Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Open Science Incentives/Veerle van den Eynden

247 views

Published on

Presented during Open Access Week 27 October 2017 during a series

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Open Science Incentives/Veerle van den Eynden

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Reasons to share data
  19. 19. Barriers to data sharing
  20. 20. Benefits from data sharing
  21. 21. Motivations for more data sharing
  22. 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. 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. 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. 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

×