Presented during Open Access Week 27 October 2017 during a series

1. Open Science Incentives
Veerle Van den Eynden
UK Data Service
UK Data Archive, University of Essex
Open Access week
African Open Science Platform
27 October 2017

2. UK Data Service
• Curate, preserve, provide access to social science data
for reuse
• Funded by ESRC UK
• Data management advice for data creators
• Support for users of the service
• Information about the use to which data are put
ukdataservice.ac.uk

3. Research data services team
• Supporting researchers to make research data
shareable
• UK Data Service helps materialise Data Policy for the
Economic and Social Research Council (ESRC)
• Data management planning advice & guidance
• Data management guidance & training, esp. on
confidentiality, security, ethics
• Research data available for re-use to maximum
extent possible, via:
• ReShare repository
• http://discover.dataservice.ac.uk

4. Data sharing
New Data for Understanding the
Human Condition: International
Perspectives. OECD Global Science
Forum report, 2013.
Public Health Research Data Forum,
Joint statement: Sharing research
data to improve public health
G8 science ministers statement,
2014: open scientific research data
that are easily discoverable,
accessible, assessable, intelligible,
useable, and wherever possible
interoperable to specific quality
standards

5. Research on incentives for data sharing
What motivates researchers to share
their data?
• Qualitative study through case
studies in 5 European countries:
Sowing the Seed
• Quantitative study with 842
researchers funded by Wellcome
Trust and ESRC: Towards Open
Research
• Existing studies

6. Qualitative study of incentives, 2014
• 5 case studies – active data sharing
research groups
• 5 European countries: FI, DK, GE, UK, NL
• 5 disciplines: ethnography, media studies,
biology, biosemantics, chemistry
• 22 researchers interviewed
• Q: research, data, sharing practices,
motivations, optimal times, barriers, future
incentives,….
Van den Eynden, V. and Bishop, L. (2014).
Sowing the seed: Incentives and Motivations
for Sharing Research Data, a researcher's
perspective. Knowledge Exchange.
http://repository.jisc.ac.uk/5662/1/KE_report-
incentives-for-sharing-researchdata.pdf

7. Case studies
Denmark: LARM Audio Research Archive
Germany: Evolutionary Plant Solutions to Ecological
Challenges
Netherlands: Netherlands Bioinformatics Centre
Finland: MSc project Retired Men Gathering in Cities
UK: Chemistry Department, University of Southampton

8. Different modes of data sharing
• Private management sharing
• Collaborative sharing
• Peer exchange
• Sharing for transparent governance
• Community sharing
• Public sharing (repository)
• Mutual benefits vs data ‘donation’

9. Data sharing practices in case studies
• Data sharing = part of scientific process
• Collaborative research
• Peer exchange
• Supplementary data to publications
• Sharing early in research (raw)
• Sharing at time of publication (processed)
• Well established data sharing practices in some
disciplines: crystallography, genetics
• Development of community / topical databases:
BrassiBase, LARM archive
• Some sharing via public repositories: chemistry,
ethnography, biology

10. Incentives – direct benefits
• For research itself:
• collaborative analysis of complex data
• methods learning
• research depends on data /information, data mining
• suppl. data as evidence for publications
• research = creating data resources
• For research career:
• visibility, also of research group
• reciprocity
• reassurance, e.g. invited to share
• For discipline & for better science

11. Incentives – norms
• Sharing = default in research domain, research group,
institution
• Hierarchical sharing throughout research career
• Challenge conservative non-sharing culture
• Openness benefits research, but individual researchers
reluctant to take lead

12. Incentives – external drivers
• Funders directly fund data sharing projects
• Journals expects suppl. Data
• Learned societies develop infrastructure & resources
• Data support services
• Publisher and funder policies and expectations
• may not push data sharing as much as could do, e.g.
supplementary data in journal poor quality; mandated repository
deposits minimal, exclude valuable data
• slowly change general attitudes, practices, norms

13. Future incentives for researchers
• Policies and agreements – create level playing field
• Training – sharing to become standard research practice
• Direct funding for RDM support
• Infrastructure and standards
• Micro-publishing/micro-citation
• Broaden norms

14. Quantitative study on Open Research, 2016
• Study practices, experiences, barriers and
motivations for
• open access publishing
• sharing and reuse of data
• sharing and reuse of code
• Researchers funded by Wellcome Trust and
ESRC: biomedical, clinical, population
health, humanities, social sciences
• Survey (N=842)
Van den Eynden, Veerle et al. (2016) Towards
Open Research: Practices, experiences,
barriers and Opportunities. Wellcome Trust.
https://dx.doi.org/10.6084/m9.figshare.4055448

15. Your research data
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Quantitative data
Qualitative data
Biological / ecological data
Social science data
Imaging data
Omics data
Disclosive data that are difficult to anonymise
I do not produce data in my research
Other

16. Data / code sharing • 95% of respondents
generate research data
• 52 % shared research
data last 5 years
• 3.4 (6.5) datasets on
average
• sharing increases with
career length
• 40% of respondents
generate code
• 43% shared code last
5 years
• 2 (4) code packages
on average
• sharing increases with
career length

17. Data sharing methods
414 respondents share data:
• Full dataset (51%)
• Data subset linked to paper analysis (38%)
• Other subset of data (37%)
Via:
• Community repositories (42%)
• Institutional repositories (37%)
• Project/private repositories (15%)
• General purpose repositories (13%)
• Journal supplementary material (10%)
• Open access (76%)
• Upon request (23%)

18. Reasons to share data

19. Barriers to data sharing

20. Benefits from data sharing

21. Motivations for more data sharing

22. Reuse of data
0% 10% 20% 30% 40% 50% 60%
Background or
context to my
research
Baseline data
Research
validation
New analysis
Meta-analysis
Develop my
methodology
Teaching
material
Replication
I have not used
existing data
0% 10% 20% 30% 40% 50% 60%

23. What other research found
Youngseek, K and Adler, M (2015) Social scientists’ data sharing behaviors:
Investigating the roles of individual motivations, institutional pressures, and
data repositories. International Journal of Information Management 35(4): 408–
418.
• online survey of 361 social scientists in USA academia
• predict data sharing behaviour through theory of planned behaviour
(individual motivation is based on own motivations and availability of
resources) and institutional theory (institutional environment produces
structured field of social expectations and norms, using (dis)incentives to
shape behaviour and practices)
• main drivers for data sharing:
• personal motivations: perceived career benefit and risk, perceived
effort, attitude towards data sharing
• perceived normative pressure
• funders, journals and repositories are not significant motivators

24. What other research found
Sayogo, D.S. and Pardo, T.A. (2013) Exploring the determinants of scientific
data sharing: Understanding the motivation to publish research data.
Government Information Quarterly, 30(1): 19-31.
• Online survey with 555 researchers, cross-disciplinary, 75% USA
• Ordered logistic regression to assess the determinants of data sharing,
analysing willingness to publish datasets as open data against 7 variables:
organisational support, DM skills, data reuse acknowledgement, legal and
policy conditions owner sets for data reuse, concern for data
misinterpretation, economic motive, funder requirement
• Main determinants are:
• DM skills and institutional support
• data reuse acknowledgement, legal and policy conditions owner sets for
data reuse

25. Individual / institutional factors that motivate
researchers to share data
Study Individual factors Institutional factors
Van den Eynden and
Bishop (2014) (N=22,
interviews 5 case studies)
Direct research benefits
Career benefits
Norms of research circle and/or discipline
Funder and journal policies
Data infrastructure and support services
Funding for data sharing
Van den Eynden et al.
(2016): all disciplines
(N=842, survey)
Research benefits
Knowledge of reuse
Career benefits: enhanced academic
reputation
Norms: good research practice
Funding for data management and sharing
Assistance for data management and
sharing
Van den Eynden et al.
(2016): humanities and
social science
Case studies showcasing data Funding for data management and sharing
Assistance for data management and
sharing
Funder requirements
Van den Eynden et al.
(2016): early career
researchers
Impact: public health benefits, respond to
health emergencies
Ethical obligation to research participants
Reward: citations and credit
Youngseek and Stanton
(2012): STEM researchers
(N=1153, survey)
Career benefits
Scholarly altruism
Norms
Journal requirements
Youngseek and Adler
(2015): social scientists
(N=361, survey)
Career benefits
Attitude to data sharing
Norms
Sayogo and Pardo (2013)
(N=555)
Data management skills
Rewards: citation, acknowledgement
Institutional data management support
Legal/policy framework to guarantee good
reuse and acknowledgement