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Incentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout


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Presented on 25 October 2017 during a series of webinars on Incentives for sharing data, African Open Science Platform.

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Incentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout

  1. 1. INCENTIVIZING DATA SHARING: A “BOTTOM UP” PERSPECTIVE Dr Louise Bezuidenhout University of Oxford
  2. 2. OVERVIEW 1. Open Science and Open Data 2. Current discussions on (dis)incentivizing data sharing 3. Problems with data sharing in African research contexts 4. Finding the balance in data sharing incentivization
  4. 4. OPEN SCIENCE • Opening up access to data/knowledge/information has many recognizable benefits: • Enhance accountability of scientists for investment of public funds • Promote transparency of research and peer review • Improve reproducibility of research results and scrutiny • Speed up scientific discoveries and enable complex questions to be asked • Foster equity and capacity building on a global scale
  5. 5. MULTIPLE ACTORS IN OPEN DATA • International bodies • such as CODATA and RDA • Disciplines, consortia etc develop databases and mediate sharing channels • Public/private partnerships – ie. Journals mandating data sharing • Altmetric sharing such as FigShare • National • Governments promote public accountability and sharing • Local • Universities curate repositories Sub-local practices
  6. 6. NO “ONE SIZE FITS ALL” • Obligatory requirements – such as the deposition of data used in published articles to enhance replicability and verification • Advisory activities – data sharing as “good practice” to enhance transparency and re-use • Aspirational motivations – data sharing as a gesture of solidarity and public responsibility • Recognition that no “one size fits all” with data sharing practices • Reliance on “bottom up” development of data sharing practices • Requires individual and community buy-in in order to establish cultures of sharing
  7. 7. EMBEDDING A COMMITMENT TO DATA SHARING Appropriate policy Suitable infrastructure Effective training Responsible data practices Embedded data practices and cultures perpetuating responsible data values Individual and communal value attribution, development of norms and practices = “buy-in” from scientists Incentives Disincentives Incentives Disincentives
  8. 8. (DIS)INCENTIVES AND “CHOICES” FOR THE INDIVIDUAL SCIENTIST Generation and preliminary analysis Secondary analysis Curation and storage Dissemination (formal/informal), long-term storage or elimination Online identification and re-use • WHAT data to share • WHERE to share • HOW to annotate • WHEN to share • Responsibility for: • Producing accurate data • Ensuring data are re-usable • Surveilling data of others • Affording credit for use of others’ data Personal research cycle
  9. 9. 1. CURRENT RESEARCH ON (DIS)INCENTIVES Fergusson 2015 Why researchers share data
  10. 10. FOSTERING BUY-IN 1. Motivated by community norms and commitments to advancing research 2. Often influenced by funders, publishers and institutional codes of conduct 3. Public benefit 4. Benefits associated with increased visibility of work Aspirational and community focused. Oriented to end products of research
  11. 11. HINDERING BUY-IN 1. IP, confidentiality, ownership 2. Issues relating to individual credit (scooping, misuse, misapplication) 3. Confusing or conflicting requirements 4. Lack of time, expertise, resources 5. No one has asked me to Pragmatic and individual focused. Linked to processes of research
  12. 12. BALANCING PERCEIVED BENEFITS AND HARMS END PRODUCT-ORIENTED I think it leads to better science PROCESS-ORIENTED This data sharing is not for me – it’s for people with money and time Resource investment: Time, finance, expertise Protection of IP Improved accreditation and credit More clarity on regulations and aspirations
  13. 13. A DELICATE BALANCING ACT 1. Buy-in from scientists essential for effective data sharing 2. Good data sharing practices evolve in local communities when rewards/benefits outweigh harms 3. Relationship between incentives and disincentives complicated 4. Valuation of incentives and disincentives differs between research contexts 5. What incentivizes in one setting may not work in another 6. Therefore: 1. Must not assume that globally-endorsed incentives have equal traction in local settings 2. Relying on the good will of scientists to “just do it” is unfair 3. Need to examine physical and social aspects of research environment
  15. 15. LOOKING MORE CLOSELY AT RESEARCH ENVIRONMENTS • Pressures of daily research very different from HICs • Perceived disincentives may be very different • Research infrastructures that support data sharing absent • “Traditional” incentives may be viewed differently, or be entirely absent in low-resourced research settings • Research environments play a key role in how scientists discuss data sharing (dis)incentives and do/not buy in to data sharing activities Physical and social research environment Suitable infra- structures Appropriate data policy Effective training Effective data sharing
  16. 16. THE PHYSICAL ENVIRONMENT IN DATA SHARING • Many LMIC scientists report common problems with physical research environment • ICT provision/infrastructures and personal internet provision • Power outages, service delivery and border controls • Research funding, agency to spend • Equipment availability, technical support, outsourcing analyses • Slower data upload/downloads can disincentivize effort of sharing • Lack of core funding for office ICTs and software can hamper data sharing • Lack of technical support can complicate data sharing • Such issues are usually absent from HIC data sharing discussions. How can they be incentivized?
  17. 17. SA2/12: Like I said you can get around it with a lot of patience - waiting when the internet is not strong enough to allow you to download things. But they’re always promising us that things will improve, but they are promising one year after another but maybe it will improve.
  18. 18. KY1/1: Because the results, you know, they can be taken away. You’re dealing with colleagues and there are some – that which has taken you like 2 months, they can come and do it overnight with a whole research lab.
  19. 19. THE PERSONAL RESEARCH ENVIRONMENT AND DATA SHARING • Problems with personal research environments include: • The ability to use available resources relative to other commitments (e.g. teaching loads) • Location of work (home or office) • Skills necessary to make use of data • Many LIMC scientists lack the ability to work from home/off campus – curtails time available for data sharing activities • Lack of training / awareness of data sharing avenues / lack of mentorship and support are often cited issues • How can such issues be incorporated into incentivization discussions?
  20. 20. KY1/3: here I’m using wifi, so the moment you step out of the college you’re shut off and again in the estates [less-formal residential areas] where we stay as of now the internet is a bit expensive. It’s not affordable. So I do as much as I can here so that when I go back home I’m going to rest.
  21. 21. THE SOCIAL RESEARCH ENVIRONMENT AND DATA SHARING • Problems with the social research environment include: • Lack of – or contradictory - coherent institutional/funding/collaboration data sharing agreements • Procedures for procuring and reimbursing resources (e.g. paying for software) • Extremely publication-focused promotion criteria • High student turnover • Many LMIC scientists report difficulty of keeping track of data produced by high number of graduate students – particularly with high student turnover and no teaching assistance • How could such issues be addressed in discussions on disincentivization?
  22. 22. SA2/7: But also with the students you know we have a very high turnover of students and then those students leave and they haven’t stored their data properly or they leave with their hard drive.
  23. 23. SA2/7: probably people who are established who will do [publish in OA journals] for there because now myself if I’m here I must get the chemicals, I must get funding. KY2/1: if we want to publish we must pay out of our own pocket. It is important to publish, but it is also expensive.
  24. 24. 3. FINDING THE BALANCE IN DATA SHARING INCENTIVIZATION 1. Do globally recognized incentives translate in situ? 2. Are local incentives being properly exploited? 3. Are local disincentives adequately recognized? 4. Can data sharing be incentivized tangentially (ie. By building capacity in research environments)?
  25. 25. DO GLOBALLY RECOGNIZED INCENTIVES TRANSLATE IN SITU? KY1/8: I can’t see what [a professional networking site] has contributed to me. I don’t know why. They say it is another way of measuring how successful a researcher is. And they say that normally I am better than 90% of RG users, so I’m wondering how that is good for me. Because I don’t see any good news coming out of it – someone saying we want you here to do this or that, or give a talk. I’ve never seen anything. SA2/7: Again I’m going to say my view will be it will be established researchers who would do that [share data] now its people who let me say they don’t have to prove themselves to anyone so they know that okay it’s there but they can always have something on the side.
  26. 26. ARE LOCAL INCENTIVES BEING PROPERLY EXPLOITED? SA2/1: [y]ou know they call us to meetings and they say we have funding for this and that. And I think “great stuff”, but I wish they would ask me what the real issues are. I’ll probably tell you 100 other things outside of the money [permitted to be spent on the grant]”.
  27. 27. ARE LOCAL DISINCENTIVES ADEQUATELY RECOGNIZED? SA2/5: Yeah, it’s not something they teach you in undergrad. It’s often not something even your supervisor has worked with a lot because I guess it’s a kind of a very modern way of doing research because never before has there been this much data available. So, that’s the other thing. The student is almost, usually, the first one in the group to have the experience, so it’s hard.
  28. 28. CAN DATA SHARING BE INCENTIVIZED TANGENTIALLY? SA2/12: But where I find it difficult is people don’t understand our situation – it’s not bad will, it’s just not being able to figure it out SA1/7: if you haven’t finished your project and you contribute there’s other people with a lot more resources in terms of physical actual lab resources that can do what we do in a year in a couple of months. So if you were to share what you were doing without having finished they will finish the work for you and basically your work is rendered obsolete SA2/11: For example with the size of [the university] we don’t have the same legal power like a university in Australia or America. If someone steals their ideas they will go for them. But we are small and who is going to believe me when I say “this was my idea”. So there is that fear.
  29. 29. KEY QUESTIONS FOR INCENTIVIZATION DISCUSSIONS • Whose responsibility is it to address local incentives and disincentives? • How far can global incentives be relied upon to build local capacity? What else needs to accompany them in situ? • How can one balance the development of supportive global networks with the possibility of reliance and passiveness? • How can data sharing avoid ethical/policy hegemony? • How can balance a reliance on the “good will” of scientists with the importance of “one size doesn’t fit all”?
  30. 30. EXAMINING GLOBAL DATA SHARING MORE CLOSELY: SOME IDEAS • Need more empirical evidence detailing regional and local challenges • Need to encourage the development of local incentivization AS WELL as global • Rewards for innovation in sharing and data re-use • Funding to buy-out time to share data sets • Inclusion of altmetrics in promotion criteria • Need to incorporate discussions on research infrastructures into data sharing discussions • Ie. high teaching loads, large numbers of graduate students, low institutional support • Open up discussion about locally-appropriate sharing options (just because others are doing it doesn’t mean you need to)
  31. 31. EXAMINING GLOBAL DATA SHARING MORE CLOSELY: SOME IDEAS • Being a ”lone sharer” is not only lonely, but can also a dangerous career move • Identify and reward local “champions” and stimulate local networks of best practice • Curate examples of regional successes • Initiate discussions on credit attribution – even if fears are unsubstantiated they are sufficient to stop scientists from sharing • A strong local voice can lobby for policy changes on an international level • Need to overcome the idea that “it’s aWestern priority (and their problem)”
  32. 32. INSTITUTIONAL CHALLENGES … • Research settings differ according to culture, resources, history. Institutions are well-placed to address local variations in data sharing. It important to address: 1. Are the incentives of sharing adequately communicated in a manner that resonates with local scientists? 2. Are the personal incentives of sharing also effectively communicated and realizable? 3. Are the challenges of producing data in specific research environments adequately considered as disincentives to sharing? 4. Do policies offer any protection against these disincentives? 5. Are scientists being supported – financially, time-wise, skills, support etc?
  33. 33. THANKS • Much of the interviews were conducted while at the University of Exeter. Thanks to Prof Brian Rappert, Prof Sabina Leonelli and Dr Ann Kelly and the Leverhulme Trust • Thanks to the Institute for Science, Innovation and Society and the University of Oxford • Please feel free to contact me at • This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.