1) The document discusses supporting research data management at INRA. It outlines key dates in developing INRA's data sharing policy and recommendations from a 2012 report. 2) Researchers at INRA produce various types of data from genetics to social sciences. Current data sharing practices and pain points are discussed for each domain. 3) A proposed service offer is outlined to support data management and sharing. This includes guidance, training, a digital repository leveraging existing repositories, and additional services like DOI minting and vocabularies.

1. Support for research data management
Esther Dzalé Yeumo 21 September 2015

2. .02
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Agenda
 Introduction
 What data do we produce at INRA
 Current data management and sharing practices and pain points: the point of
view of some researchers
 The service offer under construction

3. .03
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
2009: political awarness of Inra CEO
 2012: Report* of Inra scientific council « data management
and sharing »
 9 recommendations, 1st : define Inra Policy
 2013: Inra data sharing policy
 11 data management and sharing principles
 Implementation
 Domain specific working groups  inventory, requirements
 Trans-disciplinary working groups  state of the art (legal/IP,
technical, social issues), proposals
Some key dates
Introduction
*Gaspin, C., Pontier, D., Colinet, L., Dardel, F., Franc, A., Hologne, O., Le Gall, O., Maurin, N., Perrière, G., Pichot, C., Rodolphe, F. (2012).
Rapport du groupe de travail sur la gestion et le partage des données http://prodinra.inra.fr/record/206746

4. What data?

5. .05
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Current data sharing practices: the point of view of some
scientists
E. Dzalé Yeumo, O. Hologne
16/juillet/2015
Genetic &
genomic
- In general, data is released
once the producers have
published a paper
- Many data sharing
platforms exist at the
national and international
level
- Many metadata and data
format standards exist
- Lots of data is produced in
collaboration with public and
private partners
Experimentation,
observation and
simulation
- Raw data may be of as
interest as processed data
- Data sharing rules depends
on the nature, granularity,
and origin of the data
- The importance of
metadata is of paramount
for the reusability of the
released data
- Lots of data is unique, and
can be captured only once
Social sciences
- A few data sharing
platforms exist at the
national and international
level
- Lots of data are are bought
and can’t be freely released
- Experimental economics
data can be freely released
most of the time
- Data documentation and
statistical disclosure are of
paramount importance
- The importance of the
longitudinal aspect (range or
historical aspect) of the data
increases the need for their
long term preservation
Practices

6. .06
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Current data management pain points: the point of view of some
scientists
E. Dzalé Yeumo, O. Hologne
16/juillet/2015
Genetic &
genomic
- Data is more and more
massive  economical
model for long term
hosting
- Exchange, transfer and
storage of large datasets
- Lack of human resources
- Gaps in semantic
coverage
- Many existing public data
repositories are congested
with important waiting
time for the deposit of
datasets
- Risk of conferring an
economic advantage to
our competitors or to our
partners competitors
Experimentation,
observation and
simulation
- Data standardization
- Exchange, transfer and
storage of large datasets
- Capturing metadata
automatically
- Gaps in semantic
coverage
- Metadata may be
strategic (e.g protocols,
methods)
- Some metadata or data
are sensitive (geographic
information about
epidemiologic data or
GMO data)
- Exchange of large
datasets
Social sciences
- Data archiving:
sustainability of the
existing platforms
- Statistical disclosure
control
- Lots of personal and
sensitive data (social and
economical survey data)
 risk of re-identification
- Legal and intellectual
property issues are of
paramount importance
(ex: data inferred from
existing textual data
through decision tools,
data purchased from third
parties)
Pain points

7. .07
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
What service offer to support the data management
and sharing?

8. .08
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
The scientists whish list
E. Dzalé Yeumo, O. Hologne
16/juillet/2015
Genetic &
genomic
- Provide templates for consortium
agreements + identify special cases
(biological material, long tradition of
partnership, etc.)
- A data portal with access to all the
data shared by INRA
- DOIs
- Metadata training
- Volunteering for species-related data
repositories at an international level
Experimentation,
observation and
simulation
- Recognition of all the contributors
- Recognition of data sharing as first
class skill at the institutional level
- Data papers training
- A data portal with access to all the
data shared by INRA
- Harmonization of metadata standards
and vocabularies
Social sciences
- Guidance with regards to publishing
derived data: when, how?
- Webscrapping data of interest which
may be removed from original sources
- DOIs
- A secured data platform that allows
reviewers to access data and
reproduce findings with respect to
legal and IP requirements.
- Thematic active data management
platforms
Legal, IP , social
Technics,
methods, tools

9. .09
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Awareness, guidance, training

10. A digital repository
 Base on the IT environment provided by the IT department:
 Two data centers
 The Netapp Storage Virtual Machine technology
 Outsource the long term preservation
 Leverage the many existing data repositories
 Upgrade the existing repositories up to trusted repositories in accordance with the Data
Seal of Approval and the defra assessement grids:
http://www.datasealofapproval.org/en/assessment/ and
https://defradigital.blog.gov.uk/2015/02/09/are-you-a-mature-open-data-publisher/
 Conform to the OAIS reference model
 Cover both active and historical data

11. Additional services
DOI MINTING SERVICE VOCABULARIES PUBLISHING SERVICE

12. Training
 Technical school in data papers
 Technical school in Linked Data and how to publish data according to
Linked Data principles

13. .013
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Thank you!

14. .014
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015
Appendix: 11 data sharing and management principles

15. .015
E. Dzalé Yeumo/ IG Agriculture meeting 21 September 2015