# Building a more collaborative neuroimaging science

Camille Maumet

► To cite this version:

Camille Maumet. Building a more collaborative neuroimaging science. Think Open Roverto Workshop 2020, Jul 2020, Trento (online), Italy. pp.1-46. inserm-02909432

HAL Id: inserm-02909432

https://inria.hal.science/inserm-02909432

Submitted on 30 Jul 2020

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Building a more
collaborative
neuroimaging science

<figure><img src="image_2.png" /></figure>

Camille Maumet

Univ Rennes, Inria, CNRS, Inserm

<figure><img src="image_3.png" /></figure>

@cmaumet
Sample sizes in
neuroimaging

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

[Poldrack et. al, Nature Neuroscience 2017]

2015 : 30 participants
per study
SCIENCE

# A Waste of 1,000 Research Papers

Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?

ED YONG MAY 17, 2019

<figure><img src="image_6.png" /></figure>

SEAN NEL / SHUTTERSTOCK

In 1996, a group of European researchers found that a certain gene, called *SLC6A4*, might influence a person's risk of depression.

It was a blockbuster discovery at the time. The team found that a less active version of the gene was more common among 454 people who had mood disorders than in 570 who did not. In theory, anyone who had this particular gene variant could be at higher risk for depression, and that finding, they said, might help in diagnosing such disorders, assessing suicidal behavior, or even

## Vibration effect
Why are middle-aged marathon
runners faster than
twentysomethings?

According to new data from the running app Strava, runners in their 40s are streets ahead of younger rivals

<figure><img src="image_7.png" /><figcaption>▲ 'Middle-aged runners outperform runners in the 20s ...' Photograph: FatCamera/Getty</figcaption></figure>

According to data released by the running app Strava, middle-aged runners consistently average faster marathon times than their younger rivals, apparently defying the usual rules of athletic performance. Men in the 40-49 age bracket clock an average time of four hours and 17 minutes for a marathon, according to the recent figures. Women in the same age range typically come in at just under the five-hour mark.

Faster in their forties
than twenties?

Selection bias
POLICY & ETHICS | OPINION

# Speech Recognition Tech Is Yet Another Example of Bias

Siri, Alexa and other programs sometimes have trouble with the accents and speech patterns of people from many underrepresented groups

By Claudia Lopez Lloreda on July 5, 2020

<figure><img src="image_8.png" /></figure>

Credit: Getty Images

"Claw-dia," I say once. Twice. A third time. Defeated, I say the Americanized version of my name: "Claw-dee-ah." Finally, Siri recognizes it.

Having to adapt our way of speaking to interact with speech recognition technologies is a familiar experience for people whose first language is not English or who do not have conventionally American-sounding names. I have even stopped using Siri because of it.

Lack of diversity
Sample sizes in
neuroimaging

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

[Poldrack et. al, Nature Neuroscience 2017]

2015 : 30 participants
per study

We need **bigger, more representative** and **more diverse** samples
Beyond papers...
# Publishing research artefacts

<figure><img src="image_11.png" /></figure>

Preprocessing

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Derived data

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Statistical analysis

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# Publishing research artefacts

<figure><img src="image_15.png" /></figure>

Preprocessing

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

Derived data

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

Statistical analysis

<figure><img src="image_18.png" /></figure>

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

<figure><img src="image_20.png" /></figure>
# Publishing research artefacts

<figure><img src="image_21.png" /></figure>
# Publishing research artefacts

<figure><img src="image_22.png" /></figure>

Preprocessing

<figure><img src="image_23.png" /></figure>

Derived data

<figure><img src="image_24.png" /></figure>

Statistical analysis

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<figure><img src="image_27.png" /></figure>

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HAL, BiorXiv
Pubmed

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# BIDS

**July 11, 11.50 - BIDS: a data standard to support the neuroimaging community**

Guiomar Niso

<figure><img src="image_31.png" /></figure>

* Used in over 60 labs around the world

* Adopted by: FCP-INDI, Developing Human Connectome, SchizConnect and Donders Data repository.

* Extensions: MEG, iEEG, EEG

<figure><img src="image_32.png" /></figure>

my_dataset/

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participants.csv

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sub-01/

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anat/

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sub-01_T1w.nii.gz

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func/

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sub-01_task-rest bold.nii.gz

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sub-01_task-rest bold.json
# Publishing research artefacts

<figure><img src="image_40.png" /></figure>

Preprocessing

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

Derived data

<figure><img src="image_42.png" /></figure>

Statistical analysis

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BIDS

BRAIN IMAGING DATA STRUCTURE

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

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HAL, BiorXiv
Pubmed
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

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

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<figure><img src="image_54.png" /></figure>

BIDS

BRAIN IMAGING DATA STRUCTURE

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

OpenfMRI

<figure><img src="image_57.png" /></figure>

OpenNEURO

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

SHANOIR

LORIS

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

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

HAL, BiorXiv
Pubmed

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

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# Publishing research artefacts

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

Preprocessing

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Derived data

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

Statistical analysis

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

BIDS

BRAIN IMAGING DATA STRUCTURE

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

OpenfMRI

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

OpenNEURO

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

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

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

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

SHANOIR

LORIS

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

HAL, BiorXiv
Pubmed

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

brainmap.org

neurosynth.org

brainspell

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>
# Publishing research artefacts

<figure><img src="image_80.png" /></figure>

Preprocessing

<figure><img src="image_81.png" /></figure>

Derived data

<figure><img src="image_82.png" /></figure>

Statistical analysis

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

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

BIDS
BRAIN IMAGING DATA STRUCTURE

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

OpenfMRI

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

OpenNEURO

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

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

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

SHANOIR

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

LORIS

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

HAL, BiorXiv
Pubmed

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

brainmap.org

neurosynth.org

brainspell

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

Image credits: Parcels 1 2 & 4 (CCO), Parcel 3 (CCO), Parcel 5 (CCO).
# fMRI Results

## Publication

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

<figure><img src="image_95.png" /><figcaption>Figure
(selected slices)</figcaption></figure>

<figure><img src="image_96.png" /><figcaption>Thresholded
statistics</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>Peak locations</figcaption></figure>

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

- Incomplete statistical results

- Ambiguous/incomplete methods

- Metadata is not searchable
# NIDM-Results pack

<figure><img src="image_99.png" /><figcaption>NIDM-Results
.nidm.zip</figcaption></figure>

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

ClusterLabels.nii.gz

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

Contrast.nii.gz

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

ContrastStandardError.nii.gz

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

DesignMatrix.csv

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

DesignMatrix.png

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

ExcursionSet.nii.gz

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

ExcursionSet.png

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

GrandMean.nii.gz

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

Mask.nii.gz

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

nidm.json

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

nidm.ttl

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

ParameterEstimate_001.nii.gz

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

ParameterEstimate_002.nii.gz

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

ResidualMeanSquares.nii.gz

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

SearchSpaceMask.nii.gz

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

TStatistic.nii.gz

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

ZStatistic.nii.gz
# NIDM-Results pack

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

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

NIDM-Results

.nidm.zip

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

ClusterLabels.nii.gz

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

Contrast.nii.gz

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

ContrastStandardError.nii.gz

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

DesignMatrix.csv

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

DesignMatrix.png

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

ExcursionSet.nii.gz

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

ExcursionSet.png

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

GrandMean.nii.gz

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

Mask.nii.gz

<figure><img src="image_128.png" /></figure>

nidm.json

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

nidm.ttl

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

ParameterEstimate_001.nii.gz

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

ParameterEstimate_002.nii.gz

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

ResidualMeanSquares.nii.gz

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

SearchSpaceMask.nii.gz

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

TStatistic.nii.gz

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ZStatistic.nii.gz

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

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

Design of the
experiment, pre-
processed data...

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

Statistical
estimation

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

Effects, standard error
and statistical maps...

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

Inference

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

Thresholded maps, list
of peaks and clusters...

<figure><img src="image_142.png" /></figure>
# NIDM in SPM, FSL & NeuroVault

## SPM

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

### 1. Export

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

$ nidmfsl fsl_ds107_group 49 -g Control

### 2. Publication on NeuroVault

NIDM-Results

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

<figure><img src="image_146.png" /></figure>
# Meta-analysis with NIDM-Results

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

## Coordinate-based meta-analysis

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

## Image-based meta-analysis

<figure><img src="image_149.png" /></figure>
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

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

BIDS
BRAIN IMAGING DATA STRUCTURE

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

OpenfMRI

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

OpenNEURO

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

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

SHANOIR

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

LORIS

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

HAL, BiorXiv
Pubmed

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

brainmap.org

neurosynth.org

brainspell

<figure><img src="image_161.png" /></figure>

NIDM

NEUROVAULT

<figure><img src="image_162.png" /></figure>
# Publishing research artefacts

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

Preprocessing

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

Derived data

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

Statistical analysis

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

BIDS
BRAIN IMAGING DATA STRUCTURE

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

???

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

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

OpenfMRI

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

OpenNEURO

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

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

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

SHANOIR

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

brainmap.org

HAL, BiorXiv

neurosynth.org

Pubmed

brainspell

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

NEUROVAULT

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

NIDM

Image credits: Parcels 1 2 & 4 (CCO), Parcel 3 (CCO), Parcel 5 (CCO).
A new challenge: dealing with analytic variability...
# Open data

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

Unique study

30 participants

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

OpenNEURO

studyforrest.org

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

NEUROVAULT

L I E L N L F T Q K T Q R V
S M Y C O N N E C T O M E Q
G S P K K W A R R G K E H R

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

NITRC

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

OSF

+ Images

+ Homogenous

- Datasets
# Open data

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

Unique study

30 participants

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

OpenNEURO

<figure><img src="image_184.png" /></figure>

NEUROVAULT

<figure><img src="image_185.png" /></figure>

NITRC

<figure><img src="image_186.png" /></figure>

OSF

Consortium

1000 participants

<figure><img src="image_187.png" /></figure>

ABIDE

Autism Brain Imaging
Data Exchange

1000 Functional Connectomes Project

L I E L N L F T Q K T Q R V
S M Y C O N N E C T O M E Q
G S P K K W A R R G K E H R

<figure><img src="image_188.png" /></figure>

CORR

CONSORTIUM FOR
RELIABILITY AND
REPRODUCIBILITY

<figure><img src="image_189.png" /></figure>

+ Images

+ Homogenous

- Datasets
# Open data

<figure><img src="image_190.png" /></figure>

Unique study

30 participants

<figure><img src="image_191.png" /></figure>

OpenNEURO

<figure><img src="image_192.png" /></figure>

NEUROVAULT

<figure><img src="image_193.png" /></figure>

NITRC

<figure><img src="image_194.png" /></figure>

OSF

Consortium

1000 participants

<figure><img src="image_195.png" /></figure>

ABIDE

Autism Brain Imaging
Data Exchange

1000 Functional Connectomes Project

<figure><img src="image_196.png" /></figure>

CORR

CONSORTIUM FOR
RELIABILITY AND
REPRODUCIBILITY

<figure><img src="image_197.png" /></figure>

Cohort

100 000 participants

<figure><img src="image_198.png" /></figure>

HUMAN
Connectome
PROJECT

biobank™
Improving the health of future generations

+ Images

+ Homogenous

- Datasets
# Working with open data

<figure><img src="image_199.png" /></figure>

Raw data

<figure><img src="image_200.png" /></figure>

Feature extraction

<figure><img src="image_201.png" /></figure>

Derived data

<figure><img src="image_202.png" /></figure>

Statistical analysis

<figure><img src="image_203.png" /></figure>

<figure><img src="image_204.png" /></figure>

Results
# Working with open data

<figure><img src="image_205.png" /></figure>

Raw data

Feature extraction

<figure><img src="image_206.png" /></figure>

Derived data

Statistical analysis

<figure><img src="image_207.png" /></figure>

<figure><img src="image_208.png" /></figure>

Results

Meta-analyses

Statistical analysis

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

Results
# Working with open data

<figure><img src="image_211.png" /></figure>

Raw data

<figure><img src="image_212.png" /></figure>

Raw data

Feature extraction

<figure><img src="image_213.png" /></figure>

Derived data

Statistical analysis

<figure><img src="image_214.png" /></figure>

<figure><img src="image_215.png" /></figure>

Results

Meta-analyses

Statistical analysis

<figure><img src="image_216.png" /></figure>

<figure><img src="image_217.png" /></figure>

Results
# Working with open data

<figure><img src="image_218.png" /></figure>

Raw data

<figure><img src="image_219.png" /></figure>

Raw data

Feature extraction

<figure><img src="image_220.png" /></figure>

Derived data

Statistical analysis

<figure><img src="image_221.png" /></figure>

<figure><img src="image_222.png" /></figure>

Results

Meta-analyses

<figure><img src="image_223.png" /></figure>

<figure><img src="image_224.png" /></figure>

Statistical analysis

Results
# Working with open data

<figure><img src="image_225.png" /></figure>

Raw data

<figure><img src="image_226.png" /></figure>

Raw data

<figure><img src="image_227.png" /></figure>

Feature extraction

Feature extraction

<figure><img src="image_228.png" /></figure>

Derived data

<figure><img src="image_229.png" /></figure>

Derived data

<figure><img src="image_230.png" /></figure>

Statistical analysis

<figure><img src="image_231.png" /></figure>

Statistical analysis

<figure><img src="image_232.png" /></figure>

<figure><img src="image_233.png" /></figure>

Results

<figure><img src="image_234.png" /></figure>

<figure><img src="image_235.png" /></figure>

Results

<figure><img src="image_236.png" /></figure>

Meta-analyses

<figure><img src="image_237.png" /></figure>
# Working with open data

<figure><img src="image_238.png" /></figure>
Analytic variability

"Different acceptable
analysis methods"

Carp et al. (2012)

<figure><img src="image_239.png" /></figure>

<figure><img src="image_240.png" /></figure>

≠ algorithm

<figure><img src="image_241.png" /></figure>

≠ software

<figure><img src="image_242.png" /></figure>

≠ software version

<figure><img src="image_243.png" /></figure>

≠ parameters

<figure><img src="image_244.png" /></figure>

≠ environment
<figure><img src="image_245.png" /></figure>

**July 10, 16.50** - The effect of analytical choices on fMRI findings

Rotem Botvinik-Nezer

# Question?

How does analytic variability impact neuroimaging results?
# BIDS Provenance

<figure><img src="image_246.png" /><figcaption>Satra Ghosh</figcaption></figure>

<figure><img src="image_247.png" /><figcaption>Rémi Adon</figcaption></figure>

<figure><img src="image_248.png" /></figure>

<figure><img src="image_249.png" /></figure>

BIDS
BRAIN IMAGING DATA STRUCTURE

<figure><img src="image_250.png" /></figure>

Looking for
contributors and
community input

Join us!
Working together as
a **community!**
# Brain standards by and for the community

## INCF

<figure><img src="image_251.png" /></figure>

enabling open and
FAIR neuroscience

### Working Groups/Special Interest Groups

Special Interest Groups are composed of users and developers from across the INCF network working collaboratively to develop, refine, and/or implement community standards. Working Groups are composed of SIG members working on short-term funded projects that aim to achieve a concrete deliverable.

**FAIR Metadata Working Group**

The aim of this working group is the harmonization of Common Data Elements (CDEs) for data discovery and metadata annotation.

Read more

**Neuroinformatics for Aging**

This SIG will be a community dedicated to the creation and application of neuroinformatics technologies to address clinical and wellness challenges in aging

Read more

**Neuroinformatics for cell types**

This SIG will coordinate common efforts for defining and describing cell types across neuroscience, to reduce duplicate efforts and to improve interoperability and reuse of cell type-specific data collected across groups.

Read more

**Neuroshapes: Open SHACL schemas for FAIR neuroscience data**

This SIG aims to coordinate community efforts for the development of open, use case driven and shared validatable data models (schemas, vocabularies) to enable the FAIR principles (Findable, Accessible, Interoperable and Reusable) for basic, computational and clinical neuroscience (meta)data.

Read more

**Reproducibility and Best Practices in Human Brain Imaging**

The SIG aim is to collect, compile, synthesize and distribute information from task forces working on separate projects but with reproducibility in neuroimaging as an overarching theme.

Read more

**Standardised Representations of Network Structures**

This SIG deals with the various tools and formats for creating and sharing representations of biological neuronal networks, and will work towards ensuring these are as interoperable and usable as possible for computational neuroscientists.

Read more

**Neuroimaging Quality Control (niQC)**

This SIG aims to develop standards and best practices for quality control of neuroimaging data, including standardized protocols, easy to use tools and comprehensive manuals

Read more

You can join a group or create your own!
Researching collaboratively
Hackathons

<figure><img src="image_252.png" /></figure>

BRAINHACK OPEN GENEVA 2019

BRAIN
HACK

<figure><img src="image_253.png" /></figure>

OPEN GENEVA 2019

Brainhack Open Geneva will take place from March 22-23, 2019

<figure><img src="image_254.png" /></figure>

BRAINHACK IN PADOVA: EVOLUTION

<figure><img src="image_255.png" /></figure>

BrainHack: Evolution of the Mind and the Brain

<figure><img src="image_256.png" /></figure>

EVENTS 2018

Brainhack in Padova will take place from December 5-7, 2018

<figure><img src="image_257.png" /></figure>

LA BRAINHACK 2018

<figure><img src="image_258.png" /></figure>

LA BRAINHACK 2018

EVENTS 2018

The LA Brainhack 2018 will take place November 18 - 19 at the Information Sciences Institute, Marina del Rey.

<figure><img src="image_259.png" /></figure>

BRAINHACK ZURICH 2018

<figure><img src="image_260.png" /></figure>

BRAIN
HACK
Oct
ZURICH
2018

EVENTS 2018

Brainhack Zurich 2018 will take place from October 24-26 at ETH Zurich & University Hospital Zurich.

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2018 OHBM HACKATHON

OHBM Brainhack 2018

Singapore
June 14-16, 2018

EVENTS 2018

The 2018 OHBM Hackathon will take place June 14 - 16 at the National University of Singapore, U-Town campus, Singapore.

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BRAINHACK NETWORKS

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EVENTS 2018

9 & 10 of June 2018, Brain and Spine Institute, Paris

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# Working open training
# Mozilla Open leaders

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## Mozilla Open Leaders

@MozOpenLeaders

Open Leaders are fueling the #internethealth movement.

Work Open, Lead Open #WOLO.

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mzl.la/openleaders

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Joined October 2016

## Open Sesame (Cohort C)

Notes and full recording.

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Some of the Open Sesame OL7 grads

Source:

https://medium.com/read-write-participate/open-leaders-7-demos-recap-8cee3423dbb5
# Developing open science in our community
OHBM Open Science SIG

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Our Team

Hackathon

Awards

Open Science Room

Sponsors

## The OHBM Open
Science Special
Interest Group

Dedicated to promoting and supporting open
science within our community

Learn more

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# Developing open science in our community
OHBM Open Science SIG 2020

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<figure><img src="image_275.png" /><figcaption>Rémi Gau</figcaption></figure>

<figure><img src="image_276.png" /><figcaption>Liza Levitis</figcaption></figure>

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Gould van Praag</figcaption></figure>

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Heunis</figcaption></figure>
# Developing open science in our community
## OHBM Open Science SIG 2020

Hub 1

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Asia, Pacific

Hub 2

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Europe, Middle East, Africa

Hub 3

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Americas

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<figure><img src="image_283.png" /><figcaption>Rémi Gau</figcaption></figure>

<figure><img src="image_284.png" /><figcaption>Liza Levitis</figcaption></figure>

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Gould van Praag</figcaption></figure>

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Heunis</figcaption></figure>
# Developing open science in our community
## OHBM Open Science SIG 2021

**Nominations are now open!** Details of the roles can be found [here] and you can self-nominate [here] before July 20th 2020 at 11:59 pm anywhere on earth

* General Chair Elect

* Secretary Elect

* Treasurer Elect

* Inclusivity Officer

* Hackathon Co-Chairs (two officers)

* Open Science Room Co-Chairs (two officers)

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https://ossig.netlify.app/
Thank you

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EMPENN

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https://team.inria.fr/empenn/

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https://ohbm.github.io/osr2020/volunteers/

https://ohbm.github.io/hackathon2020/team/
# Building a more collaborative neuroimaging science

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@cmaumet
camille.maumet@inria.fr

Thank you!

Join the community:

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Mozilla Open Leaders

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enabling open and
FAIR neuroscience