From the talk given at Mind Research Network in February 2015.

1. Data sharing in neuroimaging:
incentives, tools, and challenges
Chris Gorgolewski
Department of Psychology
Stanford University

2. HOW CAN YOU BENEFIT FROM
DATA SHARING?

3. NKI Enhanced
• 329 subjects (will reach 1000)
– Representative sample: young and old, some with
mental health history
• 1 hour worth of MRI (3T) scanning:
– MPRAGE (TR = 1900; voxel size = 1mm isotropic)
– 3x resting state scans (645msec, 1400msec, and
2500msec)
– Diffusion Tensor Imaging (137 direction; voxel size
= 2mm isotropic)
– Visual Checkboard and Breath Holding
manipulations

5. fcon_1000.projects.nitrc.org/indi/
enhanced/

6. Human Connectome Project
• > 500 subjects (will reach 1200)
– Young and healthy (22-35yrs)
– 200 twins!
• 1 hour worth of MRI scanning:
– State of the art sequences – high temporal and spatial resolution
– Resting-state fMRI (R-fMRI)
– Task-evoked fMRI (T-fMRI)
• Working Memory
• Gambling
• Motor
• Language
• Social Cognition
• Relational Processing
• Emotion Processing
– Diffusion MRI (dMRI)
– MEG and EEG
– 7T coming soon

7. Human Connectome Project
• Rich phenotypical data
– Cognition, personality, substance abuse etc.
• Genotyping! (not yet available)
• Methodological developments
– Fine tuned sequences
– Innovative field inhomogeneity corrections
– New preprocessing techniques
• Ready to use preprocessed data

8. humanconnectome.org

9. FCP/INDI Usage Survey
Survey Courtesy of Stan Colcombe & Cameron Craddock
FCP/INDI Data Usage Description
Master's thesis research 11.94%
Doctoral dissertation research 38.81%
Teaching resource (projects or examples) 13.43%
Pilot data for grant applications 16.42%
Research intended for publication 76.12%
Independent study (e.g., teach self about analysis) 37.31%
FCP/INDI Users; 10% respondent rate

10. Growth of the reuse of OpenfMRI
datasets

12. Motivation
• Share
your
stat
maps!
vs.
institutions scientists

13. Data sharing saves money
$878,988
cost of reacquiring data for each of the
reuses of OpenfMRI datasets

14. Data sharing fears
• Fear of being scooped
• Fear of someone finding a mistake
• Misconceptions about the ownership of the
data

15. Studies sharing data have higher
statistical quality
Wicherts JM, Bakker M, Molenaar D (2011) Willingness to Share Research Data Is
Related to the Strength of the Evidence and the Quality of Reporting of
Statistical Results. PLoS ONE 6(11): e26828. doi: 10.1371/journal.pone.0026828

16. Neuroimaging data sharing
hierarchy
Poldrack and Gorgolewski, 2014

17. Just coordinates?
• Databases such as Neurosynth or
BrainMap rely on peak coordinates
reported in papers (only strong effects)

18. Are we throwing money away?

20. Baby steps
• Everything is a question of cost and benefit
– If we keep the cost low even small benefit (or
just conviction that data sharing is GOOD) will
suffice

21. NeuroVault.org
simple data sharing
• Minimize the cost!
• We just want your statistical maps with
minimum description (DOI)
– If you want you can put more metadata, but
you don’t have to
• We streamline login process (Google,
Facebook)

22. NeuroVault.org
Gorgolewski, et al., submitted

23. Benefits - visualisation

24. Benefits - decoding

25. Live demo

26. Benefits - other
• Private collections
• Multiple contributors to one collection
• Sharable persistent URLs
• Viewer embeddable on your labs website
or your private blog
• Improved exposure of your research
• Improved reusability of your results

27. Using NeuroVault…
• Improves collaboration
• Makes your paper more attractive
• Shows you care about transparency
• Takes only five minutes
• Gives you warm and fuzzy feeling that you
helped future meta-analyses

28. Validation and gains in sensitivity

29. NeuroVault for developers
• RESTful API (field tested by Neurosynth)
• Source code available on GitHub

30. What is NIDM-Results?

31. Neuroimaging data sharing
hierarchy
Poldrack and Gorgolewski, 2014

32. MAKING DATASHARING COUNT
Credit where credit’s due

33. Quality control
• Share
your
stat
maps!
Complex datasets require
elaborate descriptions

34. • Share
your
stat
maps!
How can we appropriately
reward extra effort and risk
related with sharing data?

35. Solution – data papers
• Authors get recognizable credit for their
work.
– Even smaller contributors such as RAs can be
included.
• Acquisition methods are described in
detail.
• Quality of metadata is being controlled by
peer review.

36. Gorgolewski, Milham, and Margulies, 2013

37. • Neuroinformatics (Springer)
• GigaScience (BGI, BioMed Central)
• Scientific Data (Nature Publising Group)
• F1000Research (Faculty of 1000)
• Data in Brief (Elsevier)
• Journal of Open Psychology Data (Ubiquity
press)
Where to publish data papers?

42. What makes a good data paper?
• Clear and accurate description of the
acquisition protocol.
• Good data organization.
• Ease of access to data.
• Data quality description.
• Fair credit attribution.

43. How to improve the impact of your
dataset?
• Provide preprocessed data.
• Reach out to your peers…
– …and people outside of your field (ML)
• Build a community around the data.

44. StudyForrest.org

45. Repositories
• Field specific
– OpenfMRI.org (task based fMRI)
– FCP/INDI (resting state fMRI)
– COINS
• Field agnostic
– DataVerse (Harvard)
– Figshare (only small datasets)
– DataDryad (fees may apply)

46. OpenfMRI
• Will host any dataset that has a task based
fMRI component
• No fees
• Curated and uncurated datasets
• Recommended by many journals (including
Scientific Data)

47. Prepare in advance
• Make sure your consent form includes data
sharing
• Decide which database you want to send
your data to in advance
– Organize your data according to their
requirements
• Work on anonymized data as much as you
can

48. If I haven’t convinced you yet
• Why to share data:
– It’s the ethical thing to do (Brakewood and
Poldrack 2013)
– The journal might require it (PLoS).
– Your funders might require it (NIH).
– Track record of data sharing can improve your
chances of getting your next grant.

49. Sharing data is related to higher
citation rate
Piwowar, Day & Fridsma (2007) Piwowar & Vision(2013)

50. Acknowledgements
Russell A. Poldrack
Jean-Baptiste Poline
Yannick Schwarz
Tal Yarkoni
Michael Milham
Daniel Margulies
Yannick Schwartz
Gael Varoquox
Joseph Wexler
Gabriel Rivera
Camile Maumet
Vanessa Sochat
Thomas Nichols
MPI CBS Resting state group
Poldrack Lab
INCF Data Sharing Task
Force