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The Donders Repository - removing
barriers for the management and
sharing of research data
Robert Oostenveld
DCCN, Donders Instititute, Radboud University
NatMEG, Karolinska Institute
r.oostenveld@donders.ru.nl
Why share data?
Publishers require it
Funders require it
It is just the “right thing to do”
Why manage research data?
Improve efficiency and quality of research
Researchers can use shared data to jump-start new projects
Research findings can be re-visited upon new insights
Open Science
Open educational resources
Open access publications
Open peer review
Open methodology
Open source
Open hardware
Open data
Markus Neuschäfer; https://www.slideshare.net/mneuschaefer/1504-open-knowledgefolien
Open data versus privacy
Personal data
name
address
date of birth
phone number
license plate number
IP address
...
Crime Scene Investigation
http://www.abc.net.au/news/2017-09-19/csi/8960590
(Biometric) data
facial details
dental record
fingerprint
genetics
cortical folding pattern
clinical data
gait/movement pattern
responses on questionnaires
Personal Data is needed
and should be managed
Required for administration
Contacting your participants
Paying your participants
Follow up incidental findings
Often not required to address the research question
Sometimes used as confound (e.g. age)
Check whether the sample is representative (e.g. gender
distribution, age range)
Possibly required to assess scientific integrity (e.g. fabricated
survey data or fake interviews)
https://radboud.sona-systems.com
Personal Data
Personal data
Name, address, date of birth
Special personal data = “bijzondere persoonsgegevens”
Race
Religion or beliefs
Health
Sexual activities
Political preference, membership of a union
Criminal record
Indirect personal data – identifies someone when linked to another database
Fingerprint, DNA, facial details
Anatomical MRI
Specific pattern of data (e.g. answers on a questionnaire or interview)
https://autoriteitpersoonsgegevens.nl/nl/over-privacy/persoonsgegevens/wat-zijn-persoonsgegevens
Gradient between
personal and research data
indirect personal
data
personal data
a lot of research data
easy easyhard
Keep private
and don’t share
Share as it is
with others
?
Limit possible identification
Personal data
restrict access to personal data
protect the key that maps between the pseudonym and the identity
Biometric data
data minimization only acquire, store and share data that is needed
acquire anonymous data
acquire data using a pseudonym
use de-identification techniques
Legal constraints
collaboration: access only for authorized researchers (DAC/RDC)
sharing: access only data following data use agreement (DSC)
Limit possible identification
Anonymous
You never knew the subjects identity to start with
Pseudonymization
Use a code instead of the subjects name
De-identification
Remove (indirectly) identifying features
Blur the indirect personal data
Deface anatomical MRI
Age at the time of acquisition instead of date of birth
Use age bins instead of years
Questionnaire outcomes rather than individual item scores
…
Appropriate blurring depends
on the situation
… for example blurring the age of the subject
1 month bins 5 or 10 year bins
Personal and research data
indirect personal
data
personal data
a lot of research data
Personal and research data
data minimization
pseudonymization
data minimization
de-identifying, blurring
alotofresearchdata
personaldata
indirect
personaldata
Donders
research data
repository
Keep safe
and private
Goals of the
Donders Repository
Secure the original research data
Document the research process
Make the data accessible to the right people
Your PI, your current collaborators, your future successors
Your audience, i.e. sharing of open data
Keep the data accessible in the future (beyond your contract)
http://www.berylgraham.com/asunder/mods/met3dvdl.htm
Research
initiation
Data
acquisition
Data analysis &
documentation
Data sharing
Organization of the
Donders Repository
Different collections
DAC for raw data
RDC for processed data
DSC for open access data
Different roles
manager
contributor
viewer
data steward
external reviewer (at publication)
research administrator
system administrator
Note that directly
identifying personal data
should never go into the
research data repository
Findable
One location (versus scattered over many disks)
Some metadata (title, abstract, contributors, etc.)
Accessible
Storage system that others can access as well
Interoperable
Not addressed by DR system itself, the format and
organization of data files is domain specific
Re-usable
Policies and access mechanism for other researchers
(current/future colleagues, external researchers)
Findable Accessible Interoperable Re-usable
Collaborations based on data
Collection manager can give other researchers
(direct colleagues and/or collaborators) access
Also for (inter)national collaborations
Note that the DR system is still under development and the authorization
process (i.e. logging in) is being improved.
Open access data sharing
Enable replication
Improve reproducibility
Allow others to address different questions
Share with “everyone” under legal constraints on
the use of possibly identifying data
Data Use Agreement (DUA) for human data
Your collection gets a persistent identifier,
e.g. 11633/di.dccn.DSC_3015000.00_734
Data Use Agreement - Data Sharing Collections
Data use agreement for identifiable human data
Version RU-DI-HD-1.0
I request access to the data collected in the digital repository of the Donders Institute for Brain, Cognition and Behaviour,
part of the Radboud University, established at Nijmegen, the Netherlands (hereinafter referred to as the Donders Institute),
and I agree to the following:
1. I will comply with all relevant rules and regulations imposed by my institution and my government. This may mean that I
need my research to be approved or declared exempt by a committee that oversees research on human subjects, e.g. my
Institutional Review Board or Ethics Committee.
2. I will not attempt to establish the identity of or attempt to contact any of the included human subjects. I will not link this
data to any other database in a way that could provide identifying information. I understand that under no circumstances
will the code that would link these data to an individuals personal information be given to me, nor will any additional
information about individual subjects be released to me under these Data Use Terms.
3. I will not redistribute or share the data with others, including individuals in my research group, unless they have
independently applied and been granted access to this data.
4. I will acknowledge the use of the data and data derived from the data when publicly presenting any results or algorithms
that benefitted from their use.
(a) Papers, book chapters, books, posters, oral presentations, and all other presentations of results derived from the data
should acknowledge the origin of the data as follows: "Data were provided (in part) by the Donders Institute for Brain,
Cognition and Behaviour".
(b) Authors of publications or presentations using the data should cite relevant publications describing the methods
developed and used by the Donders Institute to acquire and process the data. The specific publications that are appropriate
to cite in any given study will depend on what the data were used and for what purposes. When applicable, a list of
publications will be included in the collection.
(c) Neither the Donders Institute or Radboud University, nor the researchers that provide this data should be included as an
author of publications or presentations if this authorship would be based solely on the use of this data.
5. Failure to abide by these guidelines will result in termination of my privileges to access to these data.
https://data.donders.ru.nl/doc/dua/
I will not attempt to establish the identity of or attempt to
contact any of the included human subjects. I will not link this
data to any other database that could provide identifying …
Donders Repository - demonstration
Web interface for managing collection properties and roles (metadata)
please go to http://data.donders.ru.nl and sign up
File browser interface for managing collection content (data)
please go to https://cyberduck.io and install, or use file Explorer/Finder
Online training and documentation (help)
please go to http://www.ru.nl/donders/research/data/ for instructions
Questions?
Work in progress - managing all your
documents and data
The Donders Repository is for archiving and sharing, but data management also involves
D: drive in your desktop
USB disk attached to your desktop
Your network drive
Werkgroupmappen
Lab computers
CastorEDC
Storage attached to the compute cluster
SurfDrive
Dropbox, Google Drive, OneDrive, …
DRE – Digital Research Environment
…
These all serve specific purposes, there is not one to replaces all others.
Where to publish/share your data
Institutional Repository
Donders Repository, elsewhere on RU repository
pilots for BSI, Faculty of Arts (including CLS), RIMLS-
FMWI, …
Generic repositories (note the DUA)
Zenodo, DataVerse, DataDryad, …
Specific repositories
Genetics, astromomy, openfmri, …

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Donders Repository - removing barriers for management and sharing of research data

  • 1. The Donders Repository - removing barriers for the management and sharing of research data Robert Oostenveld DCCN, Donders Instititute, Radboud University NatMEG, Karolinska Institute r.oostenveld@donders.ru.nl
  • 2. Why share data? Publishers require it Funders require it It is just the “right thing to do” Why manage research data? Improve efficiency and quality of research Researchers can use shared data to jump-start new projects Research findings can be re-visited upon new insights
  • 3. Open Science Open educational resources Open access publications Open peer review Open methodology Open source Open hardware Open data Markus Neuschäfer; https://www.slideshare.net/mneuschaefer/1504-open-knowledgefolien
  • 5. Personal data name address date of birth phone number license plate number IP address ... Crime Scene Investigation http://www.abc.net.au/news/2017-09-19/csi/8960590
  • 6. (Biometric) data facial details dental record fingerprint genetics cortical folding pattern clinical data gait/movement pattern responses on questionnaires
  • 7. Personal Data is needed and should be managed Required for administration Contacting your participants Paying your participants Follow up incidental findings Often not required to address the research question Sometimes used as confound (e.g. age) Check whether the sample is representative (e.g. gender distribution, age range) Possibly required to assess scientific integrity (e.g. fabricated survey data or fake interviews) https://radboud.sona-systems.com
  • 8. Personal Data Personal data Name, address, date of birth Special personal data = “bijzondere persoonsgegevens” Race Religion or beliefs Health Sexual activities Political preference, membership of a union Criminal record Indirect personal data – identifies someone when linked to another database Fingerprint, DNA, facial details Anatomical MRI Specific pattern of data (e.g. answers on a questionnaire or interview) https://autoriteitpersoonsgegevens.nl/nl/over-privacy/persoonsgegevens/wat-zijn-persoonsgegevens
  • 9. Gradient between personal and research data indirect personal data personal data a lot of research data easy easyhard Keep private and don’t share Share as it is with others ?
  • 10. Limit possible identification Personal data restrict access to personal data protect the key that maps between the pseudonym and the identity Biometric data data minimization only acquire, store and share data that is needed acquire anonymous data acquire data using a pseudonym use de-identification techniques Legal constraints collaboration: access only for authorized researchers (DAC/RDC) sharing: access only data following data use agreement (DSC)
  • 11. Limit possible identification Anonymous You never knew the subjects identity to start with Pseudonymization Use a code instead of the subjects name De-identification Remove (indirectly) identifying features Blur the indirect personal data Deface anatomical MRI Age at the time of acquisition instead of date of birth Use age bins instead of years Questionnaire outcomes rather than individual item scores …
  • 12. Appropriate blurring depends on the situation … for example blurring the age of the subject 1 month bins 5 or 10 year bins
  • 13. Personal and research data indirect personal data personal data a lot of research data
  • 14. Personal and research data data minimization pseudonymization data minimization de-identifying, blurring alotofresearchdata personaldata indirect personaldata Donders research data repository Keep safe and private
  • 15. Goals of the Donders Repository Secure the original research data Document the research process Make the data accessible to the right people Your PI, your current collaborators, your future successors Your audience, i.e. sharing of open data Keep the data accessible in the future (beyond your contract) http://www.berylgraham.com/asunder/mods/met3dvdl.htm Research initiation Data acquisition Data analysis & documentation Data sharing
  • 16. Organization of the Donders Repository Different collections DAC for raw data RDC for processed data DSC for open access data Different roles manager contributor viewer data steward external reviewer (at publication) research administrator system administrator Note that directly identifying personal data should never go into the research data repository
  • 17. Findable One location (versus scattered over many disks) Some metadata (title, abstract, contributors, etc.) Accessible Storage system that others can access as well Interoperable Not addressed by DR system itself, the format and organization of data files is domain specific Re-usable Policies and access mechanism for other researchers (current/future colleagues, external researchers) Findable Accessible Interoperable Re-usable
  • 18. Collaborations based on data Collection manager can give other researchers (direct colleagues and/or collaborators) access Also for (inter)national collaborations Note that the DR system is still under development and the authorization process (i.e. logging in) is being improved.
  • 19. Open access data sharing Enable replication Improve reproducibility Allow others to address different questions Share with “everyone” under legal constraints on the use of possibly identifying data Data Use Agreement (DUA) for human data Your collection gets a persistent identifier, e.g. 11633/di.dccn.DSC_3015000.00_734
  • 20. Data Use Agreement - Data Sharing Collections Data use agreement for identifiable human data Version RU-DI-HD-1.0 I request access to the data collected in the digital repository of the Donders Institute for Brain, Cognition and Behaviour, part of the Radboud University, established at Nijmegen, the Netherlands (hereinafter referred to as the Donders Institute), and I agree to the following: 1. I will comply with all relevant rules and regulations imposed by my institution and my government. This may mean that I need my research to be approved or declared exempt by a committee that oversees research on human subjects, e.g. my Institutional Review Board or Ethics Committee. 2. I will not attempt to establish the identity of or attempt to contact any of the included human subjects. I will not link this data to any other database in a way that could provide identifying information. I understand that under no circumstances will the code that would link these data to an individuals personal information be given to me, nor will any additional information about individual subjects be released to me under these Data Use Terms. 3. I will not redistribute or share the data with others, including individuals in my research group, unless they have independently applied and been granted access to this data. 4. I will acknowledge the use of the data and data derived from the data when publicly presenting any results or algorithms that benefitted from their use. (a) Papers, book chapters, books, posters, oral presentations, and all other presentations of results derived from the data should acknowledge the origin of the data as follows: "Data were provided (in part) by the Donders Institute for Brain, Cognition and Behaviour". (b) Authors of publications or presentations using the data should cite relevant publications describing the methods developed and used by the Donders Institute to acquire and process the data. The specific publications that are appropriate to cite in any given study will depend on what the data were used and for what purposes. When applicable, a list of publications will be included in the collection. (c) Neither the Donders Institute or Radboud University, nor the researchers that provide this data should be included as an author of publications or presentations if this authorship would be based solely on the use of this data. 5. Failure to abide by these guidelines will result in termination of my privileges to access to these data. https://data.donders.ru.nl/doc/dua/ I will not attempt to establish the identity of or attempt to contact any of the included human subjects. I will not link this data to any other database that could provide identifying …
  • 21. Donders Repository - demonstration Web interface for managing collection properties and roles (metadata) please go to http://data.donders.ru.nl and sign up File browser interface for managing collection content (data) please go to https://cyberduck.io and install, or use file Explorer/Finder Online training and documentation (help) please go to http://www.ru.nl/donders/research/data/ for instructions
  • 23. Work in progress - managing all your documents and data The Donders Repository is for archiving and sharing, but data management also involves D: drive in your desktop USB disk attached to your desktop Your network drive Werkgroupmappen Lab computers CastorEDC Storage attached to the compute cluster SurfDrive Dropbox, Google Drive, OneDrive, … DRE – Digital Research Environment … These all serve specific purposes, there is not one to replaces all others.
  • 24. Where to publish/share your data Institutional Repository Donders Repository, elsewhere on RU repository pilots for BSI, Faculty of Arts (including CLS), RIMLS- FMWI, … Generic repositories (note the DUA) Zenodo, DataVerse, DataDryad, … Specific repositories Genetics, astromomy, openfmri, …

Editor's Notes

  1. aan begin registreren deel 1: instemmen met een DUA -> toevoegen als viewer collection details op webdav link klikken -> open in new tab username en OTP invullen en browsen deel 2: ik maak een DAC met Eric als initial manager Eric logt in op Firefox browser Eric maakt de deelnemers contributor dan klikken ze wat rond, veranderen wat attributen deel 3: deelnemers installeren cyberduck configureren cyberduck maken verbinding en voegen iets kleins toe aan hun collectie met Eric wat laten we niet zien - stager - configurable content - emails - user management door research admin - documentatie beperkt