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RDA 9 th Plenary Meeting, Barcelona, Spain Friday 7 April, 14.00 – 16.00

Explore attitudes, benefits, and drawbacks of data sharing in global research community. Understand factors influencing sharing practices and the gap between policy and reality. Investigate opportunities for improvement.

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RDA 9 th Plenary Meeting, Barcelona, Spain Friday 7 April, 14.00 – 16.00

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  1. LAUNCH EVENT Open research data: a gap between practice and policy? RDA 9th Plenary Meeting, Barcelona, Spain Friday 7 April, 14.00 – 16.00

  2. Open Data; the researcher perspective (2017) • Coverage: global • Disciplines: all • Selection: ~26,800 authors from Scopus • Number of respondents: 1,164 (2.3%) • Topics: how & why researchers are sharing, attitudes towards sharing, research data ownership, aspects of research data management, perceptions on reusability

  3. Complementary methods approach

  4. Attitudes to data sharing - summary Attitudes to data sharing Perceived benefits of data sharing Top 3 Reproducibility of research 53% 73% agree that having access to other researchers’ data would benefit their research … but 64% agree that they would be willing to share their own research data with other researchers More possibilities for collaboration 55% Article more likely to be cited 50% The reasons for discrepancy? Perceived drawbacks of data sharing Competitors using data before collector has a chance to re-use it Only 26% agreed they had received sufficient training in data management Only 37% agreed that sharing research data is associated with credit/ reward in their field Legal concerns (ownership, misuse, confidentiality) 59% agreed that research data management specialists need to play a role in research data sharing 69% agree in APAC Use without crediting/citing the data collector Cost (time and financial)

  5. Data reusability practices - snapshot Factors influencing sharing: Nearly half of researchers made use of external data on their last research project. Good documentation is the most important factor for trusting another researcher’s data Researchers have little awareness of reuse licenses and proper attribution, thereby making it less rewarding to make data reusable

  6. Looking back….

  7. Why Elsevier run the report • To evaluate trends of data sharing in the research community • To help researchers understand the potential of data sharing and enhance their practices • Part of a broader engagement to understand researchers’ behaviors (e.g RDM workshops) • To better understand researchers’ needs and develop best-fitted solutions • Understand policy aspects relevant to a service provider • To contribute to policy debate and developments • To establish a benchmark in order to follow the evolution of OD (that’s why we’re doing the 2nd survey to see progress)

  8. Why CWTS run the report • Part of an investigation around key Open Science aspects (Open Science is one of CWTS’ research themes) • Epistemic factor: what means open science to scientists in the broader sense? • First bibliometric investigation on open data • Explore mixed methods within CWTS • Part of ongoing collaboration with Elsevier

  9. Benefits of running a Global Report • Valuable resource/user case for funders i.e. NWO Research Council • Well aligns with the EC’s OS agenda and OS monitor. EC expressed interest in the survey questions (for H2020 participants) • Mixed method provided a more comprehensive overview, useful to help strengthen future survey questions • Major finding: gap between policy and data sharing practices. Shed light on an important aspect for future surveys • Catalysed attention of different interested parties on the topic, resulting in a call for a concerted action

  10. Culture is changing…not uniformly! Key learning: answers depend on the data practices in a particular field For future survey: not enough emphasis placed in the design phase on difference between Intensive data-sharing and Restricted Data sharing disciplines. Recommend to take into account in the conceptual/design phase

  11. Looking back at the methodology and design phase • Phrasing of questions depends on research fields (data intensive or not), and hence on their definition of what actually is. Take into account definitions. • We mixed up sharing and reuse, which is not the same. The fact that you share doesn’t mean it is being reused. • Good practice to limit questions to “last project”; makes results more realistic and less over-estimation • Non-responder analysis needs to be done in order to assess possible bias of survey respondents towards open research data > how could we sample better • Investigate option to request survey respondent for agreement for a follow-up on specific questions (e.g. additional questions, case study, interview,…) Key learning: with insight, some questions could have been phrased differently For future survey: design carefully to avoid “wasting” time of researchers, including cultural differences

  12. Food for thoughts –the gap between policy and practice Q: In which way have you published data part of your last research project? Key learning: outcome shows a gap between policy expectations and practices For future survey: there is room for investigation in this area

  13. Granularities… • Underrepresented geographies, but survey(s) could be targeted to address specific granularities such as • Disciplines • Institutions (e.g. universities) • Defined control groups • Research fields (data intensive or data restricted) • Take into account cultural aspects, and • language related consequences Key learning: survey approach could be applied to specific granularities For future survey: can we think of “control groups”; compare ‘granularities' with global view

  14. OD uptake – African countries

  15. Who owns the data Q1e: Who do you believe ‘owns’ the research data that you have made or will make available to others as part of your last research project? GLOBAL n=1162 AFRICA n=33

  16. Funding sources Q2d: What was the primary source of funding for your last research project? AFRICA n=33 GLOBAL n=1162

  17. Attitudes to research data Q2a: To better understand your attitudes towards researchdata access, please think about the research data that typically is not published (e.g. not summary charts, tables or images), and indicate how much you agree or disagree with the following statements. % agree

  18. RDM is growing very fast in SA

  19. RDM is growing very fast in Kenya Growth: 7% Growth: 67% Source: SciVal, data extracted on September 26, 2018 – CAGR = Compound Annual Growth Rate

  20. RDM best practices in Botswana Of 3.124 publications for the period 2013-2018 (with a DOI), about 2 % of papers with data, that is 60 datasets

  21. The impact of RDM best practices in SA 2013 to date in SciVal: Out of 118,304 2699 publications (2.3%) are linked to 5,030 datasets Source: SciVal, Scholix, Mendeley Data Monitor data extracted on Sept 11, 2018 Scholix database used: is OpenAireScholexplorer

  22. RDM best practices in Kenya Source: Scopus, Scholix, data extracted on September 26, 2018 2013-to date: Of 14.045 publications (with a DOI), 527 (~3.8%) are linked to 789 datasets

  23. Thank you f.rosetta@elsevier.com

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