open science incentives/veerle van den eynden

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

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

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

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

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

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

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

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’

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

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

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

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

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

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

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

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

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%)

Reasons to share data

Barriers to data sharing

Benefits from data sharing

Motivations for more data sharing

Reuse of data

0% 10% 20% 30% 40% 50% 60%

Background orcontext to my

research

Baseline data

Researchvalidation

New analysis

Meta-analysis

Develop mymethodology

Teachingmaterial

Replication

I have not usedexisting data

0% 10% 20% 30% 40% 50% 60%

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

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

Individual / institutional factors that motivate

researchers to share dataStudy Individual factors Institutional factors

Van den Eynden and Bishop (2014) (N=22, interviews 5 case studies)

Direct research benefitsCareer benefits

Norms of research circle and/or disciplineFunder and journal policiesData infrastructure and support servicesFunding for data sharing

Van den Eynden et al. (2016): all disciplines (N=842, survey)

Research benefitsKnowledge of reuseCareer benefits: enhanced academic reputation

Norms: good research practiceFunding for data management and sharingAssistance for data management and sharing

Van den Eynden et al. (2016): humanities and social science

Case studies showcasing data Funding for data management and sharingAssistance for data management and sharing Funder requirements

Van den Eynden et al. (2016): early career researchers

Impact: public health benefits, respond to health emergenciesEthical obligation to research participantsReward: citations and credit

Youngseek and Stanton (2012): STEM researchers (N=1153, survey)

Career benefitsScholarly altruism

NormsJournal requirements

Youngseek and Adler (2015): social scientists (N=361, survey)

Career benefitsAttitude to data sharing

Norms

Sayogo and Pardo (2013) (N=555)

Data management skillsRewards: citation, acknowledgement

Institutional data management supportLegal/policy framework to guarantee good reuse and acknowledgement