[REVIEW]: A reproducible benchmark of denoising strategies in resting-state fMRI connectivity

roboneuro commented 1 year ago

Submitting author: !--author-handle-->@htwangtw@mathieuboudreau@agahkarakuzu<!--end-reviewers-list-- Reproducible preprint: https://preprint.neurolibre.org/10.55458/neurolibre.00012 Repository archive: 10.5281/zenodo.7996698 Data archive: 10.5281/zenodo.7764979 Book archive: 10.5281/zenodo.7996696 Docker archive: 10.5281/zenodo.7996700

Status

Status badge code:

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Markdown: [![status](https://neurolibre.org/papers/934e2fe21c1d2bcd4d014f5b03e9a812/status.svg)](https://neurolibre.org/papers/934e2fe21c1d2bcd4d014f5b03e9a812)

Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@agahkarakuzu, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@roboneuro generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @mathieuboudreau know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

@agahkarakuzu, please create your checklist typing: @roboneuro generate my checklist

htwangtw commented 1 year ago

@agahkarakuzu done!

agahkarakuzu commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"Reading the fmriprep-denoise-benchmark paper was like watching a master chef create a deliciously denoised brain scan - impressive, efficient, and oh so satisfying!"

agahkarakuzu commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"fmriprep-denoise-benchmark: because sometimes even brain activity needs a little touch up."

agahkarakuzu commented 1 year ago

Book build log @ https://github.com/SIMEXP/fmriprep-denoise-benchmark/commit/751d0e45922c623f121f60ab2212b95080238631

Running Jupyter-Book v0.13.0
Source Folder: /home/jovyan/content
Config Path: /home/jovyan/content/_config.yml
Output Path: /mnt/books/SIMEXP/github.com/fmriprep-denoise-benchmark/751d0e45922c623f121f60ab2212b95080238631/_build/html
Running Sphinx v4.5.0
Adding copy buttons to code blocks...
Adding copy buttons to code blocks...
checking bibtex cache... out of date
parsing bibtex file /home/jovyan/content/references.bib... parsed 68 entries
myst v0.15.2: MdParserConfig(renderer='sphinx', commonmark_only=False, enable_extensions=['colon_fence', 'dollarmath', 'linkify', 'substitution', 'tasklist'], dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', disable_syntax=[], url_schemes=['mailto', 'http', 'https'], heading_anchors=None, heading_slug_func=None, html_meta=[], footnote_transition=True, substitutions=[], sub_delimiters=['{', '}'], words_per_minute=200)
MyST-NB: Excluded Paths: set()
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 12 source files that are out of date
updating environment: MyST-NB: Potential docnames to execute: ['index', 'docs/tldr', 'notebooks/CITATION', 'notebooks/results-group', 'docs/setup', 'docs/software_implemetation', 'notebooks/ohbm2022abstract', 'docs/fmriprep', 'docs/timeseires_metrics', 'notebooks/contributions', 'notebooks/references', 'notebooks/results-atlas']
executing outdated notebooks... Executing: /home/jovyan/content/notebooks/results-atlas.md
Title: Benchmark denoising strategies on fMRIPrep output - input data
Keywords: 
Publication date: 2022-07-29
DOI: 10.5281/zenodo.7764979
Total size: 6911.1 MB

Link: https://zenodo.org/api/files/3ab6a0ab-9076-4304-9d95-0880412572b8/custome_templateflow.tar.gz   size: 22.2 MB

Checksum is correct. (3a04bdf835aa7e0ae12b7bd3f2ff6a2d)

Link: https://zenodo.org/api/files/3ab6a0ab-9076-4304-9d95-0880412572b8/denoise-metrics.tar.gz   size: 6888.9 MB

htwangtw commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"fmriprep-denoise-benchmark: where brain meets brawn in the battle against noise!"

htwangtw commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"fmriprep-denoise-benchmark: Making fMRI data cleaner than your grandma's house on cleaning day!"

htwangtw commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"fmriprep-denoise-benchmark: making brainwaves smoother than a freshly shaved scalp."

htwangtw commented 1 year ago

@roboneuro build book

roboneuro commented 1 year ago

:seedling: I've started building your NeuroLibre reproducible preprint! :seedling:

My close :robot: friend GPT read your paper.md and noted:

"fmriprep-denoise-benchmark: the tool that makes your brain scans look so good, even your neurons are impressed."

agahkarakuzu commented 1 year ago

@roboneuro commands

roboneuro commented 1 year ago

Hello @agahkarakuzu, here are the things you can ask me to do:


# List all available commands
@roboneuro commands

# Add to this issue's reviewers list
@roboneuro add @username as reviewer

# Remove from this issue's reviewers list
@roboneuro remove @username from reviewers

# Get a list of all editors's GitHub handles
@roboneuro list editors

# Assign a user as the editor of this submission
@roboneuro assign @username as editor

# Remove the editor assigned to this submission
@roboneuro remove editor

# Remind an author, a reviewer or the editor to return to a review after a 
# certain period of time (supported units days and weeks)
@roboneuro remind @reviewer in 2 weeks

# Check the references of the PDF for missing DOIs
@roboneuro check references

# Perform checks on the repository
@roboneuro check repository

# Adds a checklist for the reviewer using this command
@roboneuro generate my checklist

# Set a value for version
@roboneuro set v1.0.0 as version

# Set a value for branch
@roboneuro set neurolibre-paper as branch

# Set a value for repository
@roboneuro set https://github.com/organization/repo as repository

# Set a value for the archive DOI
@roboneuro set set 10.5281/zenodo.6861996 as archive

# Mention the EiCs for the correct track
@roboneuro ping track-eic

# Reject paper
@roboneuro reject

# Withdraw paper
@roboneuro withdraw

# Invite an editor to edit a submission (sending them an email)
@roboneuro invite @(.*) as editor

# Generates the pdf paper
@roboneuro generate pdf

# Recommends the submission for acceptance
@roboneuro recommend-accept

# Accept and publish the paper
@roboneuro accept

# Update data on an accepted/published paper
@roboneuro reaccept

# Generates a LaTeX preprint file
@roboneuro generate preprint

# Flag submission with questionable scope
@roboneuro query scope

# Get a link to the complete list of reviewers
@roboneuro list reviewers

# Creates a post-review checklist with editor and authors tasks
@roboneuro create post-review checklist

# Open the review issue
@roboneuro start review

# Check the status of the NeuroLibre preview server.
@roboneuro preview server status

# Check the status of the NeuroLibre preprint (production) server.
@roboneuro preprint server status

# Build a NeuroLibre reproducible preprint for technical screening.
@roboneuro build book

# Transfer data from preview to the preprint (production) server.
@roboneuro production sync data

# Transfer the final preprint (forked repo) to the preprint (production) server (DOI URL).
@roboneuro production sync book

# Start the production process by forking repository and configuring it.
@roboneuro production start

agahkarakuzu commented 1 year ago

@roboneuro production start

roboneuro commented 1 year ago

⚫ INITIATE PRODUCTION (Fork and Configure)

Status: Waiting for task assignment Last updated: 2023-05-30 21:34:54 PDT

agahkarakuzu commented 1 year ago

@roboneuro production start

roboneuro commented 1 year ago

🟢 INITIATE PRODUCTION (Fork and Configure)

Status: Success dff19945 Last updated: 2023-05-30 21:38:01 PDT

:information_source: See details

Please confirm that the forked repository is available and (_toc.yml and _config.ymlk) properly configured.

agahkarakuzu commented 1 year ago

@roboneuro commands

roboneuro commented 1 year ago

Hello @agahkarakuzu, here are the things you can ask me to do:


# List all available commands
@roboneuro commands

# Add to this issue's reviewers list
@roboneuro add @username as reviewer

# Remove from this issue's reviewers list
@roboneuro remove @username from reviewers

# Get a list of all editors's GitHub handles
@roboneuro list editors

# Assign a user as the editor of this submission
@roboneuro assign @username as editor

# Remove the editor assigned to this submission
@roboneuro remove editor

# Remind an author, a reviewer or the editor to return to a review after a 
# certain period of time (supported units days and weeks)
@roboneuro remind @reviewer in 2 weeks

# Check the references of the PDF for missing DOIs
@roboneuro check references

# Perform checks on the repository
@roboneuro check repository

# Adds a checklist for the reviewer using this command
@roboneuro generate my checklist

# Set a value for version
@roboneuro set v1.0.0 as version

# Set a value for branch
@roboneuro set neurolibre-paper as branch

# Set a value for repository
@roboneuro set https://github.com/organization/repo as repository

# Set a value for the archive DOI
@roboneuro set set 10.5281/zenodo.6861996 as archive

# Mention the EiCs for the correct track
@roboneuro ping track-eic

# Reject paper
@roboneuro reject

# Withdraw paper
@roboneuro withdraw

# Invite an editor to edit a submission (sending them an email)
@roboneuro invite @(.*) as editor

# Generates the pdf paper
@roboneuro generate pdf

# Recommends the submission for acceptance
@roboneuro recommend-accept

# Accept and publish the paper
@roboneuro accept

# Update data on an accepted/published paper
@roboneuro reaccept

# Generates a LaTeX preprint file
@roboneuro generate preprint

# Flag submission with questionable scope
@roboneuro query scope

# Get a link to the complete list of reviewers
@roboneuro list reviewers

# Creates a post-review checklist with editor and authors tasks
@roboneuro create post-review checklist

# Open the review issue
@roboneuro start review

# Check the status of the NeuroLibre preview server.
@roboneuro preview server status

# Check the status of the NeuroLibre preprint (production) server.
@roboneuro preprint server status

# Build a NeuroLibre reproducible preprint for technical screening.
@roboneuro build book

# After screening, build book from the forked reository.
@roboneuro production build book

# Transfer data from preview to the preprint (production) server.
@roboneuro production sync data

# Transfer the final preprint (forked repo) to the preprint (production) server (DOI URL).
@roboneuro production sync book

# Start the production process by forking repository and configuring it.
@roboneuro production start

agahkarakuzu commented 1 year ago

@roboneuro production sync data

roboneuro commented 1 year ago

🟢 DATA TRANSFER (Preview --> Preprint)

Status: Success 442139d4 Last updated: 2023-05-31 20:04:20 PDT

:information_source: See details

b'receiving incremental file 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325_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-36_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-36_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-36_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-444_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-444_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-444_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-64_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-64_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-64_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-7_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-7_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-7_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-ROI_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-ROI_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-mist_nroi-ROI_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_desc-confounds_phenotype.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.1lts/dataset-ds000228_desc-movement_phenotype.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-1024_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-1024_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-1024_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-128_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-128_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-128_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-256_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-256_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-256_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-512_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-512_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-512_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-64_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-64_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-difumo_nroi-64_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-gordon333_nroi-333_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-gordon333_nroi-333_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-gordon333_nroi-333_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-122_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-122_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-122_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-12_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-12_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-12_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-197_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-197_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-197_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-20_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-20_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-20_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-325_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-325_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-325_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-36_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-36_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-36_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-444_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-444_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-444_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-64_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-64_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-64_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-7_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-7_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-7_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-ROI_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-ROI_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-mist_nroi-ROI_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-100_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-200_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-300_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-400_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-500_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-600_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_connectome.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_modularity.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_atlas-schaefer7networks_nroi-800_qcfc.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_desc-confounds_phenotype.tsv\nDATA/fmriprep-denoise-benchmark/denoise-metrics/ds000228/fmriprep-20.2.5lts/dataset-ds000228_desc-movement_phenotype.tsv\n\nsent 19,607 bytes  received 16,011,625,108 bytes  2,727,010.94 bytes/sec\ntotal size is 16,007,639,228  speedup is 1.00\n'

agahkarakuzu commented 1 year ago

@roboneuro production build book

roboneuro commented 1 year ago

⚪ Book Build (Preview)

Status: Assigned to task 13574f8f Last updated: 2023-05-31 20:21:13 PDT

agahkarakuzu commented 1 year ago

OK so this works! I'll deal with in-place updating, here's the book built on fork:

https://preview.neurolibre.org/book-artifacts/roboneurolibre/github.com/fmriprep-denoise-benchmark/b568759fd2cbf9b27ef170a9c9984aaaf0de40a3/_build/html/index.html

Please do NOT execute it on BinderHub yet :)

agahkarakuzu commented 1 year ago

@roboneuro production sync book

roboneuro commented 1 year ago

🔴 REPRODUCIBLE PREPRINT TRANSFER (Preview --> Preprint)

Status: Failed 88b77cee Last updated: 2023-05-31 20:47:59 PDT

:information_source: See details

Repository is not under roboneurolibre organization!

agahkarakuzu commented 1 year ago

Failed on purpose, no worries.

agahkarakuzu commented 1 year ago

@roboneuro production sync book

roboneuro commented 1 year ago

🟢 REPRODUCIBLE PREPRINT TRANSFER (Preview --> Preprint)

Status: Success 50d0e952 Last updated: 2023-05-31 20:51:17 PDT

:information_source: See details

Reproducible Preprint URL (DOI formatted)Reproducible Preprint (bare URL)

agahkarakuzu commented 1 year ago

@roboneuro commands

roboneuro commented 1 year ago

Hello @agahkarakuzu, here are the things you can ask me to do:


# List all available commands
@roboneuro commands

# Add to this issue's reviewers list
@roboneuro add @username as reviewer

# Remove from this issue's reviewers list
@roboneuro remove @username from reviewers

# Get a list of all editors's GitHub handles
@roboneuro list editors

# Assign a user as the editor of this submission
@roboneuro assign @username as editor

# Remove the editor assigned to this submission
@roboneuro remove editor

# Remind an author, a reviewer or the editor to return to a review after a 
# certain period of time (supported units days and weeks)
@roboneuro remind @reviewer in 2 weeks

# Check the references of the PDF for missing DOIs
@roboneuro check references

# Perform checks on the repository
@roboneuro check repository

# Adds a checklist for the reviewer using this command
@roboneuro generate my checklist

# Set a value for version
@roboneuro set v1.0.0 as version

# Set a value for branch
@roboneuro set neurolibre-paper as branch

# Set a value for repository
@roboneuro set https://github.com/organization/repo as repository

# Set a value for the archive DOI
@roboneuro set set 10.5281/zenodo.6861996 as archive

# Set a value for the data archive DOI
@roboneuro set 10.5281/zenodo.6861996 as data archive

# Mention the EiCs for the correct track
@roboneuro ping track-eic

# Reject paper
@roboneuro reject

# Withdraw paper
@roboneuro withdraw

# Invite an editor to edit a submission (sending them an email)
@roboneuro invite @(.*) as editor

# Generates the pdf paper
@roboneuro generate pdf

# Recommends the submission for acceptance
@roboneuro recommend-accept

# Accept and publish the paper
@roboneuro accept

# Update data on an accepted/published paper
@roboneuro reaccept

# Generates a LaTeX preprint file
@roboneuro generate preprint

# Flag submission with questionable scope
@roboneuro query scope

# Get a link to the complete list of reviewers
@roboneuro list reviewers

# Creates a post-review checklist with editor and authors tasks
@roboneuro create post-review checklist

# Open the review issue
@roboneuro start review

# Check the status of the NeuroLibre preview server.
@roboneuro preview server status

# Check the status of the NeuroLibre preprint (production) server.
@roboneuro preprint server status

# Build a NeuroLibre reproducible preprint for technical screening.
@roboneuro build book

# After screening, build book from the forked reository.
@roboneuro production build book

# Transfer data from preview to the preprint (production) server.
@roboneuro production sync data

# Transfer the final preprint (forked repo) to the preprint (production) server (DOI URL).
@roboneuro production sync book

# Start the production process by forking repository and configuring it.
@roboneuro production start

agahkarakuzu commented 1 year ago

@roboneuro set 10.5281/zenodo.7764979 as data archive

roboneuro commented 1 year ago

Done! Data archive is now 10.5281/zenodo.7764979

agahkarakuzu commented 1 year ago

@roboneuro commands

roboneuro commented 1 year ago

Hello @agahkarakuzu, here are the things you can ask me to do:


# List all available commands
@roboneuro commands

# Add to this issue's reviewers list
@roboneuro add @username as reviewer

# Remove from this issue's reviewers list
@roboneuro remove @username from reviewers

# Get a list of all editors's GitHub handles
@roboneuro list editors

# Assign a user as the editor of this submission
@roboneuro assign @username as editor

# Remove the editor assigned to this submission
@roboneuro remove editor

# Remind an author, a reviewer or the editor to return to a review after a 
# certain period of time (supported units days and weeks)
@roboneuro remind @reviewer in 2 weeks

# Check the references of the PDF for missing DOIs
@roboneuro check references

# Perform checks on the repository
@roboneuro check repository

# Adds a checklist for the reviewer using this command
@roboneuro generate my checklist

# Set a value for version
@roboneuro set v1.0.0 as version

# Set a value for branch
@roboneuro set neurolibre-paper as branch

# Set a value for repository
@roboneuro set https://github.com/organization/repo as repository

# Set a value for the archive DOI
@roboneuro set set 10.5281/zenodo.6861996 as archive

# Set a value for the data archive DOI
@roboneuro set 10.5281/zenodo.6861996 as data archive

# Mention the EiCs for the correct track
@roboneuro ping track-eic

# Reject paper
@roboneuro reject

# Withdraw paper
@roboneuro withdraw

# Invite an editor to edit a submission (sending them an email)
@roboneuro invite @(.*) as editor

# Generates the pdf paper
@roboneuro generate pdf

# Recommends the submission for acceptance
@roboneuro recommend-accept

# Accept and publish the paper
@roboneuro accept

# Update data on an accepted/published paper
@roboneuro reaccept

# Generates a LaTeX preprint file
@roboneuro generate preprint

# Flag submission with questionable scope
@roboneuro query scope

# Get a link to the complete list of reviewers
@roboneuro list reviewers

# Creates a post-review checklist with editor and authors tasks
@roboneuro create post-review checklist

# Open the review issue
@roboneuro start review

# Check the status of the NeuroLibre preview server.
@roboneuro preview server status

# Check the status of the NeuroLibre preprint (production) server.
@roboneuro preprint server status

# Build a NeuroLibre reproducible preprint for technical screening.
@roboneuro build book

# After screening, build book from the forked reository.
@roboneuro production build book

# Transfer data from preview to the preprint (production) server.
@roboneuro production sync data

# Transfer the final preprint (forked repo) to the preprint (production) server (DOI URL).
@roboneuro production sync book

# Start the production process by forking repository and configuring it.
@roboneuro production start

# Creates Zenodo deposits (a.k.a buckets) for archiving reproducibility assets.
@roboneuro zenodo create buckets

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🔴 Reproducibility Assets - Create Zenodo buckets

Status: Failed 08705ddd Last updated: 2023-06-01 14:55:25 PDT

:information_source: See details

{'book': {'reason': '404: Cannot create book bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'repository': {'reason': '404: Cannot create repository bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'docker': {'reason': '404: Cannot create docker bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}}

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🔴 Reproducibility Assets - Create Zenodo buckets

Status: Failed 2f48dc93 Last updated: 2023-06-01 15:40:11 PDT

:information_source: See details

{'book': {'reason': '404: Cannot create book bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'repository': {'reason': '404: Cannot create repository bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'docker': {'reason': '404: Cannot create docker bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}}

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🔴 Reproducibility Assets - Create Zenodo buckets

Status: Failed fd43505a Last updated: 2023-06-01 15:47:50 PDT

:information_source: See details

{'book': {'reason': '404: Cannot create book bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'repository': {'reason': '404: Cannot create repository bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'docker': {'reason': '404: Cannot create docker bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}}

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🔴 Reproducibility Assets - Create Zenodo buckets

Status: Failed 5e1d7195 Last updated: 2023-06-01 15:53:54 PDT

:information_source: See details

{'book': {'reason': '404: Cannot create book bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'repository': {'reason': '404: Cannot create repository bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'docker': {'reason': '404: Cannot create docker bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}}

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🔴 Reproducibility Assets - Create Zenodo buckets

Status: Failed 0324cd10 Last updated: 2023-06-01 16:15:02 PDT

:information_source: See details

{'book': {'reason': '404: Cannot create book bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'repository': {'reason': '404: Cannot create repository bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}, 'docker': {'reason': '404: Cannot create docker bucket.', 'commit_hash': 'b568759fd2cbf9b27ef170a9c9984aaaf0de40a3', 'repo_url': 'https://github.com/roboneurolibre/fmriprep-denoise-benchmark'}}

agahkarakuzu commented 1 year ago

@roboneuro zenodo create buckets

roboneuro commented 1 year ago

🟢 Reproducibility Assets - Create Zenodo buckets

Status: Success 0deeba89 Last updated: 2023-06-01 16:22:42 PDT

:information_source: See details

Zenodo records have been created successfully: 
 {'book': {'conceptrecid': '7996511', 'created': '2023-06-01T23:22:29.898153+00:00', 'doi': '', 'doi_url': 'https://doi.org/', 'files': [], 'id': 7996512, 'links': {'bucket': 'https://zenodo.org/api/files/2017f9d7-532e-4197-8f99-c02b36a42d12', 'discard': 'https://zenodo.org/api/deposit/depositions/7996512/actions/discard', 'edit': 'https://zenodo.org/api/deposit/depositions/7996512/actions/edit', 'files': 'https://zenodo.org/api/deposit/depositions/7996512/files', 'html': 'https://zenodo.org/deposit/7996512', 'latest_draft': 'https://zenodo.org/api/deposit/depositions/7996512', 'latest_draft_html': 'https://zenodo.org/deposit/7996512', 'publish': 'https://zenodo.org/api/deposit/depositions/7996512/actions/publish', 'self': 'https://zenodo.org/api/deposit/depositions/7996512'}, 'metadata': {'access_right': 'open', 'contributors': [{'affiliation': 'NeuroLibre', 'name': 'NeuroLibre, Admin', 'type': 'ContactPerson'}], 'creators': [{'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hao-Ting Wang', 'orcid': '0000-0003-4078-2038'}, {'affiliation': 'Harvard University, MA, USA', 'name': 'Steven L Meisler', 'orcid': '0000-0002-8888-1572'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hanad Sharmarke'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Natasha Clarke'}, {'affiliation': 'Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada', 'name': 'François Paugam'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Nicolas Gensollen', 'orcid': '0000-0001-7199-9753'}, {'affiliation': 'Department of Psychology, Stanford University, Stanford, United States', 'name': 'Christopher J Markiewicz', 'orcid': '0000-0002-6533-164X'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Bertrand Thirion', 'orcid': '0000-0001-5018-7895'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Pierre Bellec', 'orcid': '0000-0002-9111-0699'}], 'description': 'NeuroLibre JupyterBook built at this  reference repository/commit by roboneuro, based on the latest change by the author. For details, please visit the corresponding NeuroLibre technical screening.
 \nhttps://neurolibre.org
', 'doi': '', 'keywords': ['canadian-open-neuroscience-platform', 'neurolibre'], 'license': 'CC-BY-4.0', 'prereserve_doi': {'doi': '10.5281/zenodo.7996512', 'recid': 7996512}, 'publication_date': '2023-06-01', 'publication_type': 'preprint', 'related_identifiers': [{'identifier': '10.55458/neurolibre.00012', 'relation': 'isPartOf', 'resource_type': 'publication-preprint', 'scheme': 'doi'}], 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn', 'upload_type': 'publication'}, 'modified': '2023-06-01T23:22:29.898162+00:00', 'owner': 262806, 'record_id': 7996512, 'state': 'unsubmitted', 'submitted': False, 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn'}, 'repository': {'conceptrecid': '7996513', 'created': '2023-06-01T23:22:35.195574+00:00', 'doi': '', 'doi_url': 'https://doi.org/', 'files': [], 'id': 7996514, 'links': {'bucket': 'https://zenodo.org/api/files/7de85725-9ecf-49e3-a608-b95d089358ed', 'discard': 'https://zenodo.org/api/deposit/depositions/7996514/actions/discard', 'edit': 'https://zenodo.org/api/deposit/depositions/7996514/actions/edit', 'files': 'https://zenodo.org/api/deposit/depositions/7996514/files', 'html': 'https://zenodo.org/deposit/7996514', 'latest_draft': 'https://zenodo.org/api/deposit/depositions/7996514', 'latest_draft_html': 'https://zenodo.org/deposit/7996514', 'publish': 'https://zenodo.org/api/deposit/depositions/7996514/actions/publish', 'self': 'https://zenodo.org/api/deposit/depositions/7996514'}, 'metadata': {'access_right': 'open', 'contributors': [{'affiliation': 'NeuroLibre', 'name': 'NeuroLibre, Admin', 'type': 'ContactPerson'}], 'creators': [{'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hao-Ting Wang', 'orcid': '0000-0003-4078-2038'}, {'affiliation': 'Harvard University, MA, USA', 'name': 'Steven L Meisler', 'orcid': '0000-0002-8888-1572'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hanad Sharmarke'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Natasha Clarke'}, {'affiliation': 'Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada', 'name': 'François Paugam'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Nicolas Gensollen', 'orcid': '0000-0001-7199-9753'}, {'affiliation': 'Department of Psychology, Stanford University, Stanford, United States', 'name': 'Christopher J Markiewicz', 'orcid': '0000-0002-6533-164X'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Bertrand Thirion', 'orcid': '0000-0001-5018-7895'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Pierre Bellec', 'orcid': '0000-0002-9111-0699'}], 'description': 'GitHub archive of the  reference repository/commit by roboneuro, based on the latest change by the author. For details, please visit the corresponding NeuroLibre technical screening.
 \nhttps://neurolibre.org
', 'doi': '', 'keywords': ['canadian-open-neuroscience-platform', 'neurolibre'], 'license': 'CC-BY-4.0', 'prereserve_doi': {'doi': '10.5281/zenodo.7996514', 'recid': 7996514}, 'publication_date': '2023-06-01', 'related_identifiers': [{'identifier': '10.55458/neurolibre.00012', 'relation': 'isPartOf', 'resource_type': 'publication-preprint', 'scheme': 'doi'}], 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn', 'upload_type': 'software'}, 'modified': '2023-06-01T23:22:35.195583+00:00', 'owner': 262806, 'record_id': 7996514, 'state': 'unsubmitted', 'submitted': False, 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn'}, 'docker': {'conceptrecid': '7996515', 'created': '2023-06-01T23:22:40.235022+00:00', 'doi': '', 'doi_url': 'https://doi.org/', 'files': [], 'id': 7996516, 'links': {'bucket': 'https://zenodo.org/api/files/55b3eb9d-42e9-45fb-b87c-326aa2f123ea', 'discard': 'https://zenodo.org/api/deposit/depositions/7996516/actions/discard', 'edit': 'https://zenodo.org/api/deposit/depositions/7996516/actions/edit', 'files': 'https://zenodo.org/api/deposit/depositions/7996516/files', 'html': 'https://zenodo.org/deposit/7996516', 'latest_draft': 'https://zenodo.org/api/deposit/depositions/7996516', 'latest_draft_html': 'https://zenodo.org/deposit/7996516', 'publish': 'https://zenodo.org/api/deposit/depositions/7996516/actions/publish', 'self': 'https://zenodo.org/api/deposit/depositions/7996516'}, 'metadata': {'access_right': 'open', 'contributors': [{'affiliation': 'NeuroLibre', 'name': 'NeuroLibre, Admin', 'type': 'ContactPerson'}], 'creators': [{'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hao-Ting Wang', 'orcid': '0000-0003-4078-2038'}, {'affiliation': 'Harvard University, MA, USA', 'name': 'Steven L Meisler', 'orcid': '0000-0002-8888-1572'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Hanad Sharmarke'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Natasha Clarke'}, {'affiliation': 'Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada', 'name': 'François Paugam'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Nicolas Gensollen', 'orcid': '0000-0001-7199-9753'}, {'affiliation': 'Department of Psychology, Stanford University, Stanford, United States', 'name': 'Christopher J Markiewicz', 'orcid': '0000-0002-6533-164X'}, {'affiliation': 'Inria, CEA, Université Paris-Saclay, Paris, France', 'name': 'Bertrand Thirion', 'orcid': '0000-0001-5018-7895'}, {'affiliation': "Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada", 'name': 'Pierre Bellec', 'orcid': '0000-0002-9111-0699'}], 'description': 'Docker image built from the  reference repository/commit by roboneuro, based on the latest change by the author, using repo2docker (through BinderHub). 
 To run locally:  docker load < DockerImage_10.55458_NeuroLibre_00012_b56875.zip
docker run -it --rm -p 8888:8888 DOCKER_IMAGE_ID jupyter lab --ip 0.0.0.0
 by replacing DOCKER_IMAGE_ID above with the respective ID of the Docker image loaded from the zip file.
 For details, please visit the corresponding NeuroLibre technical screening.
 \nhttps://neurolibre.org
', 'doi': '', 'keywords': ['canadian-open-neuroscience-platform', 'neurolibre'], 'license': 'CC-BY-4.0', 'prereserve_doi': {'doi': '10.5281/zenodo.7996516', 'recid': 7996516}, 'publication_date': '2023-06-01', 'related_identifiers': [{'identifier': '10.55458/neurolibre.00012', 'relation': 'isPartOf', 'resource_type': 'publication-preprint', 'scheme': 'doi'}], 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn', 'upload_type': 'software'}, 'modified': '2023-06-01T23:22:40.235031+00:00', 'owner': 262806, 'record_id': 7996516, 'state': 'unsubmitted', 'submitted': False, 'title': 'A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn'}}

neurolibre / neurolibre-reviews