/neao/steps#AutocorrelationAnalysis +:AutocorrelationAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "An analysis step used to compute a measure of autocorrelation, i.e., the correlation of the input with displaced (lagged or advanced) versions of itself. The computation produces the autocorrelation value for every lag considered."@en ; + skos:prefLabel "autocorrelation analysis"@en . + + +### http://purl.org/neao/steps#BivariateAnalysis +:BivariateAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + owl:disjointWith :MultivariateAnalysis ; + rdfs:comment "An analysis step that has only two distinct inputs considered for the computation of the output (e.g., the two time series with the local field potential recorded from two electrodes used to compute a cross-correlation)."@en ; + skos:altLabel "pairwise analysis"@en ; + skos:prefLabel "bivariate analysis"@en . + + +### http://purl.org/neao/steps#CentralTendencyStatisticalAnalysis +:CentralTendencyStatisticalAnalysis rdf:type owl:Class ; + rdfs:subClassOf :StatisticalAnalysis ; + rdfs:comment "A statistical analysis to compute a measure of central tendency, i.e., that represents the center or typical value of the input data."@en ; + skos:prefLabel "central tendency statistical analysis"@en . + + +### http://purl.org/neao/steps#CoherenceAnalysis +:CoherenceAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A spectral analysis that computes a measure of coherence between two or more inputs. Coherence is a real measure that describes the linear association of the distinct inputs (e.g., two time series) in different frequency bands. It corresponds to the absolute value of the coherency."@en ; + skos:prefLabel "coherence analysis"@en . + + +### http://purl.org/neao/steps#CoherencyAnalysis +:CoherencyAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A spectral analysis that computes a measure of coherency between two inputs. Coherency is a complex-valued measure that describes the linear association of the distinct inputs (e.g., two time series) in different frequency bands."@en ; + skos:prefLabel "coherency analysis"@en . + + +### http://purl.org/neao/steps#CompoundAnalysis +:CompoundAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty neao_base:hasSubstep ; + owl:someValuesFrom neao_base:AnalysisStep + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that is composed by two or more substeps, each performing a part of the analysis with its own data inputs/outputs and analysis parameters."@en ; + skos:prefLabel "compound analysis"@en . + + +### http://purl.org/neao/steps#ComputeASSETClusterMatrix +:ComputeASSETClusterMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis substep that computes the cluster matrix (CMAT) in the ASSET analysis, using DBSCAN with a modified distance metric. It takes the mask matrix (MMAT) as input. The cluster matrix groups the significant entries in the MMAT according to each diagonal structure that they belong to. For each significant entry in the MMAT, the CMAT will have an integer value: -1 for significant entries that do not belong to any diagonal structure, or any value greater than zero with the identification of the cluster that the entry belongs to."@en ; + skos:prefLabel "compute ASSET cluster matrix"@en . + + +### http://purl.org/neao/steps#ComputeASSETIntersectionMatrix +:ComputeASSETIntersectionMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis substep that computes the intersection matrix (IMAT). For a set of input spike trains, binned with a bin width, each entry in the IMAT corresponds to a pair of distinct bins (i.e., distinct time points in the data). The value in the entry corresponds to the number of neurons that fired in both bins corresponding to that entry. When groups of neurons fire in a sequence that repeats in time, the IMAT will show patterns that follow a diagonal direction (diagonal structure). The ASSET method aims to identify the diagonal structures by automated statistical testing and clustering procedures."@en ; + skos:prefLabel "compute ASSET intersection matrix"@en . + + +### http://purl.org/neao/steps#ComputeASSETJointProbabilityMatrix +:ComputeASSETJointProbabilityMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis substep that computes the joint probability matrix (JMAT). For every entry in the probability matrix (PMAT), the computation produces the combined probability of a fixed number of neighbors in a rectangular kernel (with fixed length and width as parameters) covering a diagonal structure. A value in the JMAT reflects how likely entries with high probability in the PMAT are to be located in the same diagonal structure."@en ; + skos:prefLabel "compute ASSET joint probability matrix"@en . + + +### http://purl.org/neao/steps#ComputeASSETMaskMatrix +:ComputeASSETMaskMatrix rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis substep that computes the mask matrix (MMAT). The parameters are the threshold values that are used to determine if the entries in the probability matrix (PMAT) and joint probability matrix (JMAT) are significant. Entry significance in either matrix is defined as a probability value greater than the provided threshold value. Significant entries in both probability and joint probability matrices are defined as 1 in the mask matrix."@en ; + skos:prefLabel "compute ASSET mask matrix"@en . + + +### http://purl.org/neao/steps#ComputeASSETProbabilityMatrixAnalytical +:ComputeASSETProbabilityMatrixAnalytical rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisProbabilityMatrixSubstep ; + owl:disjointWith :ComputeASSETProbabilityMatrixMonteCarlo ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis probability matrix substep that computes the probability matrix (PMAT) using the assumption that the input spike trains are independent and Poisson. The computation can take as input the firing rate profiles of the spike trains used for the intersection matrix (IMAT), or those will be automatically computed using convolution with a boxcar kernel of specified width. The probability distribution of the value in the intersection matrix (IMAT) is approximated by a Poisson distribution computed using LeCam's approximation. The output is a matrix with the cumulative probabilities representing the probability of having each overlap in the IMAT strictly lower than the observed overlap, under the null hypothesis of independence of the input spike trains."@en ; + skos:prefLabel "compute ASSET probability matrix (analytical method)"@en . + + +### http://purl.org/neao/steps#ComputeASSETProbabilityMatrixMonteCarlo +:ComputeASSETProbabilityMatrixMonteCarlo rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisProbabilityMatrixSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "ASSET analysis probability matrix substep that computes the probability matrix (PMAT) employing a Monte Carlo approach using surrogate data obtained from the input spike trains. Different than the analytical method of computation, the null hypothesis in this method does not incorporate the assumptions that the spike trains are Poisson. Spike train surrogates can be generated using distinct methods."@en ; + skos:prefLabel "compute ASSET probability matrix (Monte Carlo method)"@en . + + +### http://purl.org/neao/steps#ComputeASSETSequenceSynchronousEventsExtraction +:ComputeASSETSequenceSynchronousEventsExtraction rdf:type owl:Class ; + rdfs:subClassOf :ASSETAnalysisSubstep ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment "Last substep of the ASSET analysis method. Given the cluster matrix (CMAT), the identity of the neurons present in each bin of the repeated sequence in the identified diagonal structures is extracted (considering the input spike trains for the computation of the intersection matrix). The output of this substep is the final description of the ASSET pattern for each diagonal structure in the CMAT."@en ; + skos:prefLabel "compute ASSET sequence of synchronous events extraction"@en . + + +### http://purl.org/neao/steps#ComputeAngularMeanSpikePhases +:ComputeAngularMeanSpikePhases rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeFieldCouplingPurpose + ] ; + neao_base:hasBibliographicReference neao_bib:Vinck2012_53 ; + rdfs:comment "A phase analysis that computes the angle obtained from averaging the phases of an input signal at the time points where spikes occurred. For the computation, the phases are represented as vectors in the unit circle, the mean phase vector is computed, and the angle is extracted."@en ; + skos:prefLabel "compute angular mean of spike phases"@en . + + +### http://purl.org/neao/steps#ComputeAutocorrelationFunction +:ComputeAutocorrelationFunction rdf:type owl:Class ; + rdfs:subClassOf :AutocorrelationAnalysis ; + neao_base:abbreviation "compute ACF"@en ; + rdfs:comment "An autocorrelation analysis that computes the estimator for the autocorrelation function, i.e. the autocorrelation values of a time series input for a number of lags. The autocorrelation function shows temporal dependencies and repetitive patterns within the input data. The value of the autocorrelation at a specific lag shows how similar the values in the time series input are when separated by a number of time units equal to that lag. The autocorrelation value of 0 indicates no correlation. The autocorrelation varies between 1 and -1 (positive and negative correlation, respectively)."@en ; + skos:prefLabel "compute autocorrelation function"@en . + + +### http://purl.org/neao/steps#ComputeCV +:ComputeCV rdf:type owl:Class ; + rdfs:subClassOf :DispersionStatisticalAnalysis ; + neao_base:abbreviation "compute CV"@en ; + rdfs:comment "A dispersion statistical analysis that computes the coefficient of variation (CV). The CV is the ratio of the standard deviation to the mean. It is useful to compare different inputs, as the measure is unitless and indicates the relative variability in the input data."@en ; + skos:prefLabel "compute coefficient of variation"@en . + + +### http://purl.org/neao/steps#ComputeCV2 +:ComputeCV2 rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Holt1996_1806 ; + rdfs:comment "An interspike interval variability analysis that computes the CV2, a measure of the intrinsic variability of a spike train that considers adjacent interspike intervals. The CV2 is more robust against fluctuations in the firing rate than the usual approach of taking the coefficient of variation of the interspike intervals of the spike train."@en ; + skos:prefLabel "compute CV2"@en . + + +### http://purl.org/neao/steps#ComputeCVInterspikeIntervals +:ComputeCVInterspikeIntervals rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityAnalysis ; + neao_base:abbreviation "compute CV ISIs"@en ; + rdfs:comment "An interspike interval variability analysis that computes the coefficient of variation (CV) of the interspike intervals (ISIs). The CV is computed as the ratio of the standard deviation of the ISIs to their mean."@en ; + skos:prefLabel "compute coefficient of variation of the interspike intervals"@en . + + +### http://purl.org/neao/steps#ComputeCanonicalCoherence +:ComputeCanonicalCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceAnalysis ; + neao_base:abbreviation "compute caCOH"@en ; + neao_base:hasBibliographicReference neao_bib:Vidaurre2019_116009 ; + rdfs:comment "A coherence analysis that computes the canonical coherence (caCOH) according to Vidaurre et al. (2019). The computation maximizes the coherence between two inputs (e.g., distinct datasets with electroencephalogram, electromyogram or local field potential recordings). The absolute value of the coherence between the two multivariate spaces of the inputs in the frequency domain is maximized. The caCOH aims to maximize the strength of the synchronization of oscillatory signals when two multichannel datasets are present (e.g., multiple subjects). The method then finds two spatial projections maximizing the strength of synchronization."@en ; + skos:prefLabel "compute canonical coherence"@en . + + +### http://purl.org/neao/steps#ComputeCoherence +:ComputeCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceAnalysis ; + rdfs:comment "A coherence analysis that computes the coherence value between two inputs. It is obtained from the magnitude of the complex-valued cross power spectral density obtained for the two inputs normalized by their power spectral density (i.e., auto spectral density). Several frequency decomposition approaches can be used to obtain the cross and auto power spectral densities from the inputs. The computation can return the magnitude coherence (i.e., by taking the absolute value of the cross power spectral density and normalizing by the square root of the product of the two auto spectral densities) or the magnitude squared coherence (i.e., by computing the squared magnitude of the cross power spectral density and normalizing by the product of the two auto spectral densities)."@en ; + skos:prefLabel "compute coherence"@en . + + +### http://purl.org/neao/steps#ComputeCoherenceCarter +:ComputeCoherenceCarter rdf:type owl:Class ; + rdfs:subClassOf :ComputeCoherence ; + neao_base:hasBibliographicReference neao_bib:Carter1987_236 ; + rdfs:comment "A coherence analysis that computes the coherence from two inputs according to Carter (1987). The computation produces the magnitude squared coherence."@en ; + skos:prefLabel "compute coherence (Carter method)"@en . + + +### http://purl.org/neao/steps#ComputeCoherenceMultitaper +:ComputeCoherenceMultitaper rdf:type owl:Class ; + rdfs:subClassOf :ComputeCoherence ; + neao_base:hasBibliographicReference neao_bib:Thomson1982_1055 ; + rdfs:comment "A coherence analysis where the coherence value between the two inputs is computed using cross and auto power spectral densities obtained using a multitaper approach according to Thomson (1982)."@en ; + skos:prefLabel "compute coherence (multitaper method)"@en . + + +### http://purl.org/neao/steps#ComputeCoherenceRosenberg +:ComputeCoherenceRosenberg rdf:type owl:Class ; + rdfs:subClassOf :ComputeCoherence ; + neao_base:hasBibliographicReference neao_bib:Rosenberg1989_1 ; + rdfs:comment "A coherence analysis that computes the coherence from two inputs according to Rosenberg et al. (1989). The method is described for point processes (i.e., spike trains). The computation produces the magnitude squared coherence."@en ; + skos:prefLabel "compute coherence (Rosenberg method)"@en . + + +### http://purl.org/neao/steps#ComputeCoherenceWelch +:ComputeCoherenceWelch rdf:type owl:Class ; + rdfs:subClassOf :ComputeCoherence ; + neao_base:hasBibliographicReference neao_bib:Welch1967_70 ; + rdfs:comment "A coherence analysis where the coherence value between the two inputs is obtained from cross and auto power spectral densities obtained using the method described by Welch (1967). For the computation, the inputs are divided into multiple overlapping segments, and the overall cross and power spectral densities for computing the coherence are obtained from averaging the single-segment estimates."@en ; + skos:prefLabel "compute coherence (Welch method)"@en . + + +### http://purl.org/neao/steps#ComputeCoherency +:ComputeCoherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyAnalysis ; + rdfs:comment "A coherency analysis that computes the coherency for two inputs. It is computed from the complex-valued cross power spectral density obtained for the two inputs normalized by the square root of the product of their power spectral densities (i.e., auto spectral densities). For each frequency, the magnitude of the complex-valued coherency describes the strength of the association and the angle describes the phase lag between the two inputs. If the inputs contain multi-epoch data (e.g., multiple trial repetitions), the cross and auto spectral densities are averaged across epochs."@en ; + skos:altLabel "compute complex coherency"@en ; + skos:prefLabel "compute coherency"@en . + + +### http://purl.org/neao/steps#ComputeComplexityDistribution +:ComputeComplexityDistribution rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Gruen2007_96 ; + rdfs:comment "A spike train synchrony analysis that computes the complexity distribution across a set of input spike trains that typically contain the activity of different neurons. In a neuronal population, the complexity represents the total number of neurons that were spiking within a discrete time interval. For the computation, the binarized population histogram (i.e., a spike time histogram computed across spike trains, where each bin will have the count of spike trains that had at least one spike within the bin interval) is obtained using a bin size specified as a parameter. The value at each bin is the complexity. The complexity distribution is obtained by finding the frequency of each complexity value (complexity histogram) and corresponding probability density function (PDF). The complexity PDF describes the likelihood of different complexity values occurring within the neuronal population."@en ; + skos:prefLabel "compute complexity distribution"@en . + + +### http://purl.org/neao/steps#ComputeConfidenceIntervalBootstrap +:ComputeConfidenceIntervalBootstrap rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalResamplingAnalysis ; + owl:disjointWith :ComputeConfidenceIntervalJackknife ; + rdfs:comment "A confidence interval resampling analysis that computes the confidence interval using bootstrapping techniques to create many simulated samples (bootstrap samples). It involves repeatedly sampling, with replacement, from the input (observed) data. The total number of bootstrap samples is defined as a parameter. Bootstrapping makes minimal assumptions about the underlying distribution of the data, making it especially useful for small samples or when the data do not meet the assumptions of traditional parametric (non-resampling) methods."@en ; + skos:prefLabel "compute confidence interval (bootstrap resampling)"@en . + + +### http://purl.org/neao/steps#ComputeConfidenceIntervalJackknife +:ComputeConfidenceIntervalJackknife rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalResamplingAnalysis ; + rdfs:comment "A confidence interval resampling analysis that computes the confidence interval using jackknife techniques. It involves systematically leaving out one observation at a time from the input (sample) set and calculating the statistic of interest on each of these \"leave-one-out\" samples. The confidence interval is computed based on statistics obtained from those jackknife samples. Jackknife is useful for small samples or when the data do not meet the assumptions of traditional parametric (non-resampling) methods."@en ; + skos:prefLabel "compute confidence interval (jackknife resampling)"@en . + + +### http://purl.org/neao/steps#ComputeConfidenceIntervalNonResampling +:ComputeConfidenceIntervalNonResampling rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalStatisticalAnalysis ; + owl:disjointWith :ConfidenceIntervalResamplingAnalysis ; + rdfs:comment "A confidence interval statistical analysis that computes the confidence interval assuming a statistical distribution of the input data, and uses measures of central tendency and dispersion obtained from the data points in the input(s) (e.g., mean and standard error of the mean when assuming a normal distribution). The computation relies on theoretical distributions and established statistical formulas."@en ; + skos:prefLabel "compute confidence interval (non-resampling)"@en . + + +### http://purl.org/neao/steps#ComputeContinuousWaveletTransform +:ComputeContinuousWaveletTransform rdf:type owl:Class ; + rdfs:subClassOf :WaveletTransformAnalysis ; + owl:disjointWith :ComputeMorletWaveletTransform ; + neao_base:abbreviation "compute CWT"@en ; + rdfs:comment "A wavelet transform analysis that convolves the input time series with scaled and translated versions of the mother wavelet. The scale parameter can be non-dyadic (i.e., can take values that are not powers of 2). The mother wavelet used is passed as a parameter, and several types can be used (e.g., Morlet, Mexican hat, Hermitian, Meyer, Poisson). The continuous wavelet transform (CWT) is ideal for analyzing non-stationary signals, with transient behavior, rapidly changing frequencies or slowly varying changes. It is comparable to the short-time Fourier transform (STFT)."@en ; + skos:prefLabel "compute continuous wavelet transform"@en . + + +### http://purl.org/neao/steps#ComputeCorrectedImaginaryPhaseLockingValue +:ComputeCorrectedImaginaryPhaseLockingValue rdf:type owl:Class ; + rdfs:subClassOf :PhaseLockingValueAnalysis ; + neao_base:abbreviation "compute ciPLV"@en ; + neao_base:hasBibliographicReference neao_bib:Bruna2018_056011 ; + rdfs:comment "A phase locking value analysis that computes the corrected imaginary phase locking value (ciPLV), following the implementation from Bruña & Maestú (2018). It re-formulates the original phase locking value (PLV) for computational efficiency. The computation uses the imaginary part of the PLV, to make the metric insensitive to zero lag synchronizations (that can be the result of volume conduction)."@en ; + skos:prefLabel "compute corrected imaginary phase locking value"@en . + + +### http://purl.org/neao/steps#ComputeCovariance +:ComputeCovariance rdf:type owl:Class ; + rdfs:subClassOf :CovarianceAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A covariance analysis that computes the values of covariance in pairwise input data."@en ; + skos:prefLabel "compute covariance"@en . + + +### http://purl.org/neao/steps#ComputeCrossCorrelationFunction +:ComputeCrossCorrelationFunction rdf:type owl:Class ; + rdfs:subClassOf :CrossCorrelationAnalysis ; + neao_base:abbreviation "compute CCF"@en ; + rdfs:comment "A cross-correlation analysis that computes an estimate of the cross-correlation function, i.e. the cross-correlation values of two time series inputs for a number of lags. The cross-correlation function shows temporal dependencies of the first input series with respect to the second. The value of the cross-correlation at a specific lag shows how similar the values in the first input series are to values in the second input at time points separated by a number of time units equal to that lag. The cross-correlation value of 0 indicates no correlation. The cross-correlation varies between 1 and -1 (positive and negative correlation, respectively)."@en ; + skos:prefLabel "compute cross-correlation function"@en . + + +### http://purl.org/neao/steps#ComputeCrossCorrelationFunctionBiased +:ComputeCrossCorrelationFunctionBiased rdf:type owl:Class ; + rdfs:subClassOf :ComputeCrossCorrelationFunction ; + owl:disjointWith :ComputeCrossCorrelationFunctionUnbiased ; + neao_base:abbreviation "compute CCF (biased)"@en ; + rdfs:comment "An analysis step that computes the biased estimator for the cross-correlation function. The biased estimator produces cross-correlation values that deviate from the true cross-correlation."@en ; + skos:prefLabel "compute cross-correlation function (biased)"@en . + + +### http://purl.org/neao/steps#ComputeCrossCorrelationFunctionUnbiased +:ComputeCrossCorrelationFunctionUnbiased rdf:type owl:Class ; + rdfs:subClassOf :ComputeCrossCorrelationFunction ; + neao_base:abbreviation "compute CCF (unbiased)"@en ; + neao_base:hasBibliographicReference neao_bib:Stoica2005 ; + rdfs:comment "An analysis step that computes the unbiased estimator for the cross-correlation function, using the formula in Stoica & Moses (2005). The unbiased estimation uses a correction for the bias due to zero-padding in the computation, applied to the normalization coefficient. Therefore, the resultant cross-correlation values are closer to the true cross-correlation."@en ; + skos:prefLabel "compute cross-correlation function (unbiased)"@en . + + +### http://purl.org/neao/steps#ComputeCrossPowerSpectralDensityMorletWavelet +:ComputeCrossPowerSpectralDensityMorletWavelet rdf:type owl:Class ; + rdfs:subClassOf :CrossPowerSpectralDensityAnalysis ; + rdfs:comment "A cross power spectral density analysis that uses the Morlet wavelet transform to obtain the frequency information of the two inputs used to compute the cross power spectral density."@en ; + skos:prefLabel "compute cross power spectral density (Morlet wavelet method)"@en . + + +### http://purl.org/neao/steps#ComputeCrossPowerSpectralDensityMultitaper +:ComputeCrossPowerSpectralDensityMultitaper rdf:type owl:Class ; + rdfs:subClassOf :CrossPowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute CPSD (multitaper method)"@en ; + neao_base:hasBibliographicReference neao_bib:Thomson1982_1055 ; + rdfs:comment "A cross power spectral density (CPSD) analysis that uses a multitaper approach to compute the CPSD, according to Thomson (1982). The multitaper method uses discrete prolate spheroidal functions (DPSS, also known as Slepian sequences) as tapers applied to the inputs. For the computation, a CPSD using the periodogram method is obtained for each pair of tapered signals. The DPSS functions are orthogonal, and therefore applying multiple DPSS tapers result in independent estimates of the CPSD. The final CPSD is obtained by averaging the CPSDs across all tapers. The multitaper method reduces variance and bias in the CPSD, and has a high frequency resolution. However, it is computationally expensive. The number of tapers used is passed as parameter, or estimated from the desired resolution."@en ; + skos:prefLabel "compute cross power spectral density (multitaper method)"@en . + + +### http://purl.org/neao/steps#ComputeCrossPowerSpectralDensityPeriodogram +:ComputeCrossPowerSpectralDensityPeriodogram rdf:type owl:Class ; + rdfs:subClassOf :CrossPowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute CPSD (periodogram method)"@en ; + rdfs:comment "A cross power spectral density (CPSD) analysis that uses the periodogram method. For the computation, the discrete Fourier transform of each input is obtained and their cross-power spectrum is obtained. A window function can be applied to the inputs before the Fourier transform, to reduce spectral leakage. The final CPSD is obtained by normalizing the cross-power spectrum to the unit frequency using the equivalent noise bandwidth (a factor that depends on the coefficients of the window function and the sampling rate). If no window function is used or a window that does not attenuate the signal is used (e.g., Boxcar or rectangular window) the equivalent noise bandwidth is equal to the frequency resolution."@en ; + skos:altLabel "compute cross-periodogram"@en ; + skos:prefLabel "compute cross power spectral density (periodogram method)"@en . + + +### http://purl.org/neao/steps#ComputeCrossPowerSpectralDensityWelch +:ComputeCrossPowerSpectralDensityWelch rdf:type owl:Class ; + rdfs:subClassOf :CrossPowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute CPSD (Welch method)"@en ; + neao_base:hasBibliographicReference neao_bib:Welch1967_70 ; + rdfs:comment "A cross power spectral density (CPSD) analysis that uses the method defined by Welch (1967). For the computation, the two inputs are divided into several overlapping segments (length and overlap passed as parameters, or computed for the desired frequency resolution based on the input length and sampling frequency). A window function (e.g., Hann) is applied to each segment, and the cross power spectral density using the periodogram method is computed. The final CPSD is obtained by averaging all the periodograms with the individual CPSDs."@en ; + skos:prefLabel "compute cross power spectral density (Welch method)"@en . + + +### http://purl.org/neao/steps#ComputeCrossSpectrogramShortTimeFourierTransform +:ComputeCrossSpectrogramShortTimeFourierTransform rdf:type owl:Class ; + rdfs:subClassOf :SpectrogramAnalysis ; + neao_base:abbreviation "compute cross-spectrogram (STFT method)"@en ; + rdfs:comment "A spectrogram analysis that computes a cross-spectrogram using the short-time Fourier transform (STFT). The cross-spectrogram is the time-resolved description of the power of a pair of distinct inputs across the different frequency components. This can be used to investigate how common activity between the two inputs is distributed across the frequency components, and how it varies over time."@en ; + skos:prefLabel "compute cross-spectrogram (short-time Fourier transform method)"@en . + + +### http://purl.org/neao/steps#ComputeCubicAnalysis +:ComputeCubicAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Staude2010_327 ; + rdfs:comment "A spike train synchrony analysis that uses the Cumulant Based Inference of higher-order Correlation (CuBIC) test described in Staude et al. (2010). CuBIC is a statistical method to detect the presence of higher order correlations in parallel spike trains from a neuronal population (i.e., correlations among three or more neurons). It is based on the analysis of the cumulants of the population spike count. The test takes a population histogram as input data (i.e., a spike train time histogram computed across spike trains with the activity of distinct neurons). A null hypothesis that the third cumulant of the data is less than or equal to the maximized third cumulant for a correlation order is iteratively tested (with increasing orders of correlation). The output is the minimum correlation order necessary to explain the value of the third cumulant calculated from the population spike count, together with the p-values of the hypothesis tests performed."@en ; + skos:prefLabel "compute CuBIC analysis"@en . + + +### http://purl.org/neao/steps#ComputeCurrentSourceDensityICSD +:ComputeCurrentSourceDensityICSD rdf:type owl:Class ; + rdfs:subClassOf :CurrentSourceDensityAnalysis ; + neao_base:abbreviation "compute iCSD"@en ; + neao_base:hasBibliographicReference neao_bib:Leski2007_207 , + neao_bib:Leski2011_401 , + neao_bib:Pettersen2006_116 ; + rdfs:comment """A current source density (CSD) analysis that uses the inverse current source density (iCSD) estimation method described by Pettersen et al. (2006). The iCSD is based on the inversion of the electrostatic forward solution and can be applied to data obtained from electrodes with multiple configurations. The method can handle cases with spatially confined cortical activity and spatially varying extracellular conductivity. + +Three options for CSD estimation using the iCSD exist. The CSD is assumed to have cylindrical symmetry and follows one of three possible assumptions: +1. is localized in infinitely thin discs; +2. is step-wise constant; +3. is continuous and smoothly varying (using cubic splines) in the vertical direction."""@en ; + skos:prefLabel "compute current source density (inverse method)"@en . + + +### http://purl.org/neao/steps#ComputeCurrentSourceDensityKCSD +:ComputeCurrentSourceDensityKCSD rdf:type owl:Class ; + rdfs:subClassOf :CurrentSourceDensityAnalysis ; + neao_base:abbreviation "compute kCSD"@en ; + neao_base:hasBibliographicReference neao_bib:Potworowski2012_541 ; + rdfs:comment "A current source density (CSD) analysis that uses kernel methods to compute the CSD (kCSD), described by Potworowski et al. (2012). kCSD is non parametric and can estimate the CSD using signals recorded from arbitrarily distributed electrodes, as the assumption of regular electrode placement is not necessary. The method can handle 1D, 2D or 3D electrode configurations. The kCSD can also estimate CSD at any location, as it is not limited to the electrode positions, and uses cross-validation to ensure no overfitting."@en ; + skos:prefLabel "compute current source density (kernel method)"@en . + + +### http://purl.org/neao/steps#ComputeCurrentSourceDensityStandard +:ComputeCurrentSourceDensityStandard rdf:type owl:Class ; + rdfs:subClassOf :CurrentSourceDensityAnalysis ; + neao_base:abbreviation "compute CSD"@en ; + neao_base:hasBibliographicReference neao_bib:Freeman1975_369 , + neao_bib:Vaknin1988_131 ; + rdfs:comment "A current source density (CSD) analysis that uses a double spatial derivative of the recorded extracellular potentials to compute the CSD. The original method by Freeman & Nicholson (1975) assumes homogeneous cortical in-plane activity, constant extracellular electrical conductivity and equidistant electrode contacts, and can only predict the CSD at interior electrode positions. Vaknin et al. (1988) suggested a procedure to obtain the CSD for the first and last electrodes by copying the outmost recordings, therefore extending the grid beyond the electrode contacts. This is based on the assumption that the potential varies negligibly above the first and below the last electrode."@en ; + skos:prefLabel "compute current source density (standard method)"@en . + + +### http://purl.org/neao/steps#ComputeDebiasedSquaredWeightedPhaseLagIndex +:ComputeDebiasedSquaredWeightedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "compute debiased squared WPLI"@en ; + neao_base:hasBibliographicReference neao_bib:Vinck2011_1548 ; + rdfs:comment "A phase lag index (PLI) analysis that computes the debiased squared weighted PLI (WPLI) following Vinck et al. (2011). The direct PLI estimator is positively biased, especially when the sample sizes (i.e., number of trials) are small. The debiased squared WPLI is computed by computing the imaginary components of the cross spectral densities, computing the average imaginary component of the cross spectral densities, and normalizing by the computed average over the magnitudes of the imaginary component of the cross spectral densities."@en ; + skos:prefLabel "compute debiased squared weighted phase lag index"@en . + + +### http://purl.org/neao/steps#ComputeDirectedPhaseLagIndex +:ComputeDirectedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute DPLI"@en ; + neao_base:hasBibliographicReference neao_bib:Stam2012_1415 ; + rdfs:comment "A phase lag index (PLI) analysis that computes the directed PLI (DPLI) according to Stam & van Straaten (2012). The DPLI uses the Heaviside step function on the imaginary part of the cross power spectral density, and provides the ability to discriminate whether the first time series is leading or lagging the second. The DPLI ranges between 0 and 1. A DPLI value of 0.5 means that the first time series leads and lags the second time series equally often. A DPLI value greater than 0.5 means that the first time series leads the second more often than it lags. A value of 1 means that the first time series always leads. On the contrary, a DPLI value smaller than 0.5 means that the first time series lags the second more often than it leads. A DPLI value of zero means that the first time series always lags. The PLI can be computed from the DPLI."@en ; + skos:prefLabel "compute directed phase lag index"@en . + + +### http://purl.org/neao/steps#ComputeDirectedTransferFunction +:ComputeDirectedTransferFunction rdf:type owl:Class ; + rdfs:subClassOf :FunctionalConnectivityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isMultivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute DTF"@en ; + neao_base:hasBibliographicReference neao_bib:Kaminski1991_203 ; + rdfs:comment "A functional connectivity analysis that computes the directed transfer function (DTF) according to Kaminski & Blinowska (1991). DTF can estimate the direction and frequency content of the brain activity flow. The DTF measure is obtained from the spectral transfer matrix computed from multivariate time series input data. For the DTF computation, the spectral transfer matrix is obtained from a multivariate autoregressive model. The DTF estimate is obtained by using a normalization factor computed by the sum along the rows of the spectral transfer matrix. The DTF can have values in the range from 0 to 1."@en ; + skos:prefLabel "compute directed transfer function"@en . + + +### http://purl.org/neao/steps#ComputeEventRelatedPotential +:ComputeEventRelatedPotential rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:abbreviation "compute ERP"@en ; + rdfs:comment "An analysis step that computes the event-related potential (ERP). The ERP is the neural activity (e.g., local field potential or electroencephalogram voltages) around a presented stimulus or spontaneous behavioral event. Usually, the event is presented/occurs repeatedly across multiple trials, obtaining multiple event-related potential waveforms that can be averaged to cancel the noise."@en ; + skos:prefLabel "compute event-related potential"@en . + + +### http://purl.org/neao/steps#ComputeEventTriggeredAverage +:ComputeEventTriggeredAverage rdf:type owl:Class ; + rdfs:subClassOf :TriggeredAverageAnalysis ; + rdfs:comment "A triggered average analysis that uses an event of interest such as an external stimulus (e.g., electrical, visual, auditory) or a spontaneous behavior (e.g., eye blink) as a trigger to average a signal. The output of the method will provide the average value of the signal around the time where each event occurred (event-triggered average)."@en ; + skos:prefLabel "compute event-triggered average"@en . + + +### http://purl.org/neao/steps#ComputeFrequencyDomainConditionalGrangerCausality +:ComputeFrequencyDomainConditionalGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :ConditionalGrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + owl:disjointWith :ComputeTimeDomainConditionalGrangerCausality ; + neao_base:hasBibliographicReference neao_bib:Dhamala2008_354 , + neao_bib:Ding2006_0608035 ; + rdfs:comment "A conditional Granger causality (GC) analysis that computes the GC measures in the frequency domain."@en ; + skos:prefLabel "compute frequency domain conditional Granger causality"@en . + + +### http://purl.org/neao/steps#ComputeFrequencyDomainPairwiseGrangerCausalityBrovelli +:ComputeFrequencyDomainPairwiseGrangerCausalityBrovelli rdf:type owl:Class ; + rdfs:subClassOf :FrequencyDomainPairwiseGrangerCausalityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Brovelli2004_9849 ; + rdfs:comment "A frequency domain pairwise Granger causality (GC) analysis that uses the parametric approach according to Brovelli et al. (2004). It uses an MVAR (multivariate autoregressive model) to obtain the coefficients used for the computation of the spectral transfer matrix needed for GC estimation according to the frequency domain GC formulation by Geweke (1982)."@en ; + skos:prefLabel "compute frequency domain pairwise Granger causality (Brovelli method)"@en . + + +### http://purl.org/neao/steps#ComputeFrequencyDomainPairwiseGrangerCausalityDhamala +:ComputeFrequencyDomainPairwiseGrangerCausalityDhamala rdf:type owl:Class ; + rdfs:subClassOf :FrequencyDomainPairwiseGrangerCausalityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Dhamala2008_354 , + neao_bib:Wilson1972_420 ; + rdfs:comment "A frequency domain pairwise Granger causality (GC) analysis that uses the non-parametric approach according to Dhamala et al. (2008). It is based on Fourier and wavelet transforms to obtain the spectral density matrix and the algorithm from Wilson (1972) for its factorization."@en ; + skos:prefLabel "compute frequency domain pairwise Granger causality (Dhamala method)"@en . + + +### http://purl.org/neao/steps#ComputeFrequencyDomainPairwiseGrangerCausalityHafner +:ComputeFrequencyDomainPairwiseGrangerCausalityHafner rdf:type owl:Class ; + rdfs:subClassOf :FrequencyDomainPairwiseGrangerCausalityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Hafner2008_215 ; + rdfs:comment "A frequency domain pairwise Granger causality (GC) analysis that uses the parametric approach according to Hafner & Herwartz (2008). It uses a multivariate GARCH (generalized autoregressive conditional heteroskedasticity) model and constructs a Wald test on noncausality in variance. This is an alternative to methods based on the residuals of estimated univariate models. The Wald test has superior power properties."@en ; + skos:prefLabel "compute frequency domain pairwise Granger causality (Hafner method)"@en . + + +### http://purl.org/neao/steps#ComputeFrequencyDomainPairwiseGrangerCausalityWen +:ComputeFrequencyDomainPairwiseGrangerCausalityWen rdf:type owl:Class ; + rdfs:subClassOf :FrequencyDomainPairwiseGrangerCausalityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Wen2013_20110610 ; + rdfs:comment "A frequency domain pairwise Granger causality (GC) analysis that uses the non-parametric approach according to Wen et al. (2013). It is a multivariate framework for estimating GC based on spectral density matrix factorization. The approach requires only a single estimation of the spectral density matrix for the entire dataset (e.g., multiple time series inputs). GC for the subsets (i.e., pairs of inputs) can then be calculated by factorizing the relevant submatrix of this overall spectral density matrix."@en ; + skos:prefLabel "compute frequency domain pairwise Granger causality (Wen method)"@en . + + +### http://purl.org/neao/steps#ComputeISIDistance +:ComputeISIDistance rdf:type owl:Class ; + rdfs:subClassOf :TimeScaleIndependentSpikeTrainDistanceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Kreuz2007_151 ; + rdfs:comment "A time-scale independent spike train distance analysis that computes the ISI-distance, described in Kreuz et al. (2007). For the computation, the discrete sequence of spike times is transformed into a continuous temporal profile with one value per sample point. The values at each time point are derived from the interspike intervals. The distance is then obtained as the temporal average of the time profile. ISI-distance is well-designed to describe similarities in the firing rate profile of the input spike trains, but it is not optimal to capture neuronal synchrony."@en ; + skos:prefLabel "compute ISI-distance"@en . + + +### http://purl.org/neao/steps#ComputeImaginaryCoherency +:ComputeImaginaryCoherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Nolte2004_2292 ; + rdfs:comment "A coherency analysis that computes the imaginary part of the coherency for two inputs according to Nolte et al. (2004). For the computation, the imaginary part of the complex-valued cross power spectral density obtained for the two inputs is normalized by the square root of the product of their power spectral densities (i.e., auto spectral densities). If the inputs contain multi-epoch data (e.g., multiple trial repetitions), the cross and auto spectral densities are averaged across epochs. The imaginary part of the coherency is less affected by volume conduction than the (complex) coherency."@en ; + skos:prefLabel "compute imaginary coherency"@en . + + +### http://purl.org/neao/steps#ComputeInstantaneousFiringRateInterspikeInterval +:ComputeInstantaneousFiringRateInterspikeInterval rdf:type owl:Class ; + rdfs:subClassOf :InstantaneousFiringRateAnalysis ; + rdfs:comment "An instantaneous firing rate analysis that computes the instantaneous firing rate by using the reciprocal of the interspike intervals."@en ; + skos:prefLabel "compute instantaneous firing rate (interspike interval method)"@en . + + +### http://purl.org/neao/steps#ComputeInstantaneousFiringRateKernelDensityEstimation +:ComputeInstantaneousFiringRateKernelDensityEstimation rdf:type owl:Class ; + rdfs:subClassOf :InstantaneousFiringRateAnalysis ; + rdfs:comment "An instantaneous firing rate analysis that computes the instantaneous firing rate by convolution of spike times with a kernel function. The output of the computation is a weighted average of the spikes around the kernel."@en ; + skos:altLabel "compute instantaneous firing rate (kernel smoothing method)"@en ; + skos:prefLabel "compute instantaneous firing rate (kernel density estimation method)"@en . + + +### http://purl.org/neao/steps#ComputeInstantaneousFiringRateLocalRegression +:ComputeInstantaneousFiringRateLocalRegression rdf:type owl:Class ; + rdfs:subClassOf :InstantaneousFiringRateAnalysis ; + neao_base:hasBibliographicReference neao_bib:Bokil2010_146 ; + rdfs:comment "An instantaneous firing rate analysis that computes the instantaneous firing rate using local regression methods. The estimation procedure approximates the log of the firing rate using a low-order polynomial within a moving window (local neighborhood)."@en ; + skos:prefLabel "compute instantaneous firing rate (local regression method)"@en . + + +### http://purl.org/neao/steps#ComputeInterquartileRange +:ComputeInterquartileRange rdf:type owl:Class ; + rdfs:subClassOf :DispersionStatisticalAnalysis ; + neao_base:abbreviation "compute IQR"@en ; + rdfs:comment "A dispersion statistical analysis that computes the interquartile range (IQR). The IQR is the difference between the 75th and 25th percentiles (i.e., the range within which the central 50% of the data points lie)."@en ; + skos:prefLabel "compute interquartile range"@en . + + +### http://purl.org/neao/steps#ComputeInterspikeIntervalHistogram +:ComputeInterspikeIntervalHistogram rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalAnalysis ; + neao_base:abbreviation "compute ISIH"@en ; + rdfs:comment "An interspike interval analysis that computes the histogram of interspike intervals. For the computation, a time interval with fixed duration starting from zero is discretized into smaller intervals (bins). The count of input interspike intervals whose values fall into each bin is obtained. Therefore, the output contains a representation of the distribution of the interspike intervals in the input."@en ; + skos:prefLabel "compute interspike interval histogram"@en . + + +### http://purl.org/neao/steps#ComputeInterspikeIntervals +:ComputeInterspikeIntervals rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalAnalysis ; + neao_base:abbreviation "compute ISIs"@en ; + rdfs:comment "An interspike interval analysis that computes the intervals between successive spikes in a spike train (interspike intervals; ISIs)."@en ; + skos:prefLabel "compute interspike intervals"@en . + + +### http://purl.org/neao/steps#ComputeJointPeristimulusTimeHistogram +:ComputeJointPeristimulusTimeHistogram rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute JPSTH"@en ; + neao_base:hasBibliographicReference neao_bib:Aertsen1987_1 , + neao_bib:Brown2004_456 ; + rdfs:comment """A spike train synchrony analysis that computes the joint peristimulus time histogram (JPSTH) from trial-by-trial spike train inputs obtained from two different neurons, after the repeated presentation of a stimulus. The JPSTH provides a representation of the timing relationship between the firing of the two neurons in response to the stimulus. It combines the peristimulus time histograms (PSTHs) of each neuron to illustrate how their firing rates co-vary over time relative to the stimulus event. This helps in understanding the temporal correlation between the neurons. + +The computation can produce three outputs: + +* the JPSTH matrix (i.e., a matrix whose bins contain the counts of coincidences in the firing of the two neurons); +* the peristimulus coincidence histogram (i.e., a histogram obtained from a cross-section along the main diagonal of the JPSTH matrix); +* the cross-correlation histogram computed by summing the bins along the main and each paradiagonal of the JPSTH matrix (after normalizing by the bin length, as the paradiagonals in the JPSTH matrix are of different lengths)."""@en ; + skos:prefLabel "compute joint peristimulus time histogram"@en . + + +### http://purl.org/neao/steps#ComputeLV +:ComputeLV rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityAnalysis ; + neao_base:abbreviation "compute LV"@en ; + neao_base:hasBibliographicReference neao_bib:Shinomoto2003_2823 ; + rdfs:comment "An interspike interval variability analysis that computes the local variation (LV) of the interspike intervals. LV reflects the stepwise variability of a sequence of spikes, and is able to extract the spiking characteristics of individual neurons even in the presence of external modulations of the firing rate."@en ; + skos:prefLabel "compute local variation"@en . + + +### http://purl.org/neao/steps#ComputeLVR +:ComputeLVR rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalVariabilityAnalysis ; + neao_base:abbreviation "compute LvR"@en ; + neao_base:hasBibliographicReference neao_bib:Shinomoto2009_e1000433 ; + rdfs:comment "An interspike interval variability analysis that computes the revised local variation (LvR) of interspike intervals. Compared to the original local variation (LV) measure, LvR has better invariance to fluctuations in the firing rate fluctuations. This is achieved by using a refractoriness constant in the computation of the measure."@en ; + skos:prefLabel "compute revised local variation"@en . + + +### http://purl.org/neao/steps#ComputeMaximizedImaginaryCoherency +:ComputeMaximizedImaginaryCoherency rdf:type owl:Class ; + rdfs:subClassOf :CoherencyAnalysis ; + neao_base:abbreviation "compute MIC"@en ; + neao_base:hasBibliographicReference neao_bib:Ewald2012_476 ; + rdfs:comment "A coherency analysis that computes the maximized imaginary coherency (MIC) according to Ewald et al. (2012). The computation uses an eigenvalue-based optimization to find weight vectors that maximize the imaginary part of coherency computed between virtual channels derived from the input data. The weights are optimized for each frequency component. After the weights are obtained, the final MIC measure is obtained for each frequency."@en ; + skos:prefLabel "compute maximized imaginary coherency"@en . + + +### http://purl.org/neao/steps#ComputeMean +:ComputeMean rdf:type owl:Class ; + rdfs:subClassOf :CentralTendencyStatisticalAnalysis ; + rdfs:comment "A central tendency statistical analysis that computes the mean of the input data. The mean is the arithmetic average of all data points."@en ; + skos:prefLabel "compute mean"@en . + + +### http://purl.org/neao/steps#ComputeMeanFiringRate +:ComputeMeanFiringRate rdf:type owl:Class ; + rdfs:subClassOf :FiringRateAnalysis ; + neao_base:abbreviation "compute MFR"@en ; + rdfs:comment "A firing rate analysis that computes the mean firing rate, defined as the number of spikes in a time interval divided by the duration of the interval. The mean firing rate is the temporal average of the neuronal activity over that interval."@en ; + skos:prefLabel "compute mean firing rate"@en . + + +### http://purl.org/neao/steps#ComputeMeanPhaseVector +:ComputeMeanPhaseVector rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis ; + rdfs:comment "A phase analysis that computes the mean among two or more input phases. For the computation, the input phases are represented as vectors in the unit circle, and the mean phase vector is computed. The analysis can return the mean phase vector (i.e., angle and length), the vector angle, or the vector length."@en ; + skos:prefLabel "compute mean phase vector"@en . + + +### http://purl.org/neao/steps#ComputeMeanVectorLengthCanolty +:ComputeMeanVectorLengthCanolty rdf:type owl:Class ; + rdfs:subClassOf :MeanVectorLengthAnalysis ; + owl:disjointWith :ComputeMeanVectorLengthOzkurt ; + neao_base:abbreviation "compute MVL (Canolty method)"@en ; + neao_base:hasBibliographicReference neao_bib:Canolty2006_1626 ; + rdfs:comment "A mean vector length (MVL) analysis that computes the mean vector length as described in Canolty et al. (2006). For the computation, phase is extracted from the low-frequency analytic signal, and amplitude is extracted from the high-frequency analytic signal. The phase angle and magnitude is used to define a complex-valued time series, and each complex value is a vector in the polar plane. Averaging all vectors yields a mean vector whose length indicates the coupling strength and whose direction indicates the phase where amplitude is strongest. Without coupling, the vectors cancel out, resulting in a short mean vector without meaningful phase direction. If phase-amplitude coupling exists, the magnitude of a subset of vectors is especially high at a specific phase or narrow phase range."@en ; + skos:prefLabel "compute mean vector length (Canolty method)"@en . + + +### http://purl.org/neao/steps#ComputeMeanVectorLengthOzkurt +:ComputeMeanVectorLengthOzkurt rdf:type owl:Class ; + rdfs:subClassOf :MeanVectorLengthAnalysis ; + neao_base:abbreviation "compute MVL (Özkurt method)"@en ; + neao_base:hasBibliographicReference neao_bib:Ozkurt2011_438 ; + rdfs:comment "A mean vector length (MVL) analysis that computes the MVL as described in Özkurt & Schnitzler (2011). The original MVL (Canolty et al., 2006) may be affected by factors in the input data (e.g., amplitude outliers or non-uniform distribution of phase angles). This computation estimates a direct MVL that is amplitude-normalized to obtain values in the 0 to 1 range, and that takes care of possible amplitude differences in the raw data."@en ; + skos:prefLabel "compute mean vector length (Özkurt method)"@en . + + +### http://purl.org/neao/steps#ComputeMedian +:ComputeMedian rdf:type owl:Class ; + rdfs:subClassOf :CentralTendencyStatisticalAnalysis ; + rdfs:comment "A central tendency statistical analysis that computes the median of the input data. The median is the middle value when data points are arranged in ascending order (i.e., it divides the data points in two equal halves, with 50% of the data points below it and 50% above it). If there is an even number of data points, the median is the average of the two middle values."@en ; + skos:prefLabel "compute median"@en . + + +### http://purl.org/neao/steps#ComputeModulationIndex +:ComputeModulationIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseAmplitudeCouplingAnalysis ; + neao_base:abbreviation "compute MI"@en ; + neao_base:hasBibliographicReference neao_bib:Tort2010_1195 ; + rdfs:comment "A phase-amplitude coupling (PAC) analysis that computes the modulation index (MI) as described in Tort et al. (2010). For the computation, the Hilbert transform is used to obtain the instantaneous phase from the input time series with the low-frequency oscillation, and the instantaneous amplitude from the input time series with the high-frequency oscillation. The phase of the low-frequency oscillation is discretized into bins and the amplitude of the high-frequency oscillation is averaged within each bin to create a distribution. This distribution is then compared to a uniform distribution using the Kullback-Leibler divergence, normalized by the maximum possible divergence, resulting in the MI."@en ; + skos:prefLabel "compute modulation index"@en . + + +### http://purl.org/neao/steps#ComputeMorletWaveletTransform +:ComputeMorletWaveletTransform rdf:type owl:Class ; + rdfs:subClassOf :WaveletTransformAnalysis ; + rdfs:comment "A wavelet transform analysis based on the complex-valued Morlet wavelet. The transform can be performed either in the time domain (by convolution) or in the frequency domain (by multiplication)."@en ; + skos:prefLabel "compute Morlet wavelet transform"@en . + + +### http://purl.org/neao/steps#ComputeMorletWaveletTransformLeVanQuyen +:ComputeMorletWaveletTransformLeVanQuyen rdf:type owl:Class ; + rdfs:subClassOf :ComputeMorletWaveletTransform ; + owl:disjointWith :ComputeMorletWaveletTransformTallonBaudry ; + neao_base:hasBibliographicReference neao_bib:LeVanQuyen2001_83 ; + rdfs:comment "A wavelet transform analysis using the Morlet wavelet where the parametrization of the mother wavelet is done according to Le Van Quyen et al. (2001). The size of the mother wavelet is determined in number of cycles to control the frequency and temporal resolutions (approximate number of oscillation cycles within a wavelet)."@en ; + skos:prefLabel "compute Morlet wavelet transform (Le Van Quyen method)"@en . + + +### http://purl.org/neao/steps#ComputeMorletWaveletTransformTallonBaudry +:ComputeMorletWaveletTransformTallonBaudry rdf:type owl:Class ; + rdfs:subClassOf :ComputeMorletWaveletTransform ; + neao_base:hasBibliographicReference neao_bib:TallonBaudry1997_722 ; + rdfs:comment "A wavelet transform analysis using the Morlet wavelet based on the methods described in Tallon-Baudry et al. (1997). The ratio of the central frequency to the spectral bandwidth is 7, with central frequencies ranging from 20 to 100 Hz in 1 Hz steps. This resulted in varying time/frequency resolution across the spectrum: time resolution increases with frequency, while frequency resolution decreases."@en ; + skos:prefLabel "compute Morlet wavelet transform (Tallon-Baudry method)"@en . + + +### http://purl.org/neao/steps#ComputeMultivariateInteractionMeasure +:ComputeMultivariateInteractionMeasure rdf:type owl:Class ; + rdfs:subClassOf :FunctionalConnectivityAnalysis ; + neao_base:abbreviation "compute MIM"@en ; + neao_base:hasBibliographicReference neao_bib:Ewald2012_476 ; + rdfs:comment "A functional connectivity analysis that computes the multivariate interaction measure (MIM) as defined by Ewald et al. (2012). MIM is constructed from the maximization of imaginary coherency."@en ; + skos:prefLabel "compute multivariate interaction measure"@en . + + +### http://purl.org/neao/steps#ComputeMutualInformation +:ComputeMutualInformation rdf:type owl:Class ; + rdfs:subClassOf :FunctionalConnectivityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute MI"@en ; + neao_base:hasBibliographicReference neao_bib:Cover2012 ; + rdfs:comment "A functional connectivity analysis that computes a mutual information (MI) measure. MI is based on Shannon information theory, and quantifies the amount of information from one input that is obtained from another input. Therefore, it can be used to determine how the neuronal activity provides information about a variable (e.g., behavioral stimulus) or how the information flows between different brain regions or neurons. The MI is measured in bits."@en ; + skos:prefLabel "compute mutual information"@en . + + +### http://purl.org/neao/steps#ComputeNeuronalPopulationVector +:ComputeNeuronalPopulationVector rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Georgopoulos1983_327 ; + rdfs:comment "An analysis step that computes the neuronal population vector, used to describe the collective activity of a group of neurons. The neuronal population vector is obtained taking as inputs the multiple responses of a neuronal population in the context of distinct values of a behavioral measure (e.g., tuning curves showing the response of each individual neuron to different arm movement directions in the 2-D space). The analysis step obtains a weighted vectorial sum of the neural activities, which will result in an estimate of the behavioral measure considering the collective activity of the population (e.g., the movement direction given the neuronal activity)."@en ; + skos:altLabel "compute population vector"@en ; + skos:prefLabel "compute neuronal population vector"@en . + + +### http://purl.org/neao/steps#ComputeNoiseCorrelations +:ComputeNoiseCorrelations rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationAnalysis ; + neao_base:abbreviation "compute NC"@en ; + neao_base:hasBibliographicReference neao_bib:Cohen2011_811 ; + rdfs:comment "A spike train correlation analysis that computes the noise correlations (NC) between two input spike trains. The NC is the Pearson's correlation coefficient of spike count responses to repeated presentations of identical stimuli, under the same behavioral conditions. The spike counts are typically measured over the time scale of a stimulus presentation or a behavioral trial, which range from a few hundred milliseconds to several seconds. NC assesses whether neurons exhibit trial-by-trial fluctuations in firing rates that are not influenced by varying sensory or behavioral conditions."@en ; + skos:prefLabel "compute noise correlations"@en . + + +### http://purl.org/neao/steps#ComputeNoiseCovariance +:ComputeNoiseCovariance rdf:type owl:Class ; + rdfs:subClassOf :CovarianceAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A covariance analysis that computes the noise covariance, i.e., how much two noise signals vary together. The noise data inputs can be non-subject recordings (e.g., recordings from the empty experimental room) or are obtained from periods without stimulation or meaningful experimental manipulations (e.g., prestimulus intervals). These reflect random variations or disturbances that are not part of the actual signal or data of interest."@en ; + skos:prefLabel "compute noise covariance"@en . + + +### http://purl.org/neao/steps#ComputeOptimalBinSize +:ComputeOptimalBinSize rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Scott1979_605 ; + rdfs:comment "An analysis step that finds the optimal bin size considering the input data when discretizing data into smaller intervals (bins). The computation uses the formula from Scott (1979)."@en ; + skos:prefLabel "compute optimal bin size"@en . + + +### http://purl.org/neao/steps#ComputeOptimalKernelBandwidth +:ComputeOptimalKernelBandwidth rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Shimazaki2010_171 ; + rdfs:comment "An analysis step that computes the optimal fixed bandwidth (width) for a Gaussian kernel used for the estimation of the firing rate using kernel density estimation. The analysis step uses the input spike train for which the firing rate will be computed, and follows the implementation by Shimazaki & Shinomoto (2010)."@en ; + skos:prefLabel "compute optimal kernel bandwidth"@en . + + +### http://purl.org/neao/steps#ComputeOrthogonalizedPowerEnvelopeCorrelation +:ComputeOrthogonalizedPowerEnvelopeCorrelation rdf:type owl:Class ; + rdfs:subClassOf :CorrelationAnalysis ; + neao_base:hasBibliographicReference neao_bib:Hipp2012_884 ; + rdfs:comment "A functional connectivity analysis that computes the orthogonalized power envelope correlation, according to Hipp et al. (2012). This method relies on correlations between the instantaneous amplitudes of cross-region input signals (power envelopes). The instantaneous amplitudes of the two input time series are orthogonalized aiming to remove spurious correlations of signal power (e.g., due to limited spatial resolution of electrophysiological measures)."@en ; + skos:prefLabel "compute orthogonalized power envelope correlation"@en . + + +### http://purl.org/neao/steps#ComputePairwisePhaseConsistency +:ComputePairwisePhaseConsistency rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FieldFieldCouplingPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeFieldCouplingPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute PPC"@en ; + neao_base:hasBibliographicReference neao_bib:Vinck2010_112 ; + rdfs:comment "A phase analysis that computes the pairwise phase consistency (PPC) measure according to Vinck et al. (2010). The PPC quantifies the distribution of phase differences across the inputs, but is less biased by the number of observations in comparison to the phase locking value (PLV). For the computation, the phase difference (angular distance) is obtained for all pairs of observations in the input (that can be represented as vectors in the unit circle, where the angle is the relative phase). The cosine of the angular distance (an estimate of the dot product of the vectors corresponding to each element in a pair) is computed for every pair, and the PPC estimate is obtained from the average of all pairwise dot products. With phase synchronization, the distribution of the pairwise dot products is centered around an average value, while without synchronization it will be distributed across the unit circle. The PPC provides an unbiased estimate of the squared PLV."@en ; + skos:prefLabel "compute pairwise phase consistency"@en . + + +### http://purl.org/neao/steps#ComputePartialCoherence +:ComputePartialCoherence rdf:type owl:Class ; + rdfs:subClassOf :CoherenceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Rosenberg1998_57 ; + rdfs:comment "A coherence analysis that computes the partial coherence, i.e., the coherence value between a pair of inputs (e.g., time series with recordings from two distinct electrode channels) after removing the influence of one or more additional inputs (e.g., the time series with the recordings from all remaining channels). The partial coherence is computed according to Rosenberg et al. (1998). The partial coherence reflects the linear association in the frequency domain (for each frequency component) between the pair of inputs of interest, removing spurious coherence caused by confounding factors such as shared inputs to the pair of interest or volume conduction."@en ; + skos:prefLabel "compute partial coherence"@en . + + +### http://purl.org/neao/steps#ComputePartialDirectedCoherence +:ComputePartialDirectedCoherence rdf:type owl:Class ; + rdfs:subClassOf :SpectralAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isMultivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute PDC"@en ; + neao_base:hasBibliographicReference neao_bib:Baccala2001_463 ; + rdfs:comment "A coherence analysis that computes the partial directed coherence (PDC) according to Baccalá & Sameshima (2001). The PDC describes the relationships between multivariate time series inputs (direction of information flow). To compute the PDC, the multivariate partial coherences obtained from multivariate autoregressive models are decomposed. The PDC reflects a frequency-domain representation of the concept associated with Granger causality."@en ; + skos:prefLabel "compute partial directed coherence"@en . + + +### http://purl.org/neao/steps#ComputePeristimulusTimeHistogramAdaptiveKernelSmoothing +:ComputePeristimulusTimeHistogramAdaptiveKernelSmoothing rdf:type owl:Class ; + rdfs:subClassOf :PeristimulusTimeHistogramAnalysis ; + neao_base:abbreviation "compute PSTH (adaptive kernel smoothing)"@en ; + rdfs:comment "A peristimulus time histogram (PSTH) analysis that computes the PSTH using kernel density smoothing with an adaptive kernel width. The adaptive kernel is obtained by first computing the PSTH with a fixed-width kernel, and then modifying the kernel width in order to have a constant but time-dependent average number of spikes under the kernel (i.e., segments of the data with a high density of spikes will have a reduced kernel width)."@en ; + skos:prefLabel "compute peristimulus time histogram (adaptive kernel smoothing)"@en . + + +### http://purl.org/neao/steps#ComputePeristimulusTimeHistogramFixedKernelSmoothing +:ComputePeristimulusTimeHistogramFixedKernelSmoothing rdf:type owl:Class ; + rdfs:subClassOf :PeristimulusTimeHistogramAnalysis ; + neao_base:abbreviation "compute PSTH (fixed kernel smoothing)"@en ; + rdfs:comment "A peristimulus time histogram (PSTH) analysis that computes the PSTH using kernel density smoothing with a fixed kernel width specified as parameter."@en ; + skos:prefLabel "compute peristimulus time histogram (fixed kernel smoothing)"@en . + + +### http://purl.org/neao/steps#ComputePeristimulusTimeHistogramOptimalBinSize +:ComputePeristimulusTimeHistogramOptimalBinSize rdf:type owl:Class ; + rdfs:subClassOf :PeristimulusTimeHistogramAnalysis ; + neao_base:abbreviation neao_bib:Scott1979_605 , + "compute PSTH (optimal bin size)"@en ; + rdfs:comment "A peristimulus time histogram (PSTH) analysis that computes the PSTH finding the optimal bin size from the input spike train data, using the formula from Scott (1979)."@en ; + skos:prefLabel "compute peristimulus time histogram (optimal bin size)"@en . + + +### http://purl.org/neao/steps#ComputePeristimulusTimeHistogramUserSelectedBinSize +:ComputePeristimulusTimeHistogramUserSelectedBinSize rdf:type owl:Class ; + rdfs:subClassOf :PeristimulusTimeHistogramAnalysis ; + neao_base:abbreviation "compute PSTH (user-selected bin size)"@en ; + rdfs:comment "A peristimulus time histogram (PSTH) analysis that computes the PSTH using a fixed bin size specified as a parameter."@en ; + skos:prefLabel "compute peristimulus time histogram (user-selected bin size)"@en . + + +### http://purl.org/neao/steps#ComputePhaseDifference +:ComputePhaseDifference rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis ; + rdfs:comment "A phase analysis that computes the difference between two input phases."@en ; + skos:prefLabel "compute phase difference"@en . + + +### http://purl.org/neao/steps#ComputePhaseLagIndex +:ComputePhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "compute PLI"@en ; + neao_base:hasBibliographicReference neao_bib:Stam2007_1178 ; + rdfs:comment "A phase lag index (PLI) analysis that computes the PLI following Stam et al. (2007). The input data contains multiple repetitions of a pair of signals (e.g., time series with recordings from a pair of electrodes across multiple trials). For each repetition, the sign of the phase differences between the two time series is obtained from the imaginary part of the cross power spectral density (CPSD). The PLI value is the absolute value of the average of the signs of all repetitions. The PLI ranges between 0 and 1. A PLI of zero means that the first time series leads the second equally often (i.e., indicates either no coupling or coupling with a phase difference centered around 0 mod π, which could be from common sources such as volume conduction). A value greater than zero means an imbalance in the likelihood of the first time series to be either leading or lagging the second time series. A PLI of 1 indicates perfect phase locking, and that the first time series only leads or only lags (at a value of phase differences different from 0 mod π)."@en ; + skos:prefLabel "compute phase lag index"@en . + + +### http://purl.org/neao/steps#ComputePhaseLockingValue +:ComputePhaseLockingValue rdf:type owl:Class ; + rdfs:subClassOf :PhaseLockingValueAnalysis ; + neao_base:abbreviation "compute PLV"@en ; + neao_base:hasBibliographicReference neao_bib:Lachaux1999_194 ; + rdfs:comment "A phase locking value (PLV) analysis that computes the PLV value as originally described by Lachaux et al. (1999). The input data is a set of pairs of time series (e.g., the trial-by-trial local field potential recorded from two different electrodes). For each time series pair, the instantaneous phase is obtained (e.g., using the Hilbert transform or wavelet decomposition), and the phase difference for each time point is obtained. The PLV value is computed by averaging the complex phase differences across all pairs, obtaining one PLV value per time point. The PLV ranges from 0 to 1. A PLV of 1 indicates perfect phase locking, meaning the phase difference between the two time series is constant over time. A PLV of 0 indicates no phase locking, meaning the phase difference is randomly distributed over time."@en ; + skos:prefLabel "compute phase locking value"@en . + + +### http://purl.org/neao/steps#ComputePhaseSlopeIndex +:ComputePhaseSlopeIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "compute PSI"@en ; + neao_base:hasBibliographicReference neao_bib:Nolte2008_234101 ; + rdfs:comment "A functional connectivity analysis that computes the phase slope index (PSI) according to Nolte et al. (2008). The PSI is based on the slope of the phase of the cross-spectral density between two time series inputs, considering how the phase difference between two signals changes as you move from one frequency bin to the next. It is computed from the complex-valued coherency using a bandwidth specified as parameter. For the computation, the change in phase difference between neighboring frequency bins is obtained (considering the specified bandwidth) and weighted. The PSI value deviates from zero when the phase difference changes consistently across frequencies and there is substantial coherence. The sign of the PSI indicates the temporal order of the two signals (i.e., which signal is leading the other one)."@en ; + skos:prefLabel "compute phase slope index"@en . + + +### http://purl.org/neao/steps#ComputePopulationHistogram +:ComputePopulationHistogram rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainTimeHistogramAnalysis ; + owl:disjointWith :PeristimulusTimeHistogramAnalysis ; + rdfs:comment "A spike train time histogram analysis that computes a histogram across two or more spike trains that contain the activity of different neurons (i.e., a neuronal population), recorded at fully-overlapping time intervals. The activity in each histogram bin reflects the combined activity of the population at that time, and the distribution of the histogram corresponds to the population activity over time."@en ; + skos:prefLabel "compute population histogram"@en . + + +### http://purl.org/neao/steps#ComputePowerSpectralDensityBartlett +:ComputePowerSpectralDensityBartlett rdf:type owl:Class ; + rdfs:subClassOf :PowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute PSD (Bartlett method)"@en ; + neao_base:hasBibliographicReference neao_bib:Bartlett1950_1 ; + rdfs:comment "A power spectral density (PSD) analysis that uses the method defined by Bartlett (1950). For the computation, the input time is divided into non-overlapping segments, with length specified as a parameter. A periodogram is computed for each segment to obtain the single-segment PSD. The final PSD is obtained by averaging all the single-segment PSDs. A window function can be applied to each segment before computing the periodograms. The PSD obtained with the Bartlett method is less noisy than a single periodogram obtained from the entire signal, although the frequency resolution of the estimates is reduced due to segmenting. It is equivalent to the Welch method without any segment overlap."@en ; + skos:prefLabel "compute power spectral density (Bartlett method)"@en . + + +### http://purl.org/neao/steps#ComputePowerSpectralDensityMultitaper +:ComputePowerSpectralDensityMultitaper rdf:type owl:Class ; + rdfs:subClassOf :PowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute PSD (multitaper method)"@en ; + neao_base:hasBibliographicReference neao_bib:Thomson1982_1055 ; + rdfs:comment "A power spectral density (PSD) analysis that uses a multitaper approach to compute the PSD, according to Thomson (1982). The multitaper method uses discrete prolate spheroidal functions (DPSS, also known as Slepian sequences) as tapers applied to the input signal. For the computation, a PSD using the periodogram method is obtained for each tapered signal. The DPSS functions are orthogonal, and therefore applying multiple DPSS tapers result in independent estimates of the PSD. The final PSD is obtained by averaging the periodograms across all tapers. The multitaper method reduces variance and bias in the PSD, and has a high frequency resolution. However, it is computationally expensive. The number of tapers used is passed as parameter, or estimated from the desired resolution."@en ; + skos:prefLabel "compute power spectral density (multitaper method)"@en . + + +### http://purl.org/neao/steps#ComputePowerSpectralDensityPeriodogram +:ComputePowerSpectralDensityPeriodogram rdf:type owl:Class ; + rdfs:subClassOf :PowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute PSD (periodogram method)"@en ; + rdfs:comment "A power spectral density (PSD) analysis that uses the periodogram method. For the computation, the discrete Fourier transform is applied to the full length of the input, and the power spectrum is obtained. To reduce spectral leakage, a window function can be applied to the input before the computation of the Fourier transform (this is referred as the modified periodogram). The power spectrum is then normalized to the unit frequency, using the equivalent noise bandwidth (a factor that depends on the coefficients of the window function and the sampling rate). If no window function is used or a window that does not attenuate the signal is used (e.g., Boxcar or rectangular window) the equivalent noise bandwidth is equal to the frequency resolution. The PSD using the periodogram is computationally simple to obtain."@en ; + skos:altLabel "compute FFT power spectral density"@en , + "compute periodogram"@en ; + skos:prefLabel "compute power spectral density (periodogram method)"@en . + + +### http://purl.org/neao/steps#ComputePowerSpectralDensityWelch +:ComputePowerSpectralDensityWelch rdf:type owl:Class ; + rdfs:subClassOf :PowerSpectralDensityAnalysis ; + neao_base:abbreviation "compute PSD (Welch method)"@en ; + neao_base:hasBibliographicReference neao_bib:Welch1967_70 ; + rdfs:comment "A power spectral density (PSD) analysis that uses the method defined by Welch (1967). For the computation, the input is divided into several overlapping segments (length and overlap passed as parameters, or computed for the desired frequency resolution based on the input length and sampling frequency). A window function (e.g., Hann) is applied to each segment, and a periodogram is computed to obtain the PSD for the segment. The final PSD is obtained by averaging all the periodograms with the single-segment PSDs. If there is no overlap between segments, this is equivalent to the Bartlett method."@en ; + skos:prefLabel "compute power spectral density (Welch method)"@en . + + +### http://purl.org/neao/steps#ComputeRateChangeDetectionMultipleFilterTest +:ComputeRateChangeDetectionMultipleFilterTest rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Messer2014_2027 ; + rdfs:comment "An analysis step that uses the change point detection algorithm from Messer et al. (2014) to determine if a input spike train has constant firing rate (stationary) or has one or more points in which the firing rate decreases or increases (change point). In the latter case, the spike train is considered non-stationary. The analysis step outputs one or more change points in the case of non-stationarity."@en ; + skos:prefLabel "compute rate change detection multiple filter test"@en . + + +### http://purl.org/neao/steps#ComputeRectifiedAreaUnderCurve +:ComputeRectifiedAreaUnderCurve rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:abbreviation "compute RAUC"@en ; + rdfs:comment "An analysis step that computes the rectified area under the curve (RAUC). For the computation, the input signal is rectified (i.e., the absolute value is obtained) and the area under the curve is computed by integration using the composite trapezoidal rule."@en ; + skos:prefLabel "compute rectified area under the curve"@en . + + +### http://purl.org/neao/steps#ComputeRegularizedCovariance +:ComputeRegularizedCovariance rdf:type owl:Class ; + rdfs:subClassOf :ComputeCovariance ; + rdfs:comment "A covariance analysis where the computation of the covariance values incorporates regularization techniques to improve the numerical stability, especially if the number of samples is small."@en ; + skos:prefLabel "compute regularized covariance"@en . + + +### http://purl.org/neao/steps#ComputeSPIKEDistance +:ComputeSPIKEDistance rdf:type owl:Class ; + rdfs:subClassOf :TimeScaleIndependentSpikeTrainDistanceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Kreuz2012_1457 ; + rdfs:comment "A time-scale independent spike train distance analysis that computes the SPIKE-distance, described in Kreuz et al. (2012). For the computation, the discrete sequence of spike times is transformed in a continuous temporal profile with one value per sample point. The values at each time point are derived from the differences in the spike times of the two input spike trains. Compared to the ISI-distance, the SPIKE-distance is more sensitive to spike timing."@en ; + skos:prefLabel "compute SPIKE distance"@en . + + +### http://purl.org/neao/steps#ComputeSPIKESynchronization +:ComputeSPIKESynchronization rdf:type owl:Class ; + rdfs:subClassOf :TimeScaleIndependentSpikeTrainDistanceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Kreuz2015_3432 ; + rdfs:comment "A time-scale independent spike train distance analysis that computes the SPIKE synchronization distance, described in Kreuz et al. (2015). The distance detects coincidences in the spiking activity and can quantify the degree of synchrony in the input spike trains. The metric quantifies the overall fraction of coincidences. It is zero-valued if and only if the input spike trains do not contain any coincidences. It has a value of 1 if and only if each spike in every input spike train has one matching spike in all the other spike trains."@en ; + skos:prefLabel "compute SPIKE synchronization"@en . + + +### http://purl.org/neao/steps#ComputeShortTimeFourierTransform +:ComputeShortTimeFourierTransform rdf:type owl:Class ; + rdfs:subClassOf :TimeFrequencyAnalysis ; + neao_base:abbreviation "compute STFT"@en ; + rdfs:comment "A time-frequency analysis that computes the short-time Fourier transform (STFT) of the input time series. The analysis divides the input time-domain signal into short segments with equal time and computes the Fourier transform for each segment. The output is a sequence of coefficients of complex sinusoids, each representing a frequency component in the input signal at a distinct time segment. Therefore, this provides the the time-localized frequency and phase information of the input. The segments can be windowed using a window function."@en ; + skos:prefLabel "compute short-time Fourier transform"@en . + + +### http://purl.org/neao/steps#ComputeSpectrogramMorletWavelet +:ComputeSpectrogramMorletWavelet rdf:type owl:Class ; + rdfs:subClassOf :SpectrogramAnalysis ; + rdfs:comment "A spectrogram analysis that uses the Morlet wavelet transform on the input to obtain the time-frequency information used to build the spectrogram."@en ; + skos:prefLabel "compute spectrogram (Morlet wavelet method)"@en . + + +### http://purl.org/neao/steps#ComputeSpectrogramMultitaper +:ComputeSpectrogramMultitaper rdf:type owl:Class ; + rdfs:subClassOf :SpectrogramAnalysis ; + rdfs:comment "A spectrogram analysis that uses a multitaper approach to obtain the time-frequency information from the input and that is used to build the spectrogram."@en ; + skos:prefLabel "compute spectrogram (multitaper method)"@en . + + +### http://purl.org/neao/steps#ComputeSpectrogramShortTimeFourierTransform +:ComputeSpectrogramShortTimeFourierTransform rdf:type owl:Class ; + rdfs:subClassOf :SpectrogramAnalysis ; + neao_base:abbreviation "compute spectrogram (STFT method)"@en ; + rdfs:comment "A spectrogram analysis that uses the short-time Fourier transform (STFT) on the input to obtain the time-frequency information used to build the spectrogram."@en ; + skos:prefLabel "compute spectrogram (short-time Fourier transform method)"@en . + + +### http://purl.org/neao/steps#ComputeSpikeContrast +:ComputeSpikeContrast rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSynchronyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Ciba2018_136 ; + rdfs:comment "A spike train synchrony analysis that computes the Spike-contrast measure using the method described by Ciba et al. (2018). Spike-contrast is a time-scale independent measure of spike synchrony. The input is a set of parallel spike train data recorded from a population of neurons. The algorithm is based on the temporal \"contrast\" (activity vs. non-activity in certain time bins). The computation outputs a single synchrony value (comparable to a spike train distance) and a synchrony curve showing the value of Spike-contrast as a function of the bin size."@en ; + skos:prefLabel "compute Spike-contrast"@en . + + +### http://purl.org/neao/steps#ComputeSpikeFieldCoherenceFries +:ComputeSpikeFieldCoherenceFries rdf:type owl:Class ; + rdfs:subClassOf :SpikeFieldCoherenceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Fries2001_1560 ; + rdfs:comment "A spike-field coherence analysis that uses the method described in Fries et al. (2001) to compute the coherence between the spike train and the LFP. For the computation, first a spike-triggered average (STA) is obtained between the spike train and the LFP time series. Then, the power spectrum is obtained for each of the LFP segments used for the computation of the STA. These spectra are averaged to obtain the spike-triggered power spectrum. The SFC is then computed as the ratio of the power spectrum of the STA over the spike-triggered power spectrum."@en ; + skos:prefLabel "compute spike-field coherence (Fries method)"@en . + + +### http://purl.org/neao/steps#ComputeSpikeFieldCoherenceMultitaper +:ComputeSpikeFieldCoherenceMultitaper rdf:type owl:Class ; + rdfs:subClassOf :SpikeFieldCoherenceAnalysis ; + neao_base:abbreviation "compute SFC (multitaper method)"@en ; + rdfs:comment "A spike-field coherence analysis that uses a multitaper approach to compute the coherence between the spike train and the LFP."@en ; + skos:prefLabel "compute spike-field coherence (multitaper method)"@en . + + +### http://purl.org/neao/steps#ComputeSpikeFieldCoherenceWelch +:ComputeSpikeFieldCoherenceWelch rdf:type owl:Class ; + rdfs:subClassOf :SpikeFieldCoherenceAnalysis ; + neao_base:abbreviation "compute SFC (Welch method)"@en ; + neao_base:hasBibliographicReference neao_bib:Welch1967_70 ; + rdfs:comment "A spike-field coherence analysis step that uses the method by Welch (1967) to compute the coherence between the spike train and the LFP."@en ; + skos:prefLabel "compute spike-field coherence (Welch method)"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTimeTilingCoefficient +:ComputeSpikeTimeTilingCoefficient rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationAnalysis ; + neao_base:abbreviation "compute STTC"@en ; + neao_base:hasBibliographicReference neao_bib:Cutts2014_14288 ; + rdfs:comment "A spike train correlation analysis that computes the spike time tiling coefficient (STTC) as described by Cutts and Eglen (2014). The STTC measures the pairwise correlation between two input spike trains, and has advantages over the related correlation index: it is not confounded by the firing rate, it distinguishes lack of correlation from anti-correlation, periods without neural activity don't add to the correlation, and it is sensitive to firing patterns. The computation is based on a synchronicity window parameter, that is used to define short time windows around each spike that are used in the computation (spike time tiling)."@en ; + skos:prefLabel "compute spike time tiling coefficient"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainAutocorrelationHistogram +:ComputeSpikeTrainAutocorrelationHistogram rdf:type owl:Class ; + rdfs:subClassOf :AutocorrelationAnalysis ; + rdfs:comment "An autocorrelation analysis that computes the autocorrelation histogram for a input spike train. The histogram is obtained by sliding a time window that is discretized into smaller time intervals (bins) centered on a spike (corresponding to the lag zero). The spike count in each bin is obtained. Therefore, this binning process measures the number of spikes occurring at various time lags relative to the center spike. The histogram window is slidden over each spike in the input spike train, and the spike count in each bin is accumulated to produce the autocorrelation histogram output. The width of the bin interval is controlled by a parameter."@en ; + skos:prefLabel "compute spike train autocorrelation histogram"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainAutocorrelationTimeScale +:ComputeSpikeTrainAutocorrelationTimeScale rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:hasBibliographicReference neao_bib:Wieland2015_040901 ; + rdfs:comment "An analysis step that computes the autocorrelation time of a binned spike train input (spike train autocorrelation time scale). The computation follows the method described by Wieland et al. (2015)."@en ; + skos:prefLabel "compute spike train autocorrelation time scale"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainCrossCorrelationHistogram +:ComputeSpikeTrainCrossCorrelationHistogram rdf:type owl:Class ; + rdfs:subClassOf :CrossCorrelationAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeSpikeCouplingPurpose + ] ; + neao_base:abbreviation "compute CCH"@en ; + rdfs:comment "A cross-correlation analysis that computes the cross-correlation histogram (CCH) for a pair of input spike trains. The CCH shows how often spikes in the reference spike train occur before or after spikes in the reference spike train, at distinct lag intervals. For the computation, the spike trains are aligned in time. The histogram is obtained by sliding a time window that is discretized into smaller time intervals (bins) centered on a spike of the first (reference) input spike train (corresponding to the lag zero). The spike count of the second (target) spike train input is then obtained in each bin. Therefore, this binning process measures the number of spikes in the target spike train occurring at various time lags relative to the spikes in the reference spike train. The histogram window is slidden over each spike in the reference spike train, and the spike count in each bin is accumulated to produce the CCH output. The width of the bin interval is controlled by a parameter."@en ; + skos:altLabel "compute correlogram"@en , + "compute cross-correlogram"@en ; + skos:prefLabel "compute spike train cross-correlation histogram"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainCrossCorrelationHistogramEggermont +:ComputeSpikeTrainCrossCorrelationHistogramEggermont rdf:type owl:Class ; + rdfs:subClassOf :ComputeSpikeTrainCrossCorrelationHistogram ; + neao_base:abbreviation "compute CCH (Eggermont method)"@en ; + neao_base:hasBibliographicReference neao_bib:Eggermont2010_77 ; + rdfs:comment "A computation of the cross-correlation histogram (CCH) for a pair of binned input spike trains using the method described in Eggermont (2010). The formula is valid for binned spike train inputs with at most one spike per bin, and returns the cross-correlation coefficient for the lags considered (range -1 to 1)."@en ; + skos:altLabel "compute correlogram (Eggermont method)"@en , + "compute cross-correlogram (Eggermont method)"@en ; + skos:prefLabel "compute spike train cross-correlation histogram (Eggermont method)"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainFanoFactor +:ComputeSpikeTrainFanoFactor rdf:type owl:Class ; + rdfs:subClassOf :NeuronalFiringRegularityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :NeuronalFiringRegularityPurpose + ] ; + neao_base:hasBibliographicReference neao_bib:Nawrot2008_374 ; + rdfs:comment "An analysis step that computes the Fano factor (FF) for a set of input spike trains. For each input spike train, the spike count is obtained. The Fano factor is defined as the ratio of the variance of the spike count to the mean spike count, across all spike trains. The Fano factor is usually computed for spike trains representing the activity of the same neuron over different trials. The value is interpreted as the higher the Fano factor value, the larger the cross-trial non-stationarity. For a stationary Poisson process, the Fano factor has value equal to 1."@en ; + skos:prefLabel "compute spike train Fano factor"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainPearsonCorrelationCoefficient +:ComputeSpikeTrainPearsonCorrelationCoefficient rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainCorrelationAnalysis ; + rdfs:comment "A spike train correlation analysis that computes the Pearson correlation coefficient between two spike train inputs. The Pearson correlation coefficient is a real value that quantifies the linear relationship between the two spike trains. It has range [-1, 1], where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. For the computation, the input spike trains are discretized into time intervals (bins), and the spike count is obtained in each bin. The Pearson correlation coefficient is obtained by normalizing the covariance between the binned spike trains: the covariance is divided by the product of the standard deviation of each."@en ; + skos:prefLabel "compute spike train Pearson correlation coefficient"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTrainTimeHistogram +:ComputeSpikeTrainTimeHistogram rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainTimeHistogramAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :InstantaneousFiringRatePurpose + ] ; + rdfs:comment "An analysis step that computes the time histogram of a spike train. If the spike count in a bin is divided by the duration of the bin, this can be used to estimate the instantaneous firing rate at the bin interval."@en ; + skos:prefLabel "compute spike train time histogram"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTriggeredAverage +:ComputeSpikeTriggeredAverage rdf:type owl:Class ; + rdfs:subClassOf :TriggeredAverageAnalysis ; + neao_base:abbreviation "compute STA"@en ; + rdfs:comment "A triggered average analysis that uses spike times as triggers to obtain the average of a signal around each spike (spike-triggered average)."@en ; + skos:prefLabel "compute spike-triggered average"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTriggeredLFPAverage +:ComputeSpikeTriggeredLFPAverage rdf:type owl:Class ; + rdfs:subClassOf :ComputeSpikeTriggeredAverage , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeFieldCouplingPurpose + ] ; + neao_base:abbreviation "compute STA"@en ; + rdfs:comment "A triggered average analysis that uses spike times as triggers to average the local field potential (LFP) signal. The LFP is the low-frequency component of the potential recorded within a specific region of the brain using extracellular electrodes. The output of the method will provide an estimation of the average LFP voltage around each spike, i.e., the spike-triggered average of the LFP signal."@en ; + skos:prefLabel "compute spike-triggered local field potential average"@en . + + +### http://purl.org/neao/steps#ComputeSpikeTriggeredPhase +:ComputeSpikeTriggeredPhase rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis ; + rdfs:comment "A phase analysis that computes the phase angle values of an analytic signal input (or from the analytic signal obtained from an input time series) at the time points where spikes occurred. The spike times are defined in a spike train input. The output is an array with the phase angle at each spike time in the input spike train (spike-triggered phases)."@en ; + skos:prefLabel "compute spike-triggered phase"@en . + + +### http://purl.org/neao/steps#ComputeSpikeWaveformAverage +:ComputeSpikeWaveformAverage rdf:type owl:Class ; + rdfs:subClassOf :SpikeWaveformAnalysis ; + rdfs:comment "A spike waveform analysis that computes the average across two or more spike waveform inputs. For the computation, the mean value across all inputs is obtained for each time point in the sampled spike waveform. This is frequently used to reduce noise across multiple spike waveform samples of a single neuron."@en ; + skos:prefLabel "compute spike waveform average"@en . + + +### http://purl.org/neao/steps#ComputeSpikeWaveformSNR +:ComputeSpikeWaveformSNR rdf:type owl:Class ; + rdfs:subClassOf :SpikeWaveformAnalysis ; + neao_base:abbreviation "compute spike waveform SNR"@en ; + neao_base:hasBibliographicReference neao_bib:Hatsopoulos2007_5105 ; + rdfs:comment "A spike waveform analysis that computes the signal-to-noise ratio (SNR) for a set of input spike waveforms according to Hatsopoulos (2007). The SNR is defined as the difference in mean peak-to-trough voltage divided by twice the mean standard deviation (SD).The mean SD is obtained by averaging the SDs computed for each time point in the spike waveform."@en ; + skos:prefLabel "compute spike waveform signal-to-noise ratio"@en . + + +### http://purl.org/neao/steps#ComputeSpikeWaveformVariance +:ComputeSpikeWaveformVariance rdf:type owl:Class ; + rdfs:subClassOf :SpikeWaveformAnalysis ; + rdfs:comment "A spike waveform analysis that computes the variance across two or more spike waveform inputs. For the computation, the value of the variance across all inputs is obtained for each time point in the spike waveform."@en ; + skos:prefLabel "compute spike waveform variance"@en . + + +### http://purl.org/neao/steps#ComputeSpikeWaveformWidth +:ComputeSpikeWaveformWidth rdf:type owl:Class ; + rdfs:subClassOf :SpikeWaveformAnalysis ; + rdfs:comment "A spike waveform analysis that computes the width of a spike waveform input. The computation takes two time points of interest (e.g, the times of the peak and the trough), and the width is the difference with respect to the time points (e.g., number of time points in between or time interval)."@en ; + skos:prefLabel "compute spike waveform width"@en . + + +### http://purl.org/neao/steps#ComputeStandardDeviation +:ComputeStandardDeviation rdf:type owl:Class ; + rdfs:subClassOf :DispersionStatisticalAnalysis ; + neao_base:abbreviation "compute SD"@en ; + rdfs:comment "A dispersion statistical analysis that computes the standard deviation (SD), i.e., the square root of the variance. The SD indicates the average distance of each data point from the mean."@en ; + skos:prefLabel "compute standard deviation"@en . + + +### http://purl.org/neao/steps#ComputeStandardErrorMean +:ComputeStandardErrorMean rdf:type owl:Class ; + rdfs:subClassOf :DispersionStatisticalAnalysis ; + neao_base:abbreviation "compute SEM"@en ; + rdfs:comment "A dispersion statistical analysis that computes the standard error of the mean (SEM). The SEM is the standard deviation of the sampling distribution of the sample mean. It provides an estimate of how much the sample mean is expected to fluctuate around the true population mean. Smaller SEM values indicates that the sample mean is a more accurate estimate of the population mean. The SEM decreases as the sample size increases, as larger samples provide a more reliable estimate of the population mean."@en ; + skos:prefLabel "compute standard error of the mean"@en . + + +### http://purl.org/neao/steps#ComputeStockwellTransform +:ComputeStockwellTransform rdf:type owl:Class ; + rdfs:subClassOf :TimeFrequencyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Stockwell1996_998 ; + rdfs:comment "A time-frequency analysis that computes the Stockwell transform (S transform) of the input time series. The S transform generalizes the short-time Fourier transform (STFT) and extends the continuous wavelet transform (CWT). The main difference is that STFT uses a constant window width for all frequencies. The S transform is based on a moving and scalable localizing Gaussian window. Therefore, the window is frequency-dependent (adaptive windowing), which results in better time resolution in higher frequencies and better frequency resolution at lower frequencies. This makes the S transform more suitable to detect transient signals in high frequencies. The computation is computationally expensive, although fast algorithms are available."@en ; + skos:altLabel "compute S transform"@en ; + skos:prefLabel "compute Stockwell transform"@en . + + +### http://purl.org/neao/steps#ComputeTimeDomainConditionalGrangerCausality +:ComputeTimeDomainConditionalGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :ConditionalGrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:hasBibliographicReference neao_bib:Ding2006_0608035 ; + rdfs:comment "A conditional Granger causality (GC) analysis that computes the GC measures in the time domain."@en ; + skos:prefLabel "compute time domain conditional Granger causality"@en . + + +### http://purl.org/neao/steps#ComputeTimeDomainPairwiseGrangerCausality +:ComputeTimeDomainPairwiseGrangerCausality rdf:type owl:Class ; + rdfs:subClassOf :PairwiseGrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + owl:disjointWith :FrequencyDomainPairwiseGrangerCausalityAnalysis ; + neao_base:hasBibliographicReference neao_bib:Ding2006_0608035 ; + rdfs:comment "A pairwise Granger causality (GC) analysis that computes measures of GC between two inputs in the time domain. The computation involves fitting two separate autoregressive (AR) models to the input data and comparing their fit to determine if one time series can predict the other. The quality of the fit is assessed by the variance of the residuals, with GC defined as the natural logarithm of the ratio of the residual variances from the two AR models. The first AR model is univariate, predicting the future values of one time series (e.g., the first) only from its past values. The second AR model is bivariate, predicting the future values of the first time series from its past values as well as the past values of the second time series. If the bivariate model reduces the variance of the residuals (ratio greater than 1), the GC value will be positive, indicating that the second time series Granger causes the first (directional GC estimate from the second to the first). The same method is used to predict the second time series from the first, yielding the directional GC estimate from the first to the second time series. The order of the AR model is defined as parameter, and optimal values can be estimated using optimization techniques."@en ; + skos:prefLabel "compute time domain pairwise Granger causality"@en . + + +### http://purl.org/neao/steps#ComputeTransferEntropy +:ComputeTransferEntropy rdf:type owl:Class ; + rdfs:subClassOf :FunctionalConnectivityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "compute TE"@en ; + neao_base:hasBibliographicReference neao_bib:Schreiber2000_461 ; + rdfs:comment "A functional connectivity analysis that computes a measure of transfer entropy (TE) between two input time series. TE measures the directional transfer of information between the time series. It extends Granger causality, and is able to detect non-linear forms of interaction."@en ; + skos:prefLabel "compute transfer entropy"@en . + + +### http://purl.org/neao/steps#ComputeTuningCurve +:ComputeTuningCurve rdf:type owl:Class ; + rdfs:subClassOf :FiringRateAnalysis ; + rdfs:comment "A firing rate analysis that computes a tuning curve. The tuning curve describes the firing rate of a neuron as a function of a continuous attribute (e.g., orientation of a visual grating stimulus)."@en ; + skos:prefLabel "compute tuning curve"@en . + + +### http://purl.org/neao/steps#ComputeUnbiasedSquaredPhaseLagIndex +:ComputeUnbiasedSquaredPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "compute unbiased squared PLI"@en ; + neao_base:hasBibliographicReference neao_bib:Vinck2011_1548 ; + rdfs:comment "A phase lag index (PLI) analysis that computes an unbiased estimate for the squared PLI following Vinck et al. (2011). The direct PLI estimator is positively biased, especially when the sample sizes (i.e., number of trials) are small. The unbiased squared PLI is computed by averaging all pairwise products of the signs computed across the repetitions. Pairs with identical observations are excluded. The unbiased squared PLI is less affected by small-sample size biases."@en ; + skos:prefLabel "compute unbiased squared phase lag index"@en . + + +### http://purl.org/neao/steps#ComputeVanRossumDistance +:ComputeVanRossumDistance rdf:type owl:Class ; + rdfs:subClassOf :TimeScaleDependentSpikeTrainDistanceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Rossum2001_751 ; + rdfs:comment "A time-scale dependent spike train distance analysis that computes the van Rossum distance introduced in van Rossum (2001). For the computation, each spike in the input spike trains is convolved with an exponential kernel, producing continuous function representations of the input spike trains. The time scale parameter of the distance is set by the time constant of the exponential kernel. The distance is then obtained as the Euclidean distance of the convolved spike trains."@en ; + skos:prefLabel "compute van Rossum distance"@en . + + +### http://purl.org/neao/steps#ComputeVariance +:ComputeVariance rdf:type owl:Class ; + rdfs:subClassOf :DispersionStatisticalAnalysis ; + rdfs:comment "A dispersion statistical analysis that computes the variance, i.e., the average of the squared differences from the mean."@en ; + skos:prefLabel "compute variance"@en . + + +### http://purl.org/neao/steps#ComputeVictorPurpuraDistance +:ComputeVictorPurpuraDistance rdf:type owl:Class ; + rdfs:subClassOf :TimeScaleDependentSpikeTrainDistanceAnalysis ; + neao_base:hasBibliographicReference neao_bib:Victor1996_1310 ; + rdfs:comment "A time-scale dependent spike train distance analysis that computes the Victor-Purpura distance, introduced in Victor & Purpura (1996). The metric defines the distance between two spike train inputs with respect to the minimum cost of transforming one spike train into the order considering three operations: spike insertion, spike deletion and shifting a spike by some interval. The first two operations have a fixed cost equal to 1. The latter depends on a cost per time unit parameter, which sets the time scale of the analysis."@en ; + skos:prefLabel "compute Victor-Purpura distance"@en . + + +### http://purl.org/neao/steps#ComputeWeightedPhaseLagIndex +:ComputeWeightedPhaseLagIndex rdf:type owl:Class ; + rdfs:subClassOf :PhaseLagIndexAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "compute WPLI"@en ; + neao_base:hasBibliographicReference neao_bib:Vinck2011_1548 ; + rdfs:comment "A phase lag index (PLI) analysis that computes the weighted PLI (WPLI) following Vinck et al. (2011). The original PLI (Stam, 2007) is discontinuous, and small perturbations can turn phase lags into leads (and vice versa). Therefore, this hinders its capacity to detect changes in phase synchronization of small magnitude. The WPLI extends the PLI to weight the contributions of the phase leads and lags by the magnitude of the imaginary component of the cross spectral density. Therefore, these increases the power to detect changes in phase synchronization. The WPLI ranges between 0 and 1. A WPLI of zero means that there is no imbalance in the first time series leading or lagging the second (i.e., the total weight of all leading relationships is equal to the total weight of lagging relationships). A value greater than zero means an imbalance in the likelihood of leading or lagging. A WPLI of 1 indicates perfect phase locking, and that the first time series only leads or only lags."@en ; + skos:prefLabel "compute weighted phase lag index"@en . + + +### http://purl.org/neao/steps#ComputedEvokedPotential +:ComputedEvokedPotential rdf:type owl:Class ; + rdfs:subClassOf :ComputeEventRelatedPotential ; + neao_base:abbreviation "compute EP"@en ; + rdfs:comment "An analysis step that computes the evoked potential (EP). The EP is the neural activity (e.g., local field potential or electroencephalogram voltages) around a presented stimulus (e.g., auditory, visual, electrical). Usually, the stimulus is presented repeatedly across multiple trials, obtaining multiple evoked potential waveforms that can be averaged to cancel the noise. It is an event-related potential (ERP) obtained from presenting a stimulus rather than spontaneous behavioral events."@en ; + skos:prefLabel "compute evoked potential"@en . + + +### http://purl.org/neao/steps#ConditionalGrangerCausalityAnalysis +:ConditionalGrangerCausalityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isMultivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + owl:disjointWith :PairwiseGrangerCausalityAnalysis ; + rdfs:comment "A Granger causality (GC) analysis that computes a measure of conditional GC between the inputs. The conditional GC is the causality between two inputs (e.g., two time series) while controlling for the influence of an additional input (e.g., a third time series). This allows a more complete understanding of the causal relationships in multivariate time series data."@en ; + skos:prefLabel "conditional Granger causality analysis"@en . + + +### http://purl.org/neao/steps#ConfidenceIntervalResamplingAnalysis +:ConfidenceIntervalResamplingAnalysis rdf:type owl:Class ; + rdfs:subClassOf :ConfidenceIntervalStatisticalAnalysis ; + rdfs:comment "A confidence interval statistical analysis that computes the confidence interval using techniques to generate multiple samples from the (observed) data input(s)."@en ; + skos:prefLabel "confidence interval with resampling analysis"@en . + + +### http://purl.org/neao/steps#ConfidenceIntervalStatisticalAnalysis +:ConfidenceIntervalStatisticalAnalysis rdf:type owl:Class ; + rdfs:subClassOf :StatisticalAnalysis ; + rdfs:comment "A statistical analysis that computes a confidence interval (CI). The CI is a measure providing a range of values of a parameter, derived from the input (sample) data, that is likely to contain the true value of the parameter in the population with a specified level of confidence. The level of confidence is specified as a parameter to the method. For example, a 95% confidence interval means that if the same population is sampled multiple times, approximately 95% of the intervals calculated from those samples will contain the true population parameter. Confidence intervals are used to estimate parameters such as the mean and are essential for making inferences about the population based on sample data. The output contains the upper and lower limits of the CI."@en ; + skos:prefLabel "confidence interval statistical analysis"@en . + + +### http://purl.org/neao/steps#CorrelatedSpikeTimesGeneration +:CorrelatedSpikeTimesGeneration rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainGeneration ; + owl:disjointWith :RandomSpikeTimesGeneration ; + rdfs:comment "A spike train generation where the output spike trains will have spikes that are correlated in time. These methods can be used to generate spike trains with patterns in their activity."@en ; + skos:prefLabel "correlated spike times generation"@en . + + +### http://purl.org/neao/steps#CorrelationAnalysis +:CorrelationAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :CorrelationPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "An analysis step that computes a measure of correlation between two inputs. Correlation is a measure that quantifies the strength to which two variables change together. It is a scaled version of the covariance, and the values are restricted to the -1 to +1 interval. Between time series, it is computed in the time domain."@en ; + skos:prefLabel "correlation analysis"@en . + + +### http://purl.org/neao/steps#CovarianceAnalysis +:CovarianceAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step used to compute a measure of covariance from two inputs. The covariance indicates the extent to which the two inputs change together. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same applies to the lesser values, then the covariance is positive. Conversely, if greater values of one variable mainly correspond to the lesser values of the other variable, then the covariance is negative."@en ; + skos:prefLabel "covariance analysis"@en . + + +### http://purl.org/neao/steps#CrossCorrelationAnalysis +:CrossCorrelationAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "An analysis step used to compute a measure of cross-correlation, i.e., the correlation of two inputs computed for distinct time lags of the first to the second. The computation produces the cross-correlation value for every lag considered."@en ; + skos:prefLabel "cross-correlation analysis"@en . + + +### http://purl.org/neao/steps#CrossPowerSpectralDensityAnalysis +:CrossPowerSpectralDensityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralDensityAnalysis ; + neao_base:abbreviation "CPSD analysis"@en ; + rdfs:comment "A spectral density analysis that computes the cross power spectral density (CPSD) of two inputs, i.e., the distribution of their power across the different frequency components per unit frequency. The CPSD shows in the frequency domain how the two inputs are correlated in the time domain, and is equivalent to the Fourier transform of the cross-correlation function between the two signals. If the inputs are not correlated, the CPSD will be flat across the frequencies. A peak suggests that the signals are correlated at that frequency. The CPSD is often referred to as cross-spectrum."@en ; + skos:prefLabel "cross power spectral density analysis"@en . + + +### http://purl.org/neao/steps#CurrentSourceDensityAnalysis +:CurrentSourceDensityAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:abbreviation "CSD analysis"@en ; + rdfs:comment "An analysis step used to analyze extracellular electrical potentials (e.g., local field potentials or evoked potentials) recorded from multiple locations, enabling the estimation of the current sources responsible for generating these potentials. The methods can be applied to data recorded from different electrode configurations: laminar probe-like electrodes (1D methods), microelectrode array-like electrodes (2D methods) or electrodes configurations recording from a volume (e.g., multiple laminar probes or array electrodes with shanks with multiple depths; 3D methods). The output of the current source analysis provides the spatial map showing where currents are entering (sources) and leaving (sinks) the neural tissue. For each point in the map, a quantitative value indicates the magnitude of the current density at that point."@en ; + skos:altLabel "source imaging analysis"@en , + "source localization analysis"@en ; + skos:prefLabel "current source density analysis"@en . + + +### http://purl.org/neao/steps#DataGeneration +:DataGeneration rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that generates new data entities. This can be done using other input data as a start (e.g., generating a spike train surrogate from spike trains obtained from experimental recordings) or generate new data using algorithms that take specific parameters (e.g., generating a spike train using a probability distribution defined by specific parameters)."@en ; + skos:prefLabel "data generation"@en . + + +### http://purl.org/neao/steps#DataNormalization +:DataNormalization rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that adjusts the ranges and distributions of the values in the input data. This can be used to transform data measured in distinct (i.e. not directly comparable) scales to a common (i.e. comparable) scale."@en ; + skos:prefLabel "data normalization"@en . + + +### http://purl.org/neao/steps#DataSmoothing +:DataSmoothing rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that uses statistical techniques to remove noise and fluctuations from the input data to reveal underlying trends and patterns."@en ; + skos:prefLabel "data smoothing"@en . + + +### http://purl.org/neao/steps#DataTransformation +:DataTransformation rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that takes one or more inputs and modifies the contents, such that it fits a particular purpose. The data transformation steps are frequently used during pre-processing the input data for the analyses. Usually, a data transformation will not change the main representation or quality of the inputs. For example, a digital filtering step will remove certain frequency components from a time series. However, the output will resemble the original input with respect to shape or physical units. In addition, the inputs can also be converted to other formats or representations that are needed for a particular analysis step. For example, spike trains can be discretized into small intervals (binning) or the dimensionality of the input can be reduced using principal component analysis. The data transformation is in contrast to steps that perform computations that take the input and generate a derived measure with new information. For example, when computing the mean firing rate from a spike train, a single scalar value is obtained from the spike count in the input data."@en ; + skos:prefLabel "data transformation"@en . + + +### http://purl.org/neao/steps#Detrending +:Detrending rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that removes a trend (i.e., a change in the mean over time) from an input time series."@en ; + skos:prefLabel "detrending"@en . + + +### http://purl.org/neao/steps#DigitalFiltering +:DigitalFiltering rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that processes digital signal inputs (i.e., sampled time series) to attenuate or amplify specific frequency components. Digital filters can be designed using various methods, achieving distinct frequency responses and stability."@en ; + skos:prefLabel "digital filtering"@en . + + +### http://purl.org/neao/steps#DimensionalityReduction +:DimensionalityReduction rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that obtains a low-dimensional (simplified) representation of the high-dimensional input data. This step retains important information in the input data while minimizing redundancy and noise."@en ; + skos:prefLabel "dimensionality reduction"@en . + + +### http://purl.org/neao/steps#DirectedAnalysis +:DirectedAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + owl:disjointWith :NonDirectedAnalysis ; + rdfs:comment "An analysis step where the output provides information on the direction of influence in the relationships among the inputs (e.g., in the cross-correlation histogram, it is possible to analyze the timing of the spikes of the first input spike train with respect to the timing of the spikes of the second input spike train, i.e., whether they likely occur before or after)."@en ; + skos:prefLabel "directed analysis"@en . + + +### http://purl.org/neao/steps#DispersionStatisticalAnalysis +:DispersionStatisticalAnalysis rdf:type owl:Class ; + rdfs:subClassOf :StatisticalAnalysis ; + rdfs:comment "A statistical analysis that computes a measure that represents the variability in the input data. It indicated the degree to which the data points differ from the central tendency."@en ; + skos:prefLabel "dispersion statistical analysis"@en . + + +### http://purl.org/neao/steps#DistanceAnalysis +:DistanceAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :DistancePurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep . + + +### http://purl.org/neao/steps#Execute3dSPADEAnalysis +:Execute3dSPADEAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SPADEAnalysis ; + owl:disjointWith :ExecuteNon3dSPADEAnalysis ; + neao_base:hasBibliographicReference neao_bib:Stella2019_104022 ; + rdfs:comment "A SPADE analysis that uses the 3d-SPADE implementation as defined in Stella et al. (2019). The 3d-SPADE analysis considers spatio-temporal patterns (i.e., not restricted to synchronous patterns), and the pattern signature used for statistical testing considers the size, number of occurrences and duration of a pattern."@en ; + skos:prefLabel "execute 3d-SPADE analysis"@en . + + +### http://purl.org/neao/steps#ExecuteASSETAnalysis +:ExecuteASSETAnalysis rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPatternDetectionAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty neao_base:hasSubstep ; + owl:someValuesFrom :ASSETAnalysisSubstep + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:hasBibliographicReference neao_bib:Torre2016_e1004939 ; + rdfs:comment """A neuronal activity pattern detection analysis that uses the Analysis of Sequences of Synchronous EvenTs (ASSET) method defined in Torre et al. (2016). ASSET is an automatized test to identify the sequential activations of groups of neurons that repeat in time. The identification of the repeated sequences is possible by computing the intersection matrix. The input spike data is discretized into smaller intervals (bins), and the overlap of neuronal activity at each bin pair is obtained as a matrix. When a sequence of activations exists and repeats in time, a characteristic diagonal structure appears in the matrix. + +The ASSET analysis provides a robust statistical test to automatically identify the diagonal structures and to provide the neurons and their activation pattern in each repeated sequence. Overall, the analysis is composed by 6 substeps: + +1. Compute the intersection matrix (IMAT) from a set of input spike trains, using a specified bin size to discretize the data. +2. Obtain the probability matrix (PMAT). +3. Obtain the joint probability matrix (JMAT). +4. Extract significant entries in both PMAT and JMAT using specified thresholds, obtaining the mask matrix (MMAT). +5. Obtain the cluster matrix (CMAT) by using DBSCAN to cluster the significant entries in the MMAT to find each diagonal structure. Parameters for DBSCAN control the clustering result. A modified distance metric is used. +6. From the identified clusters (each a single diagonal structure in the IMAT), obtain the neuronal composition and the order of activation, producing the final neuronal activity patterns as output."""@en ; + skos:prefLabel "execute ASSET analysis"@en . + + +### http://purl.org/neao/steps#ExecuteCellAssemblyDetectionAnalysis +:ExecuteCellAssemblyDetectionAnalysis rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPatternDetectionAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "execute CAD analysis"@en ; + neao_base:hasBibliographicReference neao_bib:Russo2017_e19428 ; + rdfs:comment "A neuronal activity pattern detection analysis that uses the Cell Assembly Detection (CAD) method as defined in Russo & Durstewitz (2017). CAD allows detecting spatio-temporal spike patterns at different time scales, levels of precision, and with arbitrary internal organization. The analysis identifies patterns with different delays between the spikes (within a window determining the minimum and maximum lags), and is performed in two steps using an agglomerative clustering algorithm. First, significant pairwise correlations are identified, which is followed by the clustering procedure that progressively finds interactions of higher order. At each agglomeration step, the method can filter out patterns involving the same neurons, keeping the most significant pattern (significance pruning). In an additional pruning step, assemblies part of a larger assembly can also be eliminated (controlled by the subgroup pruning parameter). The algorithm stops when the detected assemblies reach their maximum size (determined by a parameter). The statistical test assumes independence under non-stationarity and Poisson distribution of the input spike trains."@en ; + skos:prefLabel "execute Cell Assembly Detection analysis"@en . + + +### http://purl.org/neao/steps#ExecuteNon3dSPADEAnalysis +:ExecuteNon3dSPADEAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SPADEAnalysis ; + neao_base:hasBibliographicReference neao_bib:Quaglio2017_41 ; + rdfs:comment "A SPADE analysis that uses the SPADE implementation as defined in Quaglio et al. (2017). This is the extension of SPADE to consider spatio-temporal patterns (i.e., patterns not restricted to synchronous spiking). In the non-3d SPADE analysis, the pattern signature used for statistical testing considers only the size and number of occurrences of a pattern."@en ; + skos:prefLabel "execute non-3d-SPADE analysis"@en . + + +### http://purl.org/neao/steps#ExecuteUnitaryEventAnalysisAnalytical +:ExecuteUnitaryEventAnalysisAnalytical rdf:type owl:Class ; + rdfs:subClassOf :UnitaryEventAnalysis ; + owl:disjointWith :ExecuteUnitaryEventAnalysisMonteCarlo ; + neao_base:abbreviation "execute UE analysis (analytical method)"@en ; + neao_base:hasBibliographicReference neao_bib:Gruen1999_67 , + neao_bib:Gruen2002_43 , + neao_bib:Gruen2002_81 , + neao_bib:Gruen2003_335 ; + rdfs:comment "A unitary event (UE) analysis that uses the analytical approach to determine the significance of the empirical coincidences in binned spike train data, as defined in Grün et al. (1999, 2002a, 2002b, and 2003). The analytical method tests if the number of empirical coincidences is consistent with the coincidence distribution resulting from independent processes. This distribution can be expressed analytically assuming that the input spike trains follow Poisson statistics. The UEs can be determined trial by trial, where the analytical expectancy is computed for each trial and then summed over all trials, or by averaging over all trials (according to Grün, 2003)."@en ; + skos:prefLabel "execute Unitary Event analysis (analytical method)"@en . + + +### http://purl.org/neao/steps#ExecuteUnitaryEventAnalysisMonteCarlo +:ExecuteUnitaryEventAnalysisMonteCarlo rdf:type owl:Class ; + rdfs:subClassOf :UnitaryEventAnalysis ; + neao_base:abbreviation "execute UE analysis (Monte Carlo method)"@en ; + neao_base:hasBibliographicReference neao_bib:Gruen2009_1126 ; + rdfs:comment "A unitary event (UE) analysis that uses a Monte Carlo approach based on spike train surrogates to determine the significance of the empirical coincidences in binned spike train data, according to Grün (2009). The Monte Carlo method does not rely on the assumption that the input spike data follows Poisson statistics. For the assessment of significance, the distribution of expected coincidences is determined by surrogates (spike train randomization) in each trial, and then summed over trials. The number of surrogates is determined by a parameter."@en ; + skos:prefLabel "execute Unitary Event analysis (Monte Carlo method)"@en . + + +### http://purl.org/neao/steps#FieldFieldCouplingAnalysis +:FieldFieldCouplingAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FieldFieldCouplingPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + owl:disjointWith :SpikeSpikeCouplingAnalysis ; + rdfs:comment "An analysis step that computes interactions between the neural activity represented by distinct local field potential (LFP) signals (e.g., LFP obtained from different electrodes, or distinct LFP frequency bands)."@en ; + skos:prefLabel "field-field coupling analysis"@en . + + +### http://purl.org/neao/steps#FiniteImpulseResponseFiltering +:FiniteImpulseResponseFiltering rdf:type owl:Class ; + rdfs:subClassOf :DigitalFiltering ; + neao_base:abbreviation "FIR filtering"@en ; + rdfs:comment "A digital filtering that uses a filter whose impulse response (i.e., the response of the filter to a brief input impulse) decays to zero after a finite amount of time. Therefore, the output of the filter depends on a finite number of past samples. The finite impulse response (FIR) filters are stable and can be designed such that they do not distort the phase of the signal. However, they have a higher computational cost."@en ; + skos:prefLabel "finite impulse response filtering"@en . + + +### http://purl.org/neao/steps#FiringRateAnalysis +:FiringRateAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step used to compute a measure quantifying the firing rate of one or more neurons. The firing rate is the number of action potential (spikes) that a neuron fires per time unit and is defined with a unit of frequency (e.g., Hz or spikes/s)."@en ; + skos:prefLabel "firing rate analysis"@en . + + +### http://purl.org/neao/steps#FrequencyDomainAnalysis +:FrequencyDomainAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + owl:disjointWith :TimeDomainAnalysis ; + rdfs:comment "An analysis step that analyzes the input(s) with respect to its(their) frequency content."@en ; + skos:prefLabel "frequency domain analysis"@en . + + +### http://purl.org/neao/steps#FrequencyDomainPairwiseGrangerCausalityAnalysis +:FrequencyDomainPairwiseGrangerCausalityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PairwiseGrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:hasBibliographicReference neao_bib:Geweke1982_304 ; + rdfs:comment "A pairwise Granger causality (GC) analysis that computes measures of GC between two inputs in the frequency domain. This is an extension of the GC concept in the time domain, and the measures of GC are obtained for the different frequency components of the inputs, according to Geweke (1982). The computation of the frequency-domain GC measures is based on two elements: the noise covariance matrix and the spectral transfer matrix. These can be estimated either with parametric or non-parametric methods. For the parametric estimation, an autoregressive model is fit and the Fourier transform of the autoregressive coefficients is used to obtain the spectral transfer matrix. For the non-parametric estimation, the cross-spectral density (CSD) matrix is obtained (using methods for CSD estimation such as multitapering or wavelet), and the CSD matrix is factorized to obtain the noise covariance and spectral transfer matrices."@en ; + skos:prefLabel "frequency domain pairwise granger causality analysis"@en . + + +### http://purl.org/neao/steps#FrequencyDomainTransformation +:FrequencyDomainTransformation rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that converts a time series input from the time to the frequency domain, i.e., reveal the different frequency components that make up the original signal."@en ; + skos:prefLabel "frequency domain transformation"@en . + + +### http://purl.org/neao/steps#FunctionalConnectivityAnalysis +:FunctionalConnectivityAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:abbreviation "FCA"@en ; + rdfs:comment "An analysis step that computes measures of functional connectivity. Functional connectivity refers to statistical dependencies and patterns of synchronization between the neural activity that indicate the functional interactions and co-activations that are relevant for the function of the nervous system (e.g., the interactions between different brain regions). It does not imply direct physical connections."@en ; + skos:prefLabel "functional connectivity analysis"@en . + + +### http://purl.org/neao/steps#GenerateCompoundPoissonProcess +:GenerateCompoundPoissonProcess rdf:type owl:Class ; + rdfs:subClassOf :CorrelatedSpikeTimesGeneration ; + owl:disjointWith :GenerateSingleInteractionProcess ; + dcterms:bibliographicCitation neao_bib:Staude2010_327 ; + rdfs:comment "A correlated spike times generation that produces spike trains using a compound Poisson process (CPP) according to Staude et al. (2010). The CPP is a model for parallel and correlated processes with Poisson spiking statistics at predefined firing rates."@en ; + skos:altLabel "generate CPP"@en ; + skos:prefLabel "generate compound Poisson process"@en . + + +### http://purl.org/neao/steps#GenerateISIDitheringSurrogate +:GenerateISIDitheringSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:abbreviation "generate ISI-D surrogate"@en ; + neao_base:hasBibliographicReference neao_bib:Stella2022_ENEURO ; + rdfs:comment "A spike train surrogate generation step where each spike is displaced according to the interspike interval (ISI) distribution sampled from the input spike train."@en ; + skos:prefLabel "generate joint interspike interval dithering surrogate"@en . + + +### http://purl.org/neao/steps#GenerateISIShufflingSurrogate +:GenerateISIShufflingSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Louis2010_359 ; + rdfs:comment "A spike train surrogate generation step where the interspike intervals (ISIs) of the input spike train is randomly sorted. This preserves the ISI distribution and spike count as in the original spike train input, but destroys temporal dependencies and firing rate profile."@en ; + skos:prefLabel "generate interspike interval shuffling surrogate"@en . + + +### http://purl.org/neao/steps#GenerateJointISIDitheringSurrogate +:GenerateJointISIDitheringSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:abbreviation "generate JISI-D surrogate"@en ; + neao_base:hasBibliographicReference neao_bib:Louis2010_127 ; + rdfs:comment "A spike train surrogate generation step where spikes from adjacent interspike intervals (ISIs) are dithered according to the joint-ISI (JISI) probability distribution. The distribution is obtained from the input spike train by computing the JISI histogram (i.e., a two-dimensional histogram that shows the frequency of ISIs with a given duration that are immediately followed by intervals with another duration). Due to non-stationarities in the input spike train and/or its limited duration, it is difficult to accurately estimate the underlying JISI probability distribution. Therefore, a 2D-Gaussian smoothing is applied to the JISI histogram (with a variance determined by parameter). Dithering a spike according to this (smoothed) two-dimensional histogram involves moving the spike along the anti-diagonal of the JISI distribution. The dithering time is defined by a parameter."@en ; + skos:prefLabel "generate joint interspike interval dithering surrogate"@en . + + +### http://purl.org/neao/steps#GenerateMorletWavelet +:GenerateMorletWavelet rdf:type owl:Class ; + rdfs:subClassOf :DataGeneration ; + rdfs:comment "A data generation that constructs a Morlet wavelet considering the selected parameters (i.e., sampling frequency, fundamental frequency, and number of cycles per frequency). The Morlet wavelet is composed by a complex exponential multiplied by a Gaussian window. The output data contains the discrete time points and the corresponding wavelet values."@en ; + skos:prefLabel "generate Morlet wavelet"@en . + + +### http://purl.org/neao/steps#GenerateNonStationaryGammaProcess +:GenerateNonStationaryGammaProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + rdfs:comment "A random spike times generation that uses a gamma probability distribution to produce spike trains where the firing rate varies over time."@en ; + skos:prefLabel "generate non-stationary gamma process"@en . + + +### http://purl.org/neao/steps#GenerateNonStationaryPoissonProcess +:GenerateNonStationaryPoissonProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + rdfs:comment "A random spike times generation that uses a Poisson probability distribution to produce spike trains where the firing rate varies over time. A dead time can be specified as parameter (i.e., an interval where no spikes can occur, which is analogous to the neuronal refractory period)."@en ; + skos:prefLabel "generate non-stationary Poisson process"@en . + + +### http://purl.org/neao/steps#GenerateSingleInteractionProcess +:GenerateSingleInteractionProcess rdf:type owl:Class ; + rdfs:subClassOf :CorrelatedSpikeTimesGeneration ; + neao_base:hasBibliographicReference neao_bib:Kuhn2003_67 ; + rdfs:comment "A correlated spike times generation that produces a multidimensional Poisson single interaction process (SIP) plus independent Poisson processes, according to Kuhn et al. (2003). The Poisson SIP consists of Poisson time series that are independent except for events that are simultaneous in all of them."@en ; + skos:altLabel "generate SIP"@en ; + skos:prefLabel "generate single interaction process"@en . + + +### http://purl.org/neao/steps#GenerateSpikeTimeRandomizationSurrogate +:GenerateSpikeTimeRandomizationSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Louis2010_359 ; + rdfs:comment "A spike train surrogate generation step that keeps the spike count of the input spike train, but the spike times in the surrogate spike train output are randomly chosen within the duration interval of the input spike train."@en ; + skos:prefLabel "generate spike time randomization surrogate"@en . + + +### http://purl.org/neao/steps#GenerateSpikeTrainDitheringSurrogate +:GenerateSpikeTrainDitheringSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Louis2010_359 ; + rdfs:comment "A spike train surrogate generation step that displaces the whole input spike train by a random amount of time (independent for each surrogate generated). The amount of displacement is specified as a parameter (dither time), and occurs in a window (-dither time, dither time). This surrogate maintains the ISIs and the temporal correlations within the spike train."@en ; + skos:prefLabel "generate spike train dithering surrogate"@en . + + +### http://purl.org/neao/steps#GenerateStationaryGammaProcess +:GenerateStationaryGammaProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + rdfs:comment "A random spike times generation that uses a gamma probability distribution to produce spike trains with a constant firing rate."@en ; + skos:prefLabel "generate stationary gamma process"@en . + + +### http://purl.org/neao/steps#GenerateStationaryInverseGaussianProcess +:GenerateStationaryInverseGaussianProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + rdfs:comment "A random spike times generation that uses a inverse Gaussian probability distribution to produce spike trains with a constant firing rate."@en ; + skos:prefLabel "generate stationary inverse Gaussian process"@en . + + +### http://purl.org/neao/steps#GenerateStationaryLogNormalProcess +:GenerateStationaryLogNormalProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + rdfs:comment "A random spike times generation that uses a log-normal probability distribution to produce spike trains with a constant firing rate."@en ; + skos:prefLabel "generate stationary log-normal process"@en . + + +### http://purl.org/neao/steps#GenerateStationaryPoissonProcess +:GenerateStationaryPoissonProcess rdf:type owl:Class ; + rdfs:subClassOf :RandomSpikeTimesGeneration ; + neao_base:hasBibliographicReference neao_bib:Deger2012_443 ; + rdfs:comment "A random spike times generation that uses a Poisson probability distribution to produce spike trains with a constant firing rate. A dead time can be specified as parameter (i.e., an interval where no spikes can occur, which is analogous to the neuronal refractory period)."@en ; + skos:prefLabel "generate stationary Poisson process"@en . + + +### http://purl.org/neao/steps#GenerateTrialShiftingSurrogate +:GenerateTrialShiftingSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Stella2022_ENEURO ; + rdfs:comment "A spike train surrogate generation step that shifts the entire spike train (containing the data of a single experimental trial) by an amount randomly chosen from a uniform distribution. The amount of displacement is specified as a parameter (dither time), and occurs in a window (-dither time, dither time). The input is a collection of spike trains of the same neuron, containing the spiking activity during different experimental trials. The amount of shift is independently chosen across trials and neurons. This surrogate preserves the ISI distribution and temporal correlations within the single-trial spike train."@en ; + skos:prefLabel "generate trial shifting surrogate"@en . + + +### http://purl.org/neao/steps#GenerateTrialShufflingSurrogate +:GenerateTrialShufflingSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Louis2010_359 ; + rdfs:comment "A spike train surrogate generation step where the the spike trains of single-trial activity of one of the neurons are randomly permuted, so that each trial is no longer paired with the corresponding trial of the other neuron, but with a randomly selected one. The input is a collection of spike trains with multitrial activity data of multiple neurons recorded in parallel."@en ; + skos:prefLabel "generate trial shuffling surrogate"@en . + + +### http://purl.org/neao/steps#GenerateUniformSpikeDitheringSurrogate +:GenerateUniformSpikeDitheringSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:abbreviation "generate UD surrogate"@en ; + neao_base:hasBibliographicReference neao_bib:Louis2010_359 ; + rdfs:comment "A spike train surrogate generation step that displaces each spike time in the input spike train according to a uniform distribution centered on the spike. The displacement occurs in a time window around the spike defined by a parameter (dither time)."@en ; + skos:prefLabel "generate uniform spike dithering surrogate"@en . + + +### http://purl.org/neao/steps#GenerateUniformSpikeDitheringSurrogateWithDeadTime +:GenerateUniformSpikeDitheringSurrogateWithDeadTime rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:abbreviation "generate UDD surrogate"@en ; + neao_base:hasBibliographicReference neao_bib:Stella2022_ENEURO ; + rdfs:comment "A spike train surrogate generation step that displaces each spike time in the input spike train according to a uniform distribution centered on the spike. The displacement occurs in a time window around the spike defined by a parameter (dither time) that is constrained to the intervals between adjacent spikes to avoid two spikes closer than a dead time (specified by parameter). This mimics the refractory period behavior of neurons, where the neuron cannot fire additional spikes for a short interval after one spike."@en ; + skos:prefLabel "generate uniform spike dithering surrogate with dead time"@en . + + +### http://purl.org/neao/steps#GenerateWindowShufflingSurrogate +:GenerateWindowShufflingSurrogate rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainSurrogateGeneration ; + neao_base:hasBibliographicReference neao_bib:Stella2022_ENEURO ; + rdfs:comment "A spike train surrogate generation step that shuffles the entries of a binned spike train within exclusive maximal displacement windows. The maximal displacement is specified by parameter, and represents the maximum number of bins that a spike can be displaced within the window."@en ; + skos:altLabel "generate bin shuffling within exclusive windows surrogate"@en ; + skos:prefLabel "generate window shuffling surrogate"@en . + + +### http://purl.org/neao/steps#GrangerCausalityAnalysis +:GrangerCausalityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :FunctionalConnectivityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "GC analysis"@en ; + rdfs:comment "A functional connectivity analysis that computes measures of Granger causality (GC). GC is a statistical concept where the future values of a time series are predicted based on its past values and the past values of other time series. GC quantifies bi-directional interactions between the inputs, determining the directional influence from one input to another. For example, with two inputs, GC measures how much the first input influences the second and vice versa (directional GC measure). This provides estimates of the directed connectivity between the inputs. It is also possible to compute associated measures, such as the instantaneous GC (a measure of interdependence between the inputs not accounted by their bi-directional interactions, such as shared neural input) and the total interdependence (the sum of all directional and instantaneous interactions between the inputs). The analysis can be performed in the time or frequency domains, and can take two (bivariate) or more inputs (multivariate)."@en ; + skos:prefLabel "Granger causality analysis"@en . + + +### http://purl.org/neao/steps#InfiniteImpulseResponseFiltering +:InfiniteImpulseResponseFiltering rdf:type owl:Class ; + rdfs:subClassOf :DigitalFiltering ; + neao_base:abbreviation "IIR filtering"@en ; + rdfs:comment "A digital filtering that uses a filter whose impulse response (i.e., the response of the filter to a brief input impulse) persists infinitely. Therefore, the output of the filter can depend on an infinite number of past samples. The infinite impulse response (IIR) filters can become unstable and distort the phase of the signal, but have a lower computational cost."@en ; + skos:prefLabel "infinite impulse response filtering"@en . + + +### http://purl.org/neao/steps#InstantaneousFiringRateAnalysis +:InstantaneousFiringRateAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :InstantaneousFiringRatePurpose + ] ; + rdfs:subClassOf :FiringRateAnalysis ; + rdfs:comment "A firing rate analysis that computes the instantaneous firing rate, which is the estimate of the firing rate at a specific point in time. The instantaneous firing rate value can be obtained by several methods."@en ; + skos:prefLabel "instantaneous firing rate analysis"@en . + + +### http://purl.org/neao/steps#InterspikeIntervalAnalysis +:InterspikeIntervalAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + neao_base:abbreviation "ISI analysis"@en ; + rdfs:comment "An analysis step that computes or analyzes the interval between successive spikes in a spike train (interspike interval; ISI)."@en ; + skos:prefLabel "interspike interval analysis"@en . + + +### http://purl.org/neao/steps#InterspikeIntervalVariabilityAnalysis +:InterspikeIntervalVariabilityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :InterspikeIntervalAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :NeuronalFiringRegularityPurpose + ] ; + rdfs:comment "An interspike interval analysis that computes a measure describing the variability of the interspike intervals. The measure can assess how regular a neuron is firing."@en ; + skos:prefLabel "interspike interval variability analysis"@en . + + +### http://purl.org/neao/steps#KernelSmoothing +:KernelSmoothing rdf:type owl:Class ; + rdfs:subClassOf :DataSmoothing ; + rdfs:comment "A data smoothing that performs a convolution of the input data with a kernel function. This computes a weighted average of the data around the kernel. Several kernel types can be used for the smoothing (e.g., Gaussian, exponential) and the kernel shape is controlled by a width parameter."@en ; + skos:prefLabel "kernel smoothing"@en . + + +### http://purl.org/neao/steps#LatentDynamicsAnalysis +:LatentDynamicsAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :LatentDynamicsPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that aims to identify underlying patterns and structures within time series or sequential data that are not directly observable. It involves modeling hidden (latent) variables that influence the observed data and their evolution over time. The analysis captures temporal dependencies and dynamics within the data, providing insights into the processes that generate the observed sequences."@en ; + skos:prefLabel "latent dynamics analysis"@en . + + +### http://purl.org/neao/steps#LineNoiseRemoval +:LineNoiseRemoval rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that removes noise induced by the power line."@en ; + skos:altLabel "line noise cancellation"@en ; + skos:prefLabel "line noise removal"@en . + + +### http://purl.org/neao/steps#MeanVectorLengthAnalysis +:MeanVectorLengthAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PhaseAmplitudeCouplingAnalysis ; + neao_base:abbreviation "MVL analysis"@en ; + rdfs:comment "A phase-amplitude coupling (PAC) analysis that computes the mean vector length (MVL) measure. The MVL is based on a mean vector obtained from a time series defined in the complex plane, where the amplitude is taken from the high-frequency oscillation input and the phase from the low-frequency oscillation input."@en ; + skos:prefLabel "mean vector length analysis"@en . + + +### http://purl.org/neao/steps#ModelBasedAnalysis +:ModelBasedAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that depends on assumptions on the interactions between the inputs to perform the computations. For example, the Granger causality analysis assumes linear relationships between the input signals."@en ; + skos:prefLabel "model-based analysis"@en . + + +### http://purl.org/neao/steps#ModelFreeAnalysis +:ModelFreeAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "false"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that does not depend on assumptions on the interactions between the inputs to perform the computations. For example, it can consider probability distributions obtained from the input data."@en ; + skos:prefLabel "model-free analysis"@en . + + +### http://purl.org/neao/steps#MultivariateAnalysis +:MultivariateAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isMultivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that has three or more distinct inputs considered for the computation of the output (e.g., the time series with the local field potential signals recorded from three or more electrodes, used to compute the partial directed coherence)."@en ; + skos:prefLabel "multivariate analysis"@en . + + +### http://purl.org/neao/steps#NeuronalActivityPatternDetectionAnalysis +:NeuronalActivityPatternDetectionAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :isMultivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "An analysis step that aims to identify a neuronal activity pattern, i.e., spikes of a group of neurons that occur in a specific spatio-temporal configuration."@en ; + skos:prefLabel "neuronal activity pattern detection analysis"@en . + + +### http://purl.org/neao/steps#NeuronalFiringRegularityAnalysis +:NeuronalFiringRegularityAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :NeuronalFiringRegularityPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that computes measures to assess the regularity in the firing of a neuron. Neuronal firing regularity refers to the consistency or variability in the timing of action potentials (spikes) generated by a neuron."@en ; + skos:altLabel "spike time variability analysis"@en ; + skos:prefLabel "neuronal firing regularity analysis"@en . + + +### http://purl.org/neao/steps#NonDirectedAnalysis +:NonDirectedAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step where the output does not provide information on the direction of influence in the relationships among the inputs (e.g., in the Pearson correlation coefficient computed between two spike trains, it is possible to know how strongly they tend to fire together. However, it is not possible to analyze the timing of the spikes of the first input spike with respect to the timing of the spikes of the second input spike train)."@en ; + skos:prefLabel "non-directed analysis"@en . + + +### http://purl.org/neao/steps#PairwiseGrangerCausalityAnalysis +:PairwiseGrangerCausalityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :GrangerCausalityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A Granger causality (GC) analysis that computes a measure of GC between two inputs."@en ; + skos:prefLabel "pairwise Granger causality analysis"@en . + + +### http://purl.org/neao/steps#PeristimulusTimeHistogramAnalysis +:PeristimulusTimeHistogramAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainTimeHistogramAnalysis ; + neao_base:abbreviation "PSTH analysis"@en ; + rdfs:comment "A spike train time histogram analysis that computes the peristimulus time histogram (PSTH). PSTH is the time histogram of two or more spike trains containing repeated recordings of a single neuron around the time when an event of interest occurred. The event of interest can occur at any time point during the duration of the source spike trains. The distribution of the histogram corresponds to the distribution of the activity of the neuron with respect to the event across the repeated recordings. The event of interest can be an externally presented stimulus or a spontaneous behavioral event. If the histogram represents the neuronal activity after the stimulus presentation or event occurrence, this can be referred as post-stimulus time histogram. Conversely, if the histogram shows the activity of the neuron before the stimulus presentation or event, this can be referred as prestimulus time histogram. Finally, if the histogram considers a behavioral event (or an event that is not an externally presented stimulus), the histogram can be referred to as perievent time histogram (PETH)."@en ; + skos:prefLabel "peristimulus time histogram analysis"@en . + + +### http://purl.org/neao/steps#PhaseAmplitudeCouplingAnalysis +:PhaseAmplitudeCouplingAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FieldFieldCouplingPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "PAC analysis"@en ; + rdfs:comment "A phase analysis that computes measures describing how the phase of a low-frequency oscillation modulates the amplitude of a high-frequency oscillation. Phase-amplitude coupling (PAC) can be used to investigate interactions between different frequency bands in the neural activity."@en ; + skos:prefLabel "phase-amplitude coupling analysis"@en . + + +### http://purl.org/neao/steps#PhaseAnalysis +:PhaseAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that computes measures related to the phase in the input data. Phase expresses the position of a time-varying signal relative to a fixed reference point in time. For periodic and oscillatory signals (e.g., a sine waveform), phase analysis involves determining the angle on the unit circle that corresponds to the current position within the waveform's cycle. This helps understanding the timing and synchronization of the oscillations in the input data."@en ; + skos:prefLabel "phase analysis"@en . + + +### http://purl.org/neao/steps#PhaseLagIndexAnalysis +:PhaseLagIndexAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "PLI analysis"@en ; + rdfs:comment "A phase analysis that computes the phase lag index (PLI). The PLI measures the asymmetry of the distribution of the phase differences between two input time series, i.e., if there is an imbalance in the likelihood of the first time series leading or lagging the second time series. It is designed to be invariant to common sources, such as volume conduction and/or active reference electrodes."@en ; + skos:prefLabel "phase lag index analysis"@en . + + +### http://purl.org/neao/steps#PhaseLockingValueAnalysis +:PhaseLockingValueAnalysis rdf:type owl:Class ; + rdfs:subClassOf :PhaseAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isModelBased ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "PLV analysis"@en ; + rdfs:comment "A phase analysis that computes the phase locking value (PLV). The PLV quantifies the consistency of the phase difference between two input time series across time (e.g., multiple experimental trials)."@en ; + skos:prefLabel "phase locking value analysis"@en . + + +### http://purl.org/neao/steps#PowerSpectralDensityAnalysis +:PowerSpectralDensityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralDensityAnalysis ; + neao_base:abbreviation "PSD analysis"@en ; + rdfs:comment "A spectral density analysis that computes the power spectral density of an input, i.e., the distribution of power across the different frequency components of the input signal per unit frequency. It is equivalent to the Fourier transform of the autocorrelation function of the input signal. The computed power spectral density values can be corrected depending on the analysis returning the two-sided (i.e., with negative frequencies) or one-sided (i.e., positive frequencies only) PSD. The PSD is often referred to as spectrum."@en ; + skos:altLabel "auto spectral density analysis"@en ; + skos:prefLabel "power spectral density analysis"@en . + + +### http://purl.org/neao/steps#PrincipalComponentAnalysis +:PrincipalComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReduction ; + neao_base:abbreviation "PCA"@en ; + rdfs:comment "A dimensionality reduction that reduces the dimensionality of the input data represented as a matrix with numerous rows and columns. It transforms the data into a set of principal components (PCs) that capture the maximum variance in the input. Each PC is a linear combination of the original variables and serves as a new axis in a lower-dimensional space. The PCs are orthogonal to each other, meaning they capture independent aspects of the input data's variability."@en ; + skos:prefLabel "principal component analysis"@en . + + +### http://purl.org/neao/steps#RandomSpikeTimesGeneration +:RandomSpikeTimesGeneration rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainGeneration ; + rdfs:comment "A spike train generation where the output spike trains will have random spike times, taken from a specific probability distribution. The generation process can produce spike trains where the firing rate is constant (stationary) or varies (non-stationary) over time."@en ; + skos:prefLabel "random spike times generation"@en . + + +### http://purl.org/neao/steps#Resampling +:Resampling rdf:type owl:Class ; + rdfs:subClassOf :DataTransformation ; + rdfs:comment "A data transformation that changes the number of samples in the input data."@en ; + skos:prefLabel "resampling"@en . + + +### http://purl.org/neao/steps#SPADEAnalysis +:SPADEAnalysis rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPatternDetectionAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + rdfs:comment """A neuronal activity pattern detection analysis that uses the Spatio-temporal PAttern Detection and Evaluation (SPADE) method. The SPADE analysis takes a set of parallel spike trains as input, and returns significant spatio-temporal neuronal activity (spike) patterns. + +The SPADE method consists of three substeps: + +1. Detect all putative patterns in the input data using the frequent item set mining (FIM) algorithm. This step requires the discretization of the input spike train data (binning). The bin size determines the temporal resolution of the analysis. + +2. The detected FIM patterns are evaluated for statistical significance, considering the null hypothesis of independence of the spike trains given the modulations by the firing rate. This substep is called Pattern Spectrum Filtering (PSF). For the testing, the patterns are pooled based on their signature: size and occurrence count (non-3d-SPADE) or size, occurrence count and pattern duration (3d-SPADE). The pattern spectrum collects the counts of patterns from each signature. The statistical test is done by a Monte Carlo approach, using spike train surrogates generated from the original data. The final output of this substep is the p-value spectrum, which has the same dimensions as the pattern spectrum. The p-value is computed as the ratio of surrogates containing patterns with that signature to the total number of realizations. + +3. Conditional test on the significant patterns to remove patterns arising from the overlap of true pattern spikes and chance spikes (pattern set reduction; PSR)."""@en ; + skos:prefLabel "SPADE analysis"@en . + + +### http://purl.org/neao/steps#SpectralAnalysis +:SpectralAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that computes measures describing the input data with respect to its frequency contents."@en ; + skos:prefLabel "spectral analysis"@en . + + +### http://purl.org/neao/steps#SpectralDensityAnalysis +:SpectralDensityAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralAnalysis ; + rdfs:comment "A spectral analysis that computes the density of a measure of the input(s) over the frequency spectrum. Density means that the measure value (e.g., power) for each frequency component is expressed per unit frequency. For example, for an input time series with voltages recorded from an electrode (measured in V), the power for each frequency component of the signal will be in V**2, while the power density will be in V**2/Hz. Therefore, the power values are normalized per unit frequency. This normalization allows for consistent comparisons of results from analyses with different frequency resolutions, as the spectral density remains unaffected by these variations."@en ; + skos:prefLabel "spectral density analysis"@en . + + +### http://purl.org/neao/steps#SpectrogramAnalysis +:SpectrogramAnalysis rdf:type owl:Class ; + rdfs:subClassOf :TimeFrequencyAnalysis ; + rdfs:comment "A time-frequency analysis that shows the power (or power density) of different frequency components of the input(s) as they change over time. This can be obtained for a single input (spectrogram) or for two distinct inputs (cross-spectrogram)."@en ; + skos:prefLabel "spectrogram analysis"@en . + + +### http://purl.org/neao/steps#SpikeFieldCoherenceAnalysis +:SpikeFieldCoherenceAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :FunctionalConnectivityPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeFieldCouplingPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isFrequencyDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + neao_base:abbreviation "SFC analysis"@en ; + rdfs:comment "An analysis step that computes the spike-field coherence (SFC), which is the coherence computed between an input spike train and an input time series with the local field potential (LFP). Coherence is a measure of the association between the two inputs in the frequency domain. SFC can be used to quantify the relationship between the spiking activity of neurons and the oscillatory activity in the LFP. It represents the similarity of dynamics between the spike train and the voltage fluctuations produced by the neural activity in the local environment where the spiking activity was recorded."@en ; + skos:prefLabel "spike-field coherence analysis"@en . + + +### http://purl.org/neao/steps#SpikeFieldCouplingAnalysis +:SpikeFieldCouplingAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeFieldCouplingPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + owl:disjointWith :SpikeSpikeCouplingAnalysis ; + rdfs:comment "An analysis step that computes interactions between the spiking activity of neurons (individual or population) and the local field potential (LFP)."@en ; + skos:prefLabel "spike-field coupling analysis"@en . + + +### http://purl.org/neao/steps#SpikeSpikeCouplingAnalysis +:SpikeSpikeCouplingAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :SpikeSpikeCouplingPurpose + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that computes interactions between the spiking activity of one or more neurons."@en ; + skos:prefLabel "spike-spike coupling analysis"@en . + + +### http://purl.org/neao/steps#SpikeTrainCorrelationAnalysis +:SpikeTrainCorrelationAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :CorrelationPurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "An analysis step that computes measures estimating the correlation between spike train inputs. The correlation value is a normalized measure of covariation in the input spike train data, and reflects the strength and direction of the association: positive values mean that the inputs vary in the same direction, and negative values mean that the inputs vary in opposite directions (e.g., if the activity in one spike train increases, it decreases in the other)."@en ; + skos:prefLabel "spike train correlation analysis"@en . + + +### http://purl.org/neao/steps#SpikeTrainDissimilarityAnalysis +:SpikeTrainDissimilarityAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that computes a measure comparing spike train inputs and providing an estimation of their similarity/dissimilarity. This is frequently done by computing spike train distances, which are measures that assign the notion of distance, i.e., the input spike trains are considered as elements in a space and, if similar, will be close together."@en ; + skos:prefLabel "spike train dissimilarity analysis"@en . + + +### http://purl.org/neao/steps#SpikeTrainGeneration +:SpikeTrainGeneration rdf:type owl:Class ; + rdfs:subClassOf :DataGeneration , + [ rdf:type owl:Restriction ; + owl:onProperty neao_base:hasOutput ; + owl:allValuesFrom neao_data:ArtificialData + ] ; + rdfs:comment "A data generation that produces one or more artificial spike trains using distinct statistical procedures to determine the spike times."@en ; + skos:prefLabel "spike train generation"@en . + + +### http://purl.org/neao/steps#SpikeTrainSurrogateGeneration +:SpikeTrainSurrogateGeneration rdf:type owl:Class ; + rdfs:subClassOf :DataGeneration ; + rdfs:comment "A data generation that produces one or more spike train surrogates. A spike train surrogate is a new spike train derived from an input spike train (usually experimentally recorded). This is done using methods that alter the original spike times while trying to maintain specific statistical features of the original spike train (e.g., firing rate, interspike interval distribution). This is used to destroy fine temporal correlations in the spiking activity."@en ; + skos:prefLabel "spike train surrogate generation"@en . + + +### http://purl.org/neao/steps#SpikeTrainSynchronyAnalysis +:SpikeTrainSynchronyAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step to assess synchronization in two or more spike train inputs that typically represent the activity of different neurons. Spike train synchronization refers to the temporal coordination of action potentials (spikes) between neurons, and describes the degree to which their spikes tend to occur at the same time."@en ; + skos:prefLabel "spike train synchrony analysis"@en . + + +### http://purl.org/neao/steps#SpikeTrainTimeHistogramAnalysis +:SpikeTrainTimeHistogramAnalysis rdf:type owl:Class ; + rdfs:subClassOf :FiringRateAnalysis ; + rdfs:comment """A firing rate analysis that computes histograms of spike train data over time. The time histogram is obtained by discretizing the duration of the spike train into distinct time intervals (bins), and obtaining the spike count inside each bin. + +The histogram can show one of three different measures: + +* the spike count at each bin (across all spike trains); +* the mean spike count per bin (spike count in the bin divided by the number of spike trains); +* the firing rate (mean spike count in the bin divided by bin width)."""@en ; + skos:prefLabel "spike train time histogram analysis"@en . + + +### http://purl.org/neao/steps#SpikeWaveformAnalysis +:SpikeWaveformAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that is used to compute measures to describe or make inferences from spike waveform input data. A spike waveform refers to the shape of an electrical signal produced by a neuron when it fires an action potential."@en ; + skos:prefLabel "spike waveform analysis"@en . + + +### http://purl.org/neao/steps#StatisticalAnalysis +:StatisticalAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "A generic analysis step that computes measures to summarize and make inferences about the data input. These include measures of central tendency, dispersion and confidence intervals. The subclasses represent analysis steps that are usually used for aggregation of data and description of samples (e.g., compute the mean and standard deviation of the output of trial-by-trial analyses or across subjects). All analysis steps for specific applications related to the analysis of neuroelectrophysiology data itself (e.g., analyzing interspike interval variability) are covered by separate, independent classes."@en ; + skos:prefLabel "statistical analysis"@en . + + +### http://purl.org/neao/steps#TensorComponentAnalysis +:TensorComponentAnalysis rdf:type owl:Class ; + rdfs:subClassOf :DimensionalityReduction ; + neao_base:abbreviation "TCA"@en ; + rdfs:comment "A dimensionality reduction that reduces the dimensionality of the input data represented as a tensor (i.e., an array with multiple dimensions). The tensor component analysis (TCA) transforms the data into a set of low-dimensional tensors that capture the maximum variance in the input (tensor components)."@en ; + skos:altLabel "tensor principal component analysis"@en ; + skos:prefLabel "tensor component analysis"@en . + + +### http://purl.org/neao/steps#TimeDomainAnalysis +:TimeDomainAnalysis rdf:type owl:Class ; + owl:equivalentClass [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step that analyzes the input(s) with respect to time."@en ; + skos:prefLabel "time domain analysis"@en . + + +### http://purl.org/neao/steps#TimeFrequencyAnalysis +:TimeFrequencyAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpectralAnalysis ; + rdfs:comment "A spectral analysis that computes measures describing the frequency content of the input(s) in a time-resolved manner. It allows the analysis of how different frequency components evolve over time, which is essential for non-stationary signals whose spectral characteristics change. The joint time-frequency representation helps in identifying transient features, frequency shifts, and other dynamic behaviors in the input(s)."@en ; + skos:prefLabel "time-frequency analysis"@en . + + +### http://purl.org/neao/steps#TimeScaleDependentSpikeTrainDistanceAnalysis +:TimeScaleDependentSpikeTrainDistanceAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDissimilarityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :DistancePurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A spike train dissimilarity analysis that computes a spike train distance that depends on a parameter that determines a temporal scale in the spike trains to which the distance is sensitive. By computing the spike train distance for different time scale parameter values, it is possible to make inferences on the time scale that is discriminative in the neural activity."@en ; + skos:prefLabel "time-scale dependent spike train distance analysis"@en . + + +### http://purl.org/neao/steps#TimeScaleIndependentSpikeTrainDistanceAnalysis +:TimeScaleIndependentSpikeTrainDistanceAnalysis rdf:type owl:Class ; + rdfs:subClassOf :SpikeTrainDissimilarityAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :hasPurpose ; + owl:hasValue :DistancePurpose + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isBivariate ; + owl:hasValue "true"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] , + [ rdf:type owl:Restriction ; + owl:onProperty :isTimeDomain ; + owl:hasValue "true"^^xsd:boolean + ] ; + rdfs:comment "A spike train dissimilarity analysis that computes a spike train distance that does not depend on a time scale parameter and that are time-scale adaptive. They can be used in scenarios where there are no previous knowledge of the relevant time scales in the input spike trains."@en ; + skos:prefLabel "time-scale independent spike train distance analysis"@en . + + +### http://purl.org/neao/steps#TriggeredAverageAnalysis +:TriggeredAverageAnalysis rdf:type owl:Class ; + rdfs:subClassOf neao_base:AnalysisStep ; + rdfs:comment "An analysis step where a signal is averaged to obtain a value around a point in time representing an event of interest (i.e., a trigger). For each event time, a finite duration window of the input time series is selected around the event time. An average for each time point is then obtained across all windows."@en ; + skos:prefLabel "triggered average analysis"@en . + + +### http://purl.org/neao/steps#UnitaryEventAnalysis +:UnitaryEventAnalysis rdf:type owl:Class ; + rdfs:subClassOf :NeuronalActivityPatternDetectionAnalysis , + [ rdf:type owl:Restriction ; + owl:onProperty :isDirected ; + owl:hasValue "false"^^xsd:boolean + ] ; + neao_base:abbreviation "UE analysis"@en ; + neao_base:hasBibliographicReference neao_bib:Riehle1997_1950 ; + rdfs:comment "A neuronal activity pattern detection analysis that uses the Unitary Event (UE) method. UE is a statistical technique focused on identifying synchronous activity among neurons, known as unitary events (UEs), which occur more frequently than an expectation based solely on firing rates. The input can contain spike trains from one or more neurons and one or more trials. For the computation, the input spike train data is discretized into small time intervals (bins), and coincidences across the different spike trains are computed. The significance of the number of observed (empirical) coincidences is determined by comparing to an expected number given the firing rates of the neurons. To account for possible non-stationarities in the firing rates, the method uses a sliding temporal window over the data, whose width is specified as parameter. Therefore, a measure of significant spike synchrony (joint surprise) is obtained for each window. The statistical evaluation can be done using either analytical methods or Monte-Carlo testing with surrogate spike data. The output presents the significant coincidences (UE patterns) and the participant neurons."@en ; + skos:prefLabel "Unitary Event analysis"@en . + + +### http://purl.org/neao/steps#WaveletTransformAnalysis +:WaveletTransformAnalysis rdf:type owl:Class ; + rdfs:subClassOf :TimeFrequencyAnalysis ; + neao_base:hasBibliographicReference neao_bib:Farge1992_395 ; + rdfs:comment "A time-frequency analysis that uses wavelets to obtain the time-frequency representation of the time series input. The wavelet is a rapidly decaying oscillation. The wavelet transform breaks the signal into shifted and scaled versions of the wavelet (mother wavelet), and the output contains the information on both the frequencies present in the signal and the time. The wavelets provide good localization in time and frequency, making them suitable to analyze signals with transient features. Different types of wavelets exist, with distinct properties. They can be chosen to tailor the analysis to particular purposes. The output of the wavelet transform is often referred to as scaleogram."@en ; + skos:prefLabel "wavelet transform analysis"@en . + + +################################################################# +# Individuals +################################################################# + +### http://purl.org/neao/steps#CorrelationPurpose +:CorrelationPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of computing a correlation measure."@en ; + skos:prefLabel "correlation purpose"@en . + + +### http://purl.org/neao/steps#DataSmoothingPurpose +:DataSmoothingPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of smoothing the data."@en ; + skos:prefLabel "data smoothing purpose"@en . + + +### http://purl.org/neao/steps#DistancePurpose +:DistancePurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of computing a distance metric."@en ; + skos:prefLabel "distance purpose"@en . + + +### http://purl.org/neao/steps#FieldFieldCouplingPurpose +:FieldFieldCouplingPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of assessing the interactions between inputs with local field potential data."@en ; + skos:prefLabel "field-field coupling purpose"@en . + + +### http://purl.org/neao/steps#FunctionalConnectivityPurpose +:FunctionalConnectivityPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of estimating functional connectivity."@en ; + skos:prefLabel "functional connectivity purpose"@en . + + +### http://purl.org/neao/steps#InstantaneousFiringRatePurpose +:InstantaneousFiringRatePurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of estimating the instantaneous firing rate."@en ; + skos:prefLabel "instantaneous firing rate purpose"@en . + + +### http://purl.org/neao/steps#LatentDynamicsPurpose +:LatentDynamicsPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of estimating latent dynamics."@en ; + skos:prefLabel "latent dynamics purpose"@en . + + +### http://purl.org/neao/steps#NeuronalFiringRegularityPurpose +:NeuronalFiringRegularityPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of assessing the regularity in neuronal firing."@en ; + skos:prefLabel "neuronal firing regularity purpose"@en . + + +### http://purl.org/neao/steps#NeuronalSynchronizationPurpose +:NeuronalSynchronizationPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of assessing neuronal synchronization."@en ; + skos:prefLabel "neural synchronization purpose"@en . + + +### http://purl.org/neao/steps#SpikeFieldCouplingPurpose +:SpikeFieldCouplingPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of assessing interactions between spiking activity and the local field potential."@en ; + skos:altLabel "spike-LFP coupling purpose"@en ; + skos:prefLabel "spike-field coupling purpose"@en . + + +### http://purl.org/neao/steps#SpikeSpikeCouplingPurpose +:SpikeSpikeCouplingPurpose rdf:type owl:NamedIndividual , + :AnalysisPurpose ; + rdfs:comment "The analysis purpose of assessing interactions between the spiking activity of neurons."@en ; + skos:prefLabel "spike-spike coupling purpose"@en . + + +################################################################# +# Annotations +################################################################# + +:ApplyWaveformOutlierRejection rdfs:comment "An artifact removal step that aims to identify and remove input spike waveforms that differs significantly from other waveforms in the input. This include spike waveforms with too late peak or where the action potential does not rise towards the peak of other waveforms."@en . + + +################################################################# +# General axioms +################################################################# + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ApplyCanonicalPolyadicTensorDecomposition + :ApplyCoupledCanonicalPolyadicTensorDecomposition + :ApplyNonNegativeTensorComponentAnalysis + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ApplyDemixedPrincipalComponentAnalysis + :ApplyProbabilisticPrincipalComponentAnalysis + :ApplyStandardPrincipalComponentAnalysis + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeCoherenceCarter + :ComputeCoherenceMultitaper + :ComputeCoherenceRosenberg + :ComputeCoherenceWelch + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeCrossPowerSpectralDensityMorletWavelet + :ComputeCrossPowerSpectralDensityMultitaper + :ComputeCrossPowerSpectralDensityPeriodogram + :ComputeCrossPowerSpectralDensityWelch + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeCrossSpectrogramShortTimeFourierTransform + :ComputeSpectrogramMorletWavelet + :ComputeSpectrogramMultitaper + :ComputeSpectrogramShortTimeFourierTransform + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeCurrentSourceDensityICSD + :ComputeCurrentSourceDensityKCSD + :ComputeCurrentSourceDensityStandard + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeFrequencyDomainPairwiseGrangerCausalityBrovelli + :ComputeFrequencyDomainPairwiseGrangerCausalityDhamala + :ComputeFrequencyDomainPairwiseGrangerCausalityHafner + :ComputeFrequencyDomainPairwiseGrangerCausalityWen + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeInstantaneousFiringRateInterspikeInterval + :ComputeInstantaneousFiringRateKernelDensityEstimation + :ComputeInstantaneousFiringRateLocalRegression + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputePeristimulusTimeHistogramAdaptiveKernelSmoothing + :ComputePeristimulusTimeHistogramFixedKernelSmoothing + :ComputePeristimulusTimeHistogramOptimalBinSize + :ComputePeristimulusTimeHistogramUserSelectedBinSize + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputePowerSpectralDensityBartlett + :ComputePowerSpectralDensityMultitaper + :ComputePowerSpectralDensityPeriodogram + :ComputePowerSpectralDensityWelch + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :ComputeSpikeFieldCoherenceFries + :ComputeSpikeFieldCoherenceMultitaper + :ComputeSpikeFieldCoherenceWelch + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :GenerateISIDitheringSurrogate + :GenerateISIShufflingSurrogate + :GenerateJointISIDitheringSurrogate + :GenerateSpikeTimeRandomizationSurrogate + :GenerateSpikeTrainDitheringSurrogate + :GenerateTrialShiftingSurrogate + :GenerateTrialShufflingSurrogate + :GenerateUniformSpikeDitheringSurrogate + :GenerateUniformSpikeDitheringSurrogateWithDeadTime + :GenerateWindowShufflingSurrogate + ) +] . + + +[ rdf:type owl:AllDisjointClasses ; + owl:members ( :GenerateNonStationaryGammaProcess + :GenerateNonStationaryPoissonProcess + :GenerateStationaryGammaProcess + :GenerateStationaryInverseGaussianProcess + :GenerateStationaryLogNormalProcess + :GenerateStationaryPoissonProcess + ) +] . + + +### Generated by the OWL API (version 4.5.29.2024-05-13T12:11:03Z) https://github.com/owlcs/owlapi diff --git a/src/tests/catalog-v001.xml b/src/tests/catalog-v001.xml new file mode 100644 index 0000000..57d9751 --- /dev/null +++ b/src/tests/catalog-v001.xml @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/src/tests/tests.owl b/src/tests/tests.owl new file mode 100644 index 0000000..00252e9 --- /dev/null +++ b/src/tests/tests.owl @@ -0,0 +1,102 @@ +@prefix : . +@prefix owl: . +@prefix rdf: . +@prefix xml: . +@prefix xsd: . +@prefix rdfs: . +@prefix skos: . +@prefix vann: . +@prefix dcterms: . +@prefix neao_base: . +@prefix neao_data: . +@prefix neao_steps: . +@base . + + rdf:type owl:Ontology ; + owl:imports neao_base: , + neao_data: , + neao_steps: ; + [ "ASSETAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] , + [ "ComputePairwiseGrangerCausality SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] , + [ "SPADEAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "false"^^xsd:boolean + ] , + [ "ComputeInterspikeIntervals SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "false"^^xsd:boolean + ] ; + dcterms:creator "Cristiano Köhler (ORCID: 0000-0003-0503-5264)" , + "Michael Denker (ORCID: 0000-0003-1255-7300)" ; + dcterms:license ; + rdfs:comment "Test suite to check the inferences of NEAO. This module should not be used in any annotations."@en . + +################################################################# +# Annotation properties +################################################################# + +### http://ainf.aau.at/ontodebug#axiom + rdf:type owl:AnnotationProperty ; + rdfs:subPropertyOf . + + +### http://ainf.aau.at/ontodebug#testCase + rdf:type owl:AnnotationProperty . + + +### http://ainf.aau.at/ontodebug#type + rdf:type owl:AnnotationProperty ; + rdfs:subPropertyOf . + + +[ "ASSETAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "true"^^xsd:boolean +] . + +[ "ComputePairwiseGrangerCausality SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "SPADEAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "false"^^xsd:boolean + ] . + +[ "ComputePairwiseGrangerCausality SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ASSETAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ComputeInterspikeIntervals SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "SPADEAnalysis SubClassOf CompoundAnalysis"^^xsd:string ; + "false"^^xsd:boolean + ] . + +[ "ComputePairwiseGrangerCausality SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ComputeInterspikeIntervals SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ComputeInterspikeIntervals SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ComputePairwiseGrangerCausality SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "true"^^xsd:boolean + ] . + +[ "ComputeInterspikeIntervals SubClassOf FunctionalConnectivityAnalysis"^^xsd:string ; + "false"^^xsd:boolean + ] . + +### Generated by the OWL API (version 4.5.9.2019-02-01T07:24:44Z) https://github.com/owlcs/owlapi