# Open science: a dynamic ecosystem

Gaëlle Leroux

► To cite this version:

Gaëlle Leroux. Open science: a dynamic ecosystem. Cutting gardens 2023, CuttingEEG, Oct 2023, Lyon, France. hal-04247531

HAL Id: hal-04247531

https://hal.science/hal-04247531v1

Submitted on 18 Oct 2023

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# Open Science:
a dynamic ecosystem

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Methodological
support
for imaging
projects

Gaëlle Leroux, PhD

Research engineer

gaelle.leroux@cnrs.fr

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https://orcid.org/0000-0003-2729-4945

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RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

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Inserm

La science pour la santé
From science to health

UNIVERSITE
DE LYON

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Lyon 1
# Why open up the science?

→ **to open up the results of science** (publications, research data where possible)

→ **but also a new way of doing research.**

This involves opening up processes, codes, methods and protocols.

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> It comes from a movement to **contest the monopoly position of the major publishers by promoting open access.**

Source: translation of a [Couperin Guide](#)

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> "Open science is a movement that aims to **involve new stakeholders, such as everyday citizens and non-research organisations**, in the research process, and to **make the products of research — everything from scientific publications and data to software programs — more accessible and reusable for the scientific community and for society at large**.

> Sharing research findings not only with other researchers but with society is **the best way to advance knowledge** and **build trustworthy relationships with multiple stakeholders**."

Source: [INRAE committed to open science](#)

Image credits: icons made by Dooder www.flaticon.com & Bonazzi/Salzmanart (2022) *Science*. doi:10.1126/science.adf4323
# Open Science

<figure><img src="image_12.png" /></figure>

The Open Science "umbrella" with **Open Science Foundation** badges (adapted).
*These are not the only terms under the umbrella*

Adapted from an [academic blog of Vu Univ., NL](#)
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The Turing Way

Data

<table><thead><tr><th></th><th></th><th>Same</th><th>Different</th></tr></thead><tbody><tr><th rowspan="2" scope="rowgroup">Analysis</th><th scope="row">Same</th><td>Reproducible</td><td>Replicable</td></tr><tr><th scope="row">Different</th><td>Robust</td><td>Generalisable</td></tr></tbody></table>

The Turing Way Community. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. doi: 10.5281/zenodo.3233853 (link to the schema)
The data deluge in EEG/MEG, as for science in general

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# Why Most Published Research Findings Are False

John P. A. Ioannidis

<table><tr><td>74,946<br>Save</td><td>7,388<br>Citation</td></tr><tr><td>3,001,940<br>View</td><td>9,576<br>Share</td></tr></table>

### Inadequate methodologies

A result is all the more likely to be false when:

- **effect sizes** are small

- there is a **greater number and lesser preselection** of tested relationships

- there is **greater flexibility** in designs, definitions, outcomes, and analytical modes

- there is greater **financial and other interest** and prejudice

+ the propensity of **journals to publish only positive results**

+ the "publish or perish" culture for researchers
IS THERE A REPRODUCIBILITY CRISIS?

<figure><img src="image_16.png" /></figure>

©nature

HAVE YOU FAILED TO REPRODUCE AN EXPERIMENT?

Most scientists have experienced failure to reproduce results.

<figure><img src="image_17.png" /></figure>

Baker (2016) 1,500 scientists lift the lid on reproducibility. *Nature*. https://doi.org/10.1038/533452a
<figure><img src="image_18.png" /></figure>

ReScience

Reproducible science is good. Replicated science is better.

Science ~~Broken Science~~

- Paywall

- Non-shared data

- Closed tools & Software

- Non-reproducible

- Anonymous peer-review

- Publish or Perish

<figure><img src="image_19.png" /></figure>

Open science ~~Science~~

- Open access

- Open data

- Open source

- Open methodology

- Open peer-review

- Much more fun...

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NO TRANSPARENCY
NO CONSENSUS

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SO HOW
DID YOU GET
THESE RESULTS?

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HOW DID
HE DO
THAT?

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HOW DO
I KNOW
IT'S TRUE?

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YOU WILL
JUST HAVE
TO TRUST ME

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https://pbs.twimg.com/media/Cjc UC4XAAAKaJl.jpg:large
Reproducibility Spectrum

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Peng (2011) Reproducible Research in Computational Science. Science https://doi.org/10.1126/science.1213847

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Key message #1: reproducibility is not binary but rather a continuum
# Open science is a cultural change

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Key message #2:
data sharing is
becoming the norm in
neuroimaging

I might add another layer at the bottom of the pyramid:
“Raise awareness of the problem.”

Burk, 2023. [Source](#)
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Key message #3
for project leaders:
consider data
sharing from the
beginning

FUTURE PARADIGM
CURRENT

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Key message #4
for project leaders:
consider data reuse
before collecting new
ones
# An open ecosystem of open tools

## STUDY INCEPTION AND PLANNING

<figure><img src="image_36.png" /></figure>

### Study Preparation

Ensure quality of data and design

* Release Standard Operating Procedures

* Pilot your experiment

<figure><img src="image_37.png" /></figure>

https://compose.neurosynth.org/

protocol exchange

<figure><img src="image_38.png" /></figure>

protocols.io

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https://dmp.opidor.fr/

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### Pre-Registration

Have a thorough research plan: study design, hypotheses and analysis

* Follow best practices

* Use pre-registration / Registered Reports, report deviations

<figure><img src="image_41.png" /></figure>

OSF
PREREGISTRATION

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AS PREDICTED

ClinicalTrials.gov

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
Pre-registration of a project
with peer-reviews: “registered reports”

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https://www.cos.io/initiatives/registered-reports

Registration Forms and Templates: https://osf.io/zab38/wiki/home/

Nosek et al. (2018) The preregistration revolution. *Proc. Natl Acad. Sci. USA* https://doi.org/10.1073/pnas.1708274114

Nosek & Lakens (2014) Registered reports: a method to increase the credibility of published results. *Soc. Psychol.* https://doi.org/10.1027/1864-9335/a000192
Pre-registration of a project
with peer-reviews: “registered reports”

<figure><img src="image_44.png" /></figure>

*Figure 2. Positive result rates for standard reports and Registered Reports. Error bars indicate 95% confidence intervals around the observed positive result rate.*

Scheel et al. (2021) An excess of positive results: Comparing the standard Psychology literature with Registered Reports. *Advances in Methods and Practices in Psychological Science*. https://doi.org/10.1177/25152459211007467
# An open ecosystem of open tools

## STUDY INCEPTION AND PLANNING

<figure><img src="image_45.png" /></figure>

### Study Preparation

Ensure quality of data and design

* Release Standard Operating Procedures

* Pilot your experiment

<figure><img src="image_46.png" /></figure>

https://compose.neurosynth.org/

protocol exchange

<figure><img src="image_47.png" /></figure>

protocols.io

<figure><img src="image_48.png" /></figure>

https://dmp.opidor.fr/

<figure><img src="image_49.png" /></figure>

### Pre-Registration

Have a thorough research plan: study design, hypotheses and analysis

* Follow best practices

* Use pre-registration / Registered Reports, report deviations

<figure><img src="image_50.png" /></figure>

OSF
PREREGISTRATION

<figure><img src="image_51.png" /></figure>

AS PREDICTED

ClinicalTrials.gov

<figure><img src="image_52.png" /></figure>

### Ethics and Sharing plan

Meet ethical and regulatory requirements

* Informed consent

* Internationally oriented data governance

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Open Brain
Consent

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### Monitoring Quality

Ensure quality of acquired data

* Establish quality control checkpoints

* Quality assurance

* Include positive control analyses

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ReproNim

MRIQC

XNAT

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Qoala-T

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VisualQC

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
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# Make open data sharing a no-brainer for ethics committees

## Sample consent forms & data user agreement in many languages (English, French, Italian, Spanish, etc.)

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https://open-brain-consent.readthedocs.io/en/stable/gdpr/index.html#gdpr-documents

Bannier et al. (2021) The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data. *Hum Brain Mapp.* https://doi.org/10.1002/hbm.25351
# An open ecosystem of open tools

## DATA ACQUISITION

<figure><img src="image_61.png" /></figure>

### Brain Data

Transparent and reproducible data acquisition

* Use open hardware

* Use open software

* Report full acquisition details

<figure><img src="image_62.png" /></figure>

open source
imaging

<figure><img src="image_63.png" /></figure>

OMR-Hub

<figure><img src="image_64.png" /></figure>

VENUS

<figure><img src="image_65.png" /></figure>

OPENBCI

<figure><img src="image_66.png" /></figure>

qMR
Lab

<figure><img src="image_67.png" /></figure>

### Stimulus

Reproducible stimulus presentations

* Report detailed stimuli, timings, responses, etc.

* Use open frameworks and share stimulus materials

<figure><img src="image_68.png" /></figure>

PsychoPy
and timing utilities

ERP CORE

<figure><img src="image_69.png" /></figure>

Pavlovia

<figure><img src="image_70.png" /></figure>

ReproStim

## RESEARCH DATA MANAGEMENT

<figure><img src="image_71.png" /></figure>

### Data organization and standards

Ensure data are interoperable and reusable

* Organize your neuroimaging data following the Brain Imaging Data Structure (BIDS)

* Utilize available tools, converters and apps that work with BIDS

<figure><img src="image_72.png" /></figure>

BIDS
BRAIN IMAGING DATA STRUCTURE

<figure><img src="image_73.png" /></figure>

NIFTI

<figure><img src="image_74.png" /></figure>

JSON

<figure><img src="image_75.png" /></figure>

TSV

Labguru

<figure><img src="image_76.png" /></figure>

eLabFTW

<figure><img src="image_77.png" /></figure>

BIDS

BRAIN IMAGING DATA STRUCTURE

https://bids.neuroimaging.io/

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
<figure><img src="image_78.png" /></figure>

<table><thead><tr><th></th><th colspan="6">modality</th></tr></thead><tbody><tr><th rowspan="6">datatype</th><th>MRI</th><th>PET</th><th>meeg</th><th>behavioral</th><th>microscopy</th><th>NIRS</th><th>motion</th></tr><tr><td>anat</td><td>pet</td><td>eeg</td><td>beh</td><td>micr</td><td>nirs</td><td>motion</td></tr><tr><td>func</td><td></td><td>meg</td><td></td><td></td><td></td><td></td></tr><tr><td>dwi</td><td></td><td>ieeg</td><td></td><td></td><td></td><td></td></tr><tr><td>perf</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

To be extended to:

* Animal electrophysiology

* Eyetracking

* Magnetic resonance spectroscopy

* ...
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sub-01

ses-01

anat

sub-01_ses-01_T1w.nii.gz

sub-01_ses-01_task-olfloc_run-01_eeg.edf

sub-01_ses-01_task-olfloc_run-01_events.tsv

sub-01_ses-01_task-olfloc_run-01 bid channels.tsv

Slide from pres. Rémi Gau @CRNL 2023

Pernet et al. (2019) EEG-BIDS, an extension to the brain imaging data structure for
electroencephalography. *Sci Data* https://doi.org/10.1038/s41597-019-0104-8
<figure><img src="image_80.png" /><figcaption>Arbitrarily organized data</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>MNE-BIDS</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>BIDS-compliant dataset</figcaption></figure>

https://github.com/mne-tools/mne-bids

Niso et al. (2018) MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. *Sci Data* https://doi.org/10.1038/sdata.2018.110
# An open ecosystem of open tools

## DATA ACQUISITION

<figure><img src="image_83.png" /></figure>

### Brain Data

Transparent and reproducible data acquisition

* Use open hardware

* Use open software

* Report full acquisition details

<figure><img src="image_84.png" /></figure>

open
source
imaging

<figure><img src="image_85.png" /></figure>

OMR-Hub

<figure><img src="image_86.png" /></figure>

VENUS

<figure><img src="image_87.png" /></figure>

OPENBCI

<figure><img src="image_88.png" /></figure>

qMR
Lab

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

### Stimulus

Reproducible stimulus presentations

* Report detailed stimuli, timings, responses, etc.

* Use open frameworks and share stimulus materials

<figure><img src="image_91.png" /></figure>

PsychoPy
and timing utilities

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>

Pavlovia

<figure><img src="image_94.png" /></figure>

ReproStim

## RESEARCH DATA MANAGEMENT

<figure><img src="image_95.png" /></figure>

### Data organization and standards

Ensure data are interoperable and reusable

* Organize your neuroimaging data following the Brain Imaging Data Structure (BIDS)

* Utilize available tools, converters and apps that work with BIDS

<figure><img src="image_96.png" /></figure>

BIDS

BRAIN IMAGING DATA STRUCTURE

<figure><img src="image_97.png" /></figure>

NIFTI

<figure><img src="image_98.png" /></figure>

JSON

<figure><img src="image_99.png" /></figure>

TSV

<figure><img src="image_100.png" /></figure>

### Metadata and data annotation

Ensure data are understandable, interoperable and reusable

* Annotate data using established structures

* Track provenance with Neuroimaging Data Model (NIDM)

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

PyNIDM
NIDM-Terms

<figure><img src="image_103.png" /></figure>

cognitive atlas

InterleX

CogPO

<figure><img src="image_104.png" /></figure>

### Data management and tracking

Track and version control data for update, retrieval, and reuse

* Use open data hosting services

* Use tools for decentralized data management

<figure><img src="image_105.png" /></figure>

Data
ladd

<figure><img src="image_106.png" /></figure>

OpenNEURO

<figure><img src="image_107.png" /></figure>

GIN

<figure><img src="image_108.png" /></figure>

brainlife

<figure><img src="image_109.png" /></figure>

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
# An open ecosystem of open tools

## DATA PROCESSING AND ANALYSIS

<figure><img src="image_110.png" /></figure>

### Software

Choose from the many open tools available

* Implement best practices when writing code, and share openly

* Test for analytical robustness

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

### Standardizing workflows

Enhance reproducibility

* Use standardized workflows for automation, reproducibility and transparency

* Use containers to ensure computational reproducibility

MRIQC
fMRIPrep

NiPreps

<figure><img src="image_113.png" /></figure>

MNE
MNE-BIDS
Pipeline

<figure><img src="image_114.png" /></figure>

BIDS

<figure><img src="image_115.png" /></figure>

[A]

<figure><img src="image_116.png" /></figure>

docker

<figure><img src="image_117.png" /></figure>

### Statistical modelling

Focus on reproducibility and transparency

* Use analysis pipelines with automatic (machine readable) documentation

* Use standardized structures

<figure><img src="image_118.png" /></figure>

ni
learn

<figure><img src="image_119.png" /></figure>

AFNI
FSL

FitLins
Neuroscout

BIDS-SM

<figure><img src="image_120.png" /></figure>

SPM

MNE

<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /></figure>

EEGLAB

<figure><img src="image_123.png" /></figure>

Brainstorm

<figure><img src="image_124.png" /></figure>

### Multiverse analysis

Explore effects of variations in analysis pipeline on study results

* Vary critical analysis parameters

* Re-run analysis with different tools and test for robustness

<figure><img src="image_125.png" /></figure>

FUZZY

<figure><img src="image_126.png" /></figure>

Explorable
Multiverse
Analyses

<figure><img src="image_127.png" /></figure>

### Key message #5:

Open data and analysis software are essential for reproducibility in data analysis

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
<figure><img src="image_128.png" /></figure>

The Open MEG Archive (OMEGA)

<figure><img src="image_129.png" /></figure>

The multidisciplinary
archive for French
research https://hal.science/

<figure><img src="image_130.png" /></figure>

Data sharing

FAIR (Findable, Accessible,
Interoperable, Reusable)

* Standardize data with rich metadata, and share it under persistent identifiers on public repositories and a license

* Publish Data Descriptors

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

NITRC

<figure><img src="image_135.png" /></figure>

brainlife.io

<figure><img src="image_136.png" /></figure>

Child Mind
Institute

INDI
Institute for Brain
Imaging and
Neuroimaging

OSF

zenodo

<figure><img src="image_137.png" /></figure>

Paper publication

Publish with openness and
reproducibility in mind

* Follow best practices

* Publish on preprint servers and/or
open access journals

bioRxiv

medRxiv

<figure><img src="image_138.png" /></figure>

NeuroLibre

CO

CODE OCEAN

<figure><img src="image_139.png" /></figure>

PsyArXiv

<figure><img src="image_140.png" /></figure>

OSF PREPRINTS

jupyter {book}

Open Research Europe

DISSEMINATION

<figure><img src="image_141.png" /></figure>

Code sharing

FAIR for reproducible analysis

* Document code and share it on public repositories + license

* Use version control

* Use containers

<figure><img src="image_142.png" /></figure>

cascad

<figure><img src="image_143.png" /></figure>

git

<figure><img src="image_144.png" /></figure>

GitHub

GitLab

<figure><img src="image_145.png" /></figure>

binder

<figure><img src="image_146.png" /></figure>

<figure><img src="image_147.png" /></figure>

<figure><img src="image_148.png" /></figure>

<figure><img src="image_149.png" /></figure>

<figure><img src="image_150.png" /></figure>

zenodo

<figure><img src="image_151.png" /></figure>

GIN

<figure><img src="image_152.png" /></figure>

Derived data sharing

FAIR derived data and results

* Share results of analyses in an interoperable and understandable way, so they are reusable and reproducible

<figure><img src="image_153.png" /></figure>

NEUROVAULT

<figure><img src="image_154.png" /></figure>

brainlife

<figure><img src="image_155.png" /></figure>

zenodo

<figure><img src="image_156.png" /></figure>

EBRAINS

<figure><img src="image_157.png" /></figure>

Software Heritage

https://www.softwareheritage.org/

<figure><img src="image_158.png" /></figure>

cascad

the first certification
agency for scientific
code & data

https://www.cascad.tech/

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
Artemis

English (US)

study_id

Design and sample

Hardware

Acquisition

Preprocessing

Measurement

Channel choice

Visualization

Other

<figure><img src="image_159.png" /></figure>

Home

ARTEMIS readme

<figure><img src="image_160.png" /></figure>

ARTEM-IS

Agreed Reporting Template for EEG Methodology -
International Standard: template for event-related
potentials (ERP)

The goal of the ARTEM-IS is to use insights derived from systematic reviews to create human-friendly, machine-readable templates for documenting the methodological details of an EEG study or pipeline. The template for reporting ERP methodology before you is a pilot project of ARTEM-IS, based on which other subfields of EEG may follow.

https://remi-gau.github.io/artemis_checklist/#/
<figure><img src="image_161.png" /></figure>

## The Open MEG Archive (OMEGA)

<figure><img src="image_162.png" /></figure>

<figure><img src="image_163.png" /></figure>

The multidisciplinary
archive for French
research

<figure><img src="image_164.png" /></figure>

### Data sharing

FAIR (Findable, Accessible, Interoperable, Reusable)

* Standardize data with rich metadata, and share it under persistent identifiers on public repositories and a license

* Publish Data Descriptors

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>

NITRC

<figure><img src="image_169.png" /></figure>

brainlife.io

<figure><img src="image_170.png" /></figure>

OSF

<figure><img src="image_171.png" /></figure>

<figure><img src="image_172.png" /></figure>

Child Mind Institute

BIDS

<figure><img src="image_173.png" /></figure>

### Paper publication

Publish with openness and reproducibility in mind

* Follow best practices

* Publish on preprint servers and/or open access journals

bioRxiv

<figure><img src="image_174.png" /></figure>

NeuroLibre

CO

CODE OCEAN

medRxiv

<figure><img src="image_175.png" /></figure>

PsyArXiv

<figure><img src="image_176.png" /></figure>

OSF PREPRINTS

jupyter {book}

Open Research Europe

<figure><img src="image_177.png" /></figure>

### Code sharing

FAIR for reproducible analysis

* Document code and share it on public repositories + license

* Use version control

* Use containers

cascad

<figure><img src="image_178.png" /></figure>

git

<figure><img src="image_179.png" /></figure>

<figure><img src="image_180.png" /></figure>

GitHub

GitLab

<figure><img src="image_181.png" /></figure>

<figure><img src="image_182.png" /></figure>

<figure><img src="image_183.png" /></figure>

OSF

CO
CODE OCEAN

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GIN

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### Beyond publication

Disseminate scientific results to the broader scientific community and to the society in general

* Conferences, workshops, mass media, social networks

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brainhack.org

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### Derived data sharing

FAIR derived data and results

* Share results of analyses in an interoperable and understandable way, so they are reusable and reproducible

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NEUROVAULT

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EBRAINS

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OSF
zenodo

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### Inclusive, diverse community research

Open resources and welcoming spaces, diversity of ideas and approaches

* Equity, diversity and inclusivity

* Impact on careers, work-life balance, and mental health

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Women in
Neuroscience

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CARE Principles
for Indigenous
Data Governance

Niso et al. (2022) Open and reproducible neuroimaging: From study inception to publication. *NeuroImage*. https://doi.org/10.1016/j.neuroimage.2022.119623 - https://oreoni.github.io/index.html
## Some readings

The thematic booklets of the Passport for Open Science collection are now available in english!

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## Workshops

National Reproducibility Network

Grenoble, France

26-28th March 2024

https://www.recherche-reproductible.fr/

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http://open-neuro.org
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Where ever we are on the Open Science journey: there are tools to make it easier and more reproducible.

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You're not alone!
You're surrounded by people
happy to help in your
communities

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Humans make mistakes: self-correct the bugs and check the impact of them.

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Image credit: icons made by Freepik, VectorPortal, SBTS2018 on www.flaticon.com
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This work has been only possible with the valuable help of:

**Olivier Bertrand, Director of the CRNL**

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**The Open Science group at the CRNL:** Pierre-Emmanuel Aguera, Olivier Bertrand, Laurent Bezin, Anne Cheylus, **Justine Épinat-Duclos**, Alexandra Fargeot, Guillaume Sescousse*

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CRNL think tanks on scientific integrity leaded by Fabien Chauveau, Nicolas Fourcaud-Trocmé, Nicolas Grimault, Nicola Kuczewski*; on Methodology leaded by Alexandra Corneyllie & Françoise Lecaignard* and on Neurolmaging with Justine Épinat-Duclos & Isabelle Faillenot*

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CRNL working groups on Open Access, Data Management Plans, Electronic Lab Notebooks

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Inspiring colleagues in networks & communities

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BIDS
BRAIN IMAGING DATA STRUCTURE

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Ouvrir
la science !

Research data College

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Former and current colleagues who support and challenge open science to make a better science.