The document discusses the intricacies of incentivizing data sharing in the context of open science, particularly focusing on challenges faced in African research environments. It highlights that effective data sharing relies on a balance of incentives and disincentives, local practices, and community buy-in, while noting that globally recognized incentives may not be applicable in all local settings. Overall, the document underscores the need for tailored approaches that understand specific research contexts and encourage responsible data sharing practices.

1. INCENTIVIZING DATA SHARING:
A “BOTTOM UP” PERSPECTIVE
Dr Louise Bezuidenhout
University of Oxford

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

3. THE AGE OF OPEN SCIENCE

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. 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. 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. 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. (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. 1. CURRENT RESEARCH ON
(DIS)INCENTIVES
Fergusson 2015 Why researchers share data https://dataone.org

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. 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. 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. 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

14. 2. PROBLEMATIZING INCENTIVES: AN
AFRICAN PERSPECTIVE
?

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 louise.bezuidenhout@insis.ox.ac.uk
• This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International License.