**Recognising data sharing** Chris Keene, Jisc

2. Chris Keene – Jisc
Iain Hrynaszkiewicz – Springer Nature
Recognising Data Sharing

3. Recognition
Slide: M Humphries; Photo: C Ingram

4. 3 key strands
» 1. Data download metrics
»2. Critically engaging with the debates around data citation
» 3. Ensuring that data sharing is recognised within existing modes of
recognition

5. 1. Data Download Metrics
» There is a demand for an accurate understanding of how data is
reused and at what level.
» This supports the development of RDM services and curation and
storage planning.
» It also reassures researchers that their shared data is being
accessed and used.
» Research data downloads need to be analysed in different ways to
article downloads.

6. From IRUS-UK…
» Offers COUNTER compliant download metrics for institutional
repositories
» 127 institutions use this popular Jisc service, users are very positive
about it
» stable and scalable architecture
» Connects to common article repository systems.

7. … to IRUS for Data
» Ability to return statistics at file and item levels
» Connectability to data specific and bespoke repositories
» COUNTER for data
» More modern back-end architecture
» A fully featured API
» A new, API driven, visual user interface
» 20+ test sites, using multiple platforms
On the
way
On the
way
On the
way

8. 24 live test sites…
28/06/2017 Jisc RDN Recogising data sharing
Repository Software Platform
Aston Data Explorer Eprints
Brighton Research Data Eprints
Cranfield figshare figshare
Apollo – University of
Cambridge Repositiry
DSpace
data.bris Research Data
Repository
CKAN
DataSTORRE: Stirling Online
Repository for Research Data
DSpace
Edinburgh DataShare DSpace
Lincoln Repository Eprints
Loughborough figshare figshare
Loughborough University
Institutional Repository
DSpace
LSE Research Online Eprints
LSHTM Data Compass Eprints
Open Research Exeter (ORE) DSpace
Research Data Leeds Eprints
Salford figshare figshare
Sheffield figshare figshare
UK Data Service – ReShare Eprints
University of Bath Research Data
Archive
Eprints
University of Birmingham
ePapers Repository
Eprints
University of Huddersfield
Repository
Eprints
University of Hull, Hydra Fedora
University of Reading Research
Data Archive
Eprints
University of Southampton -
ePrints Soton
Eprints
WRAP: Warwick Research
Archive Portal
Eprints
… 5 platforms (and counting)

9. We don’t fundamentally understand what citations mean, a
problem that as a community we tend to brush under the carpet.
This is a problem because it turns out they actually mean nothing
at all…
- Cameron Neylon
http://repository.jisc.ac.uk/6553/
2. Critically engaging with data citation
28/06/2017 Jisc RDN Recogising data sharing

10. The issues
» Citation metrics are widely used for purposes for which they have
not been designed
» Understanding of the limitations of citation metrics, especially
amongst managers, is poor.
» Dominant citation metrics are not transparent.
28/06/2017 Jisc RDN Recogising data sharing

11. And…
» these issues already apply to “traditional” citations.
» How can we be sure these do not apply to the emerging world of
data citations?

12. Open Metrics project aims
28/06/2017
To encourage and facilitate experiments
around alternative research metrics
To highlight and promote the benefits of
the responsible use of research metrics.
Jisc RDN Recogising data sharing

13. 3. Existing modes of recognition – Journal Policies
» ”The prototype that could be built would not be an authoritative source of
information for researchers or support staff as it would not contain the
information required at the level of data type […]To answer the question set
using the sources available, a high degree of subjectivity and interpretation
had to be applied as there were very few standard terms or definitions.
Interpretation of policy was often best undertaken at the domain level, which
further compounded the problems of building a scalable, generic database to
codify the information.”
(Naughton and Kernohan, 2016)
Article in “UKSG Insights”: http://doi.org/10.1629/uksg.28

14. The story so far…
Initial Aim
To develop templates and
guidance for journal
publishers around research
data policies
future
Looking at how Sherpa can
include information to
support data policies, and
other information to support
open science

15. Collaboration
»Linked to ongoing NIH-supported
BioCaddie project, hosted by the
Force11Collaboration
»Aimed at developing an implementation
pathway for the JDDCP principles…
The Data Citation Implementation Pilot (DCIP)
28/06/2017 Jisc RDN Recogising data sharing

16. The Joint Declaration of Data Citation Principles (JDDCP)
1. Importance Data should be considered legitimate, citable
products of research. Data citations should be accorded the same
importance in the scholarly record as citations of other research
objects, such as publications
2. Credit and Attribution Data citations should facilitate
giving scholarly credit and normative and legal attribution to all
contributors to the data, recognizing that a single style or mechanism
of attribution may not be applicable to all data.
3. Evidence In scholarly literature, whenever and wherever a
claim relies upon data, the corresponding data should be cited
4. Unique Identification A data citation should include a
persistent method for identification that is machine actionable,
globally unique, and widely used by a community
5. Access Data citations should facilitate access to the data
themselves and to such associated metadata, documentation, code,
and other materials, as are necessary for both humans and machines
to make informed use of the referenced data
6. Persistence
Unique identifiers, and metadata describing the data, and its
disposition, should persist -- even beyond the lifespan of the data
they describe
7. Specificity andVerifiability
Data citations should facilitate identification of, access to, and
verification of the specific data that support a claim. Citations or
citation metadata should include information about provenance and
fixity sufficient to facilitate verfiying that the specific timeslice,
version and/or granular portion of data retrieved subsequently is the
same as was originally cited
8. Interoperability and Flexibility
Data citation methods should be sufficiently flexible to accommodate
the variant practices among communities, but should not differ so
much that they compromise interoperability of data citation practices
across communities
https://www.force11.org/group/joint-declaration-data-citation-
principles-final
(Martone (ed), 2014)

17. The work of the DCIP
» Five expert groups
› EG1: FAQs/Documentation – developing documentation/guidance aimed at publishers
› EG2: Identifiers
› EG3: Publisher Early Adopters– mapping the publication process as it pertains to data.
› EG4: Repository Early Adopters – focus on machine readable landing pages and metadata
sharing
› EG5: JATS (JournalArticleTag Suite)
https://www.force11.org/group/dcip

18. DCIP EG3 Outputs
» “Authors can be encouraged to cite data or can be required to cite data. Authors should
provide details of previously published major datasets used and also major datasets
generated by the work of the paper.The policy should specify which datasets to cite (e.g.,
underlying data versus relevant data not used for analysis) and how to format data
citations. It is recommended if at all possible that data citation occurs either in the
standard reference list or (less preferable) in a separate list of cited data, formatted
similarly to standard literature references. But regardless of where citations appear in the
manuscript, they should be in readily parsable form.”
» A preprint of “Data Citation Roadmap for Scientific Publishers” is available on BioRxiv:
https://doi.org/10.1101/100784

19. Recognising data sharing
»Data usage metrics
»How should data be cited
»Exploring new scholarly metrics and indicators, including data
citation
»Funder and journal policies