diff --git a/docs/labguide/general_lab_policies.html b/docs/labguide/general_lab_policies.html index a9746e9..aaab25b 100644 --- a/docs/labguide/general_lab_policies.html +++ b/docs/labguide/general_lab_policies.html @@ -366,7 +366,7 @@

On this page

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General lab policies

Administrative requests

The assigned administrative assistant for the lab (currently Diana Savastio) should be the default person to send any administrative requests. In the past, we have had issues where requests were coming from multiple members of our lab and/or to multiple administrative staff, which was not being sufficiently mindful of time and busy schedules of the administrative staff. Therefore, in general, you should not directly contact other departmental administrators with requests unless directed by the administrative assistant or Dr. Poldrack. However, there may be exceptions to this that justify contacting other staff directly (e.g., CNI-related questions directly to CNI staff like Laima Baltusis).

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Onboarding

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New members of the lab will be provided with a detailed list of onboarding procedures as well as access to the lab’s Google Drive which contains internal lab documentation.

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Lab procedures

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Internal information about lab procedures are available via a set of Google docs (all of which require permission to access):

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diff --git a/docs/labguide/mentoring/mentoring.html b/docs/labguide/mentoring/mentoring.html index a1d7f0f..845c555 100644 --- a/docs/labguide/mentoring/mentoring.html +++ b/docs/labguide/mentoring/mentoring.html @@ -428,7 +428,7 @@

What doe

There are always more qualified applicants than there are spots in our graduate programs, and there is no way to guarantee admission to any particular program. On the flipside, there are also no absolute requirements: we look at the whole picture, and other factors can sometimes outweigh a weaker academic record. There are a few factors that are particularly important for admission to my lab:

  1. Research experience: It is very rare for someone to be accepted into any of the programs I am affiliated with at Stanford without significant research experience. Sometimes this can be obtained as an undergraduate, but more often successful applicants to our program have spent at least a year working as a research assistant in an active research laboratory. There are a couple of important reasons for this. First, we want you to understand what you are getting into; many people have rosy ideas of what it’s like to be a scientist, which can fall away pretty quickly in light of the actual experience of doing science. Spending some time in a lab helps you make sure that this is how you want to spend your life. In addition, it provides you with someone who can write a recommendation letter that speaks very directly to your potential as a researcher. Letters are a very important part of the admissions process, and the most effective letters are those that go into specific detail about your abilities, aptitude, and motivation.
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  2. Technical skills: The research that we do in our lab is highly technical, requiring knowledge of computing systems, programming, and math/statistics. I would say that decent programming ability is a pretty firm prerequisite for entering my lab; once you enter the lab I want you to be able to jump directly into doing science, and this just can’t happen if you have to spend a year teaching yourself how to program from scratch. More generally, we expect you to be able to pick up new technical topics easily; I don’t expect students to necessarily show up knowing how a reinforcement learning model works, but I expect them to be able to go and figure it out on their own by reading the relevant papers and then implement it on their own. The best way to demonstrate programming ability is to show a specific project that you have worked on. This could be an open source project that you have contributed to, or a project that you did on the side for fun (for example, mine your own social media feed, or program a cognitive task and measure how your own behavior changes from day to day). If you don’t currently know how to program, see my post on learning to program from scratch, and get going!
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  3. Technical skills: The research that we do in our lab is highly technical, requiring knowledge of computing systems, programming, and math/statistics. I would say that decent programming ability is a pretty firm prerequisite for entering my lab; once you enter the lab I want you to be able to jump directly into doing science, and this just can’t happen if you have to spend a year teaching yourself how to program from scratch. More generally, we expect you to be able to pick up new technical topics easily; I don’t expect students to necessarily show up knowing how a reinforcement learning model works, but I expect them to be able to go and figure it out on their own by reading the relevant papers and then implement it on their own. The best way to demonstrate programming ability is to show a specific project that you have worked on. This could be an open source project that you have contributed to, or a project that you did on the side for fun (for example, mine your own social media feed, or program a cognitive task and measure how your own behavior changes from day to day). If you don’t currently know how to program, see my post on learning to program from scratch, and get going!
    1. Risk taking and resilience: If we are doing interesting science then things are going to fail, and we have to learn from those failures and move on.  I want to know that you are someone who is willing to go out on a limb to try something risky, and can handle the inevitable failures gracefully.  Rather than seeing a statement of purpose that only lists all of your successes, I find it very useful to also know about risks you have taken (be they physical, social, or emotional), challenges you have faced, failures you have experienced, and most importantly what you learned from all of these experiences.
    5. diff --git a/docs/labguide/publication/authorship.html b/docs/labguide/publication/authorship.html index 72cb780..c05a1a0 100644 --- a/docs/labguide/publication/authorship.html +++ b/docs/labguide/publication/authorship.html @@ -365,7 +365,8 @@

    On this page

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    Authorship

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    Authorship policies

    • The lab’s policy follows the IJCME recommendations on Defining the Role of Authors and Contributors, which state that authorship should be based on four criteria:
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        Authorship

      • Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
    • Importantly, we view substantial contributions to data acquisition or analysis alone to be sufficient for authorship on the primary project manuscript, as long criteria 2-4 are met. For this reason, research assistants in our lab often end up being coauthors on our publications.
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    • Criterion #1 also implies that Dr. Poldrack will not accept authorship on any work that he has not played a substantial role in either designing, analyzing, or interpreting.  Please do not send completed manuscripts with a request for him to be a coauthor.
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    • The sharing of data is not considered to be sufficient on its own to warrant authorship; instead, credit should be given through citation of a data descriptor and/or dataset DOI.  However, authorship is appropriate in cases where the dataset owner provides substantial input into the research, or when required by the data use agreement.
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    • Criterion #1 also implies that Dr. Poldrack will not accept authorship on any work that he has not played a substantial role in either designing, analyzing, or interpreting. Please do not send completed manuscripts with a request for him to be a coauthor.
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    • The sharing of data is not considered to be sufficient on its own to warrant authorship; instead, credit should be given through citation of a data descriptor and/or dataset DOI. However, authorship is appropriate in cases where the dataset owner provides substantial input into the research, or when required by the data use agreement.
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    Affiliation

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    1. The listed affiliation should include any institutions where one was affiliated while completing the work. -
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      • Any work completed at Stanford should include the Stanford affiliation, even if the individual has left Stanford.
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      • Work completed prior to joining Stanford but published after joining the lab does not need to include the Stanford affiliation, but it may be included (along with the institution where the work was completed) if work was done on the paper after joining the lab.
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    The listed affiliation for a publication should include any institutions where one was affiliated while completing the work.
    +- Any work completed at Stanford should include the Stanford affiliation, even if the individual has left Stanford. - Work completed prior to joining Stanford but published after joining the lab does not need to include the Stanford affiliation, but it may be included (along with the institution where the work was completed) if work was done on the paper after joining the lab.

    diff --git a/docs/labguide/research/intellectual_property.html b/docs/labguide/research/intellectual_property.html index 3be5ead..97b83bd 100644 --- a/docs/labguide/research/intellectual_property.html +++ b/docs/labguide/research/intellectual_property.html @@ -380,8 +380,8 @@

    On this page

    Intellectual property

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    All products of research at Stanford (including data and code) are the property of the University. However, faculty have wide latitude to release software as open source under the University’s open source policy, as long as it doesn’t conflict with any other obligations. Students are allowed to release code under an open source license at Stanford with faculty permission; students in the Poldracklab have blanket permission to do so, as all of our code is intended to be made open available, as discussed in the section on code sharing. After leaving the lab, trainees can continue to reuse any code or other research materials (e.g. stimuli) developed as part of their work in the lab as long as the code has been released under an open source license and they continue to abide by the terms of the license.

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    As discussed in the section on data management and sharing, all data collected within our laboratory is meant to be shared upon submission of the related paper. In cases where these data can be deidentified they will be shared under a public domain dedication (CC0), which places no restrictions on their use by other researchers. Thus, any researcher can continue to use those data once they leave the lab. In other cases it may be necessary to restrict data sharing (e.g. when the data cannot be deidentified), in which case researchers will need to obtain a data use agreement from Stanford in order to access those data at their new institution. Dr. Poldrack commits to supporting any such requests, unless they violate other obligations of his or the University.

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    All products of research at Stanford (including data and code) are the property of the University. However, faculty have wide latitude to release software as open source under the University’s open source policy, as long as it doesn’t conflict with any other obligations. Students are allowed to release code under an open source license at Stanford with faculty permission; students in the Poldracklab have blanket permission to do so, as all of our code is intended to be made open available, as discussed in the section on code sharing. After leaving the lab, trainees can continue to reuse any code or other research materials (e.g. stimuli) developed as part of their work in the lab as long as the code has been released under an open source license and they continue to abide by the terms of the license.

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    As discussed in the section on data management and sharing, all data collected within our laboratory is meant to be shared upon submission of the related paper. In cases where these data can be deidentified they will be shared under a public domain dedication (CC0), which places no restrictions on their use by other researchers. Thus, any researcher can continue to use those data once they leave the lab. In other cases it may be necessary to restrict data sharing (e.g. when the data cannot be deidentified), in which case researchers will need to obtain a data use agreement from Stanford in order to access those data at their new institution. Dr. Poldrack commits to supporting any such requests, unless they violate other obligations of his or the University.

    In many cases we use data within the lab under Data Use Agreements (such as the ABCD or HCP datasets), and any researcher wishing to use such datasets must be explicitly listed on the DUA. Once a researcher is no longer included in the Stanford DUA for a particular dataset, they must no longer access the dataset via Stanford computer systems.

    Research ideas

    diff --git a/docs/publications.html b/docs/publications.html index c895fc4..3a38776 100644 --- a/docs/publications.html +++ b/docs/publications.html @@ -385,7 +385,7 @@

    Publications

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    2024

    Demidenko MI, Mumford JA, Poldrack RA (2024). Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. bioRxiv. OA DOI

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    Data/code/OSF pages for published papers

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    2024

    Mumford JA, Bissett PG, Jones HM, Shim S, Rios JAH, Poldrack RA (2024). The response time paradox in functional magnetic resonance imaging analyses. Nature Human Behaviour, 8, 349-360. DOI Data Code