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.codespellrc

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# Ref: https://github.com/codespell-project/codespell#using-a-config-file
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[codespell]
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skip = .git,*.pdf,*.svg,go.sum,*.css,*.min.*,.codespellrc
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check-hidden = true
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ignore-regex = \b(TE|currentY|Lew|fnndsc.childrens.harvard.edu)\b
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# ignore-words-list =

.github/workflows/codespell.yml

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# Codespell configuration is within .codespellrc
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---
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name: Codespell
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on:
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push:
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branches: [master]
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pull_request:
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branches: [master]
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permissions:
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contents: read
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jobs:
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codespell:
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name: Check for spelling errors
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Codespell
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uses: codespell-project/actions-codespell@v2

2019_training_logistics.html

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5Steps/acquisition.html

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<li>Use <a href="http://reproin.repronim.org">ReproIn</a> to automate conversion of your imaging data to <a href="http://bids.neuroimaging.io">BIDS</a>.
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<li>Annotate your metadata (all experimental details) as you collect it.
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<ul>
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<li>There are a number of ways to annotate your data. The idea with annotations are to provide unambigous markup of your data, whether it be imaging, behavioral, clinical, etc. One strategy is to annotate the variables you intend to collect during the study design process. This can occur by choosing data structures that are publically available and already annotated (e.g. <a href="https://ndar.nih.gov/data_dictionary.html">NDAR</a>) or by using the tools below. Regardless of the approach, the important aspect is to unambigously describe what the variables collected mean, their ranges, min/max, units, etc. For more information on the desired properties for annotations, see: <a href="https://docs.google.com/spreadsheets/d/1PKoO9wqxJy92Je1doX_a42Ey2PzFOqGTxQgSpHtu7D4/edit#gid=632728597">Federated Data Elements</a>
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<li>There are a number of ways to annotate your data. The idea with annotations are to provide unambiguous markup of your data, whether it be imaging, behavioral, clinical, etc. One strategy is to annotate the variables you intend to collect during the study design process. This can occur by choosing data structures that are publicly available and already annotated (e.g. <a href="https://ndar.nih.gov/data_dictionary.html">NDAR</a>) or by using the tools below. Regardless of the approach, the important aspect is to unambiguously describe what the variables collected mean, their ranges, min/max, units, etc. For more information on the desired properties for annotations, see: <a href="https://docs.google.com/spreadsheets/d/1PKoO9wqxJy92Je1doX_a42Ey2PzFOqGTxQgSpHtu7D4/edit#gid=632728597">Federated Data Elements</a>
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<ul>
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<li> <a href="http://brainverse.repronim.org">BrainVerse</a> is a tool being developed which can help to annotate your variables by directly incorporating data structures from NDAR.
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<li> <a href="https://github.com/incf-nidash/PyNIDM">PyNIDM</a> is both a Python library for creating metadata documents using the Neuroimaging Data Model (<a href="http://nidm.nidash.org/">NIDM</a>) but also contains tools such as "bidsmri2nidm" and "csv2nidm" which can be used to interactively annotate both BIDS datasets (via participants.csv file) and general CSV-formatted data files containinga header row of variables which require annotation. The annotation process requires you to have a <a href="https://scicrunch.org/">SciCrunch</a> account and API-access key which are freely available on the site. The tools iteratve over your variables and query the InterLex information resource looking for terms that match your variables. If no term is found you are asked to contribute a new term definition which will be used in the InterLex and thus provide persistent annotations.

5Steps/design.html

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5Steps/processing.html

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5Steps/publication.html

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5Steps/stats.html

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5steps.html

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about_us.html

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collaborators.html

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<p>If you are not redirected, <a href="community.html#collaborating-projects">click here</a>.</p>
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community.html

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describe.html

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discover.html

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do.html

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We strongly believe in the benefits of modular design,
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collaboration, and reuse. That is why, instead of developing a
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reusing and contributing to existing projects as much as possible.
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eab.html

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events.html

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fellows-resources.html

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<dd>Southampton Brain Image Processing Workshops</dd>
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<dd>academics and clinicians developing image processing and data analysis methods for neurological disorders</dd>
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<dd><a href="https://sotnir.github.io/svg" target="_blank">https://sotnir.github.io/svg</a><br></dd>
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<dt>Fellow</dt>
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<dd>Yukai Zou</dd>
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