Please cite this work if you reuse it or find it useful :
This Shiny application, part of the GlobalTunaAtlas project, is designed to facilitate the exploration and analysis of worldwide tuna fisheries data. To begin the exploration, users are invited to explore data and select desired filters. Upon submission, the application will render the selected indicators, which may include visual representations such as maps and graphs. The selection of the dataset to explore can be changed in the Dataset selection tab and more info about this app and the global project are displayed in the “More about” panel.
Please be aware that mapping features may experience delays due to data complexity and applied filters.
This Shiny application provides access to several datasets that allow for a thorough exploration of data from the Global Tuna Atlas database. Each dataset comes with a detailed explanation and a DOI identifier, which can be found in the table below. These details aim to provide you with precise context and technical insights into the accessible data.
The following dataset can be found under the following DOI. For each dataset a abstract is provided for more comprehensive choosing. Please contact us if you need more information.
| DOI | Filename |
|---|---|
| 10.5281/zenodo.14184244 | global_catch_tunaatlasird_level2 (without_geom).csv |
| 10.5281/zenodo.11460074 | global_catch_firms_level0_harmonized.csv |
Currently, we strongly advise opting for
global_catch_tunaatlasird_level2 data. This recommendation is based on
the accessibility and the richness of information these data provide,
making exploration and analysis both simpler and more rewarding.
It is crucial to note that our application does not support the display of multiple grid types simultaneously. To maximize the value of the explored information, we recommend using datasets that are already aggregated into 5-degree grids. This approach prevents the selection of a specific grid type, which could lead to a partial loss of available information.
By choosing aggregated data, you benefit from a comprehensive and consolidated view, thus facilitating the identification of trends and patterns within the data.
The repository is a work in progress intended for exploring tuna fisheries data. It contains publicly available data from Tuna RFMOs. The content should not be used for publications without explicit agreement from the authors. Accuracy of estimates depends on data quality and availability, and may not represent the official view of IRD or its affiliates. Caution must be taken when interpreting all data presented, and differences between information products published by IRD and other sources using different inclusion criteria and different data cut-off times are to be expected. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. See “Detailed explanations on the datasets used” for further background and other important considerations surrounding the source data.
This repository has been enriched and supported by the efforts and contributions of multiple individuals. Their diverse skills and dedication have been pivotal in the development and success of this project.
- Julien Barde
- Norbert Billet
- Emmanuel Chassot
- Taha Imzilen
- Paul Taconet
- Bastien Grasset
- UMR MARBEC (Marine Biodiversity, Exploitation and Conservation)
Each contributor has played a significant role in the repository’s development, offering unique insights, expertise, and dedication to the project’s growth.
Special thanks are extended to Alain Fontenau, whose original draft of a set of indicators laid the groundwork for significant portions of the analysis code. His contributions have been instrumental in guiding the project’s analytical direction. Documentation IRD on Global Tuna Atlas Global Tuna Atlas pdf
The implementation of the indicators into the R programming language was skillfully executed by Norbert Billet and Julien Barde during the iMarine FP7 project. Their expertise in R programming facilitated the translation of conceptual indicators into practical, executable code, thereby enabling robust data analysis and insights.
This shiny app as been enriched by Grasset Bastien from an original repository created by Julien Barde.
For question about the use of the shiny app or about the creation of the data of the Global Tuna Atlas, feel free to contact us on github or by email: - [email protected] - [email protected].
This document serves to recognize and appreciate the collective efforts of all individuals involved.
The app is a component of the GlobalFisheriesAtlas Virtual Research Environment (VRE), which includes an RStudio server for developers and a Shiny proxy server for hosting applications. The VRE aims to simplify data exploration through analytical indicators without the need for delving into source code. To access this inrastructure you can create an account into https://blue-cloud.d4science.org/group/globalfisheriesatlas and then access the shiny application.
Run
shiny::runApp()
The app uses the renv package for package management. Ensure
compatibility with the R version specified in the lockfile to avoid
loading errors.
A connection to a populated database with Global Tuna Atlas data and metadata is required but no mandatory to observe DOI data. Instructions for database creation or connection setup are available on the project’s GitHub page. The connection identifiers must be provided through a connection_tunaatlas_inv.txt file copied in the repository (or copied in the docker if using the docker image).
Pullling the image and running it
docker pull ghcr.io/firms-gta/tunaatlas_pie_map_shiny:latest
docker run -p 3838:3838 -v ghcr.io/firms-gta/tunaatlas_pie_map_shiny:latest If you have credential to connect the infrastructure DB or if you have replicated the database and want to access it with the shiny application
docker run -e DB_HOST=mydbhost -e DB_PORT=5432 -e DB_NAME=mydatabase -e DB_USER_READONLY=myuser -e DB_PASSWORD=mypassword tunaatlas_pie_map_shinyAccess the app by navigating to http://localhost:3838 in your browser.
If you have cloned the github repository in local you can then:
cd the_repo_where_you_pulled_the_shiny_app
docker build -t my-shiny-app .
docker run -p 3838:3838 -v my-shiny-appAccess the app by navigating to http://localhost:3838.
Every dataset need to match the CWP standard.
Create in you repo the default_dataset.qs file wihch contains your dataset in CWP standard format and in .qs format. Then run the application or create the docker image
No easy way found yet. We recommend pulling the repository and building the docker image in local (see )
This work has been supported by several funding sources, outlined below:
- Institute of Research for Development (IRD)
- The support from IRD has been crucial for conducting our research.
- iMarine FP7 Project
- Funded under the European Union’s Seventh Framework Programme (FP7).
- BlueBridge H2020 Project
- BlueBridge has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675680.
- Blue Cloud H2020 Project
- Blue-Cloud has been funded by the European Union’s Horizon programme call BG-07-2019-2020, topic: [A] 2019 - Blue Cloud services, Grant Agreement no. 862409. The views and opinions expressed in this website are solely the responsibility of the author and do not necessarily reflect the views of the European Commission.
This document offers a detailed explanation of the filtering components used in the Shiny application. The filtering interface allows users to customize data display based on specific criteria such as year, selected species, and fishing fleet.
- Select a Dataset: Begin by choosing the desired dataset.
- Apply Filters: Specify filters based on provided criteria.
- Submit: Click submit to view the data visualizations.
Users can select a range of year or select several non consecutives years by clicking on the “Discrete selection of the year” button.
Users can filter the data based on species. Two action buttons provide shortcuts for selecting all species or only the major tunas species being YFT, ALB, BET, SKJ, SBF
A collapsible panel allows users to select one or multiple fishing fleets. An action button is provided to select all fishing fleets at once.
From all the maps user is allowed to select a wkt to filter data from a specific region. It is needed to click on submit after this selection to filter the data.
Users have the option to reset the geographical selection to a global view or reset all filters to their default values. A submit button sends the selected filtering criteria to update the data display.
Future filters as month, trimester, gear_type, fishing_mode are to be added shortly.