Presented by Louise Bezuidenhout during the African Open Science Platform Side Event for Science Forum South Africa, on 7 Dec. 2016

1. DATA AND COMMUNICATION OF
RESEARCH: INCENTIVES AND
DISINCENTIVES
Dr Louise Bezuidenhout
University of Notre Dame / University of theWitwatersrand

2. EMBEDDING OPENNESS IN SCIENCE
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

3. REASONS TO SHARE … OR NOT
• Can be highly specific to individual or
distributed through community
• Reflect social, cultural and physical context of
research
• Also reflect regulatory landscape
• Fluid and continually evolving
• Reflect local and international scientific
community priorities
• How does one get “buy-in” for data initiatives
from individual scientists?
Suitable
infra-
structures
Appropriate
data policy
Effective
training

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

5. FOSTERING BUY-IN
Generation
and preliminary
analysis
Secondary
analysis
Curation and
storage
Dissemination
(formal/informal),
long-term storage
or elimination
Online
identification
and re-use
• Reasons to share:
• Motivated by community norms and commitments to advancing research
• Often influenced by funders and institutional codes of conduct
• Public benefit
• Aspirational and community focused. Oriented to end products of research
• Reasons not to share:
• Lack of resources (time, money, expertise)
• Issues relating to individual credit (scooping, misuse, misapplication)
• Confusing or conflicting requirements
• Pragmatic and individual focused. At all levels of research data production

6. KEY QUESTIONS FOR AFRICAN RESEARCH
• Reasons to share/not share vary between countries
• Highly context specific
• Research in African settings differs according to culture, resources, history.
Therefore, important to ask:
1. Are the incentives of sharing adequately communicated in a manner that resonates with
African scientists?
2. Are the personal incentives of sharing also effectively communicated and realizable?
3. Are the challenges of producing data in low-resourced research environments
adequately considered as disincentives to sharing?
4. Do policies offer any protection against these disincentives?

7. EXAMINING (DIS)INCENTIVIZATION
AMONGST AFRICAN SCIENTISTS
• Fieldwork – ethnographies of 4 laboratories in sub-Saharan Africa
• 56 semi-structured interviews with postgraduate and research scientists,
laboratory observations, policy review and governance interviews
• How do scientists discuss their data engagement activities and Open Data
involvement?
Data Generation
• Working in the lab
• Finding the data to
initiate research
Data
Processing
• Storage
• Curation
• Accessibility
for re-use
Data Sharing
• Publication
• Contribution
to databases
etc
• Altmetrics
• Profiling data
online
Data Re-Use
• Finding data online
• Access
• Downloading data
• Processing re-used data

8. IT’S A NICE IDEA … IN THEORY
SA1/3: I think it leads to better science
KY1/1: I won’t release data unless I first of all publish

9. INCENTIVES OR DISINCENTIVES?
1. Most respondents supported the ideal of Open Data in principle
2. Many respondents discussed Open Data as something “out there” and not in
their research context
3. Very few respondents had awareness of the personal benefits of sharing or
evidence for it in their research facility
4. Daily challenges of producing data in low-resourced research environments
were frequently cited as disincentives for sharing
5. Few respondents felt policies offered any protection against these
disincentives and current research infrastructures compounded these
problems

10. COMPLICATED COMMUNAL INCENTIVES
• Interested observers or embedded actors?
• While most respondents supported open data, the issue was discussed in
very abstract terms - evidence of incomplete/limited buy-in
• Lack of ownership of Open Data initiative
• KY1/4: It’s aWestern issue
• Lack of awareness of responsibility or potential contributions
• KY2/13: How much do we do to develop our own data?What processes do we
need to convince people that the data are good?

11. NO EVIDENCE OF PERSONAL INCENTIVES
• Absence of mentors to teach and set best practice
• 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.
• Misinformation causing confusion - particularly around Open Access
• SA2/6: And then you never know really how you know whether you dealing with
predatory or although I have a good idea, but the fees to me look predator.

12. NO EVIDENCE OF PERSONAL INCENTIVES
• Lack of evidence of benefits amongst peers - particularly relating to visibility
and profiling
• 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.

13. DAILY DISINCENTIVES
• Physical
• 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
• Personal:
• 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
• Social:
• Institutional policies such as data sharing guidelines
• Formal and informal intellectual property regimes
• Procedures for procuring and reimbursing resources (e.g. paying for software)
• High student turnover

14. DAILY DISINCENTIVES SHAPING DATA USE
• Restrictions to access
• 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.
• Under-recognized costs
• KY1/9: you know, these fees for joining, they add up quickly and you must choose
[what to join].
• Problematic support infrastructures
• 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.

15. DAILY DISINCENTIVES SHAPING DATA SHARING
• Lack of resources
• 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.
• Speed of research
• 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.
• Difficulty of controlling data
• 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.

16. POLICY DISINCENTIVES
• Policies don’t reflect challenges “on the ground”
• 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
• Lack of protection from policies for misuse
• 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.

17. UNDERSTANDING
INCENTIVES/DISINCENTIVES
• Crucial for transition from compliance to robust data cultures
• Not just about carrot or stick – highly complex and contextual
• Incentivization must include:
• adding long-term value and advance individual and community aspirations
• pragmatic short-term rewards
• are in line with accepted community values and have community support
• Addressing disincentives must consider:
• daily research challenges of individual scientists
• achievability of data requirements in situ
• full scope of data engagement activities

18. THANKS
• This work was conducted while at the University of Exeter. Thanks to Prof
Brian Rappert, Prof Sabina Leonelli and Dr Ann Kelly.
• Thanks to the Leverhulme Trust for sponsoring this research under the project
Beyond the Digital Divide: Sharing Data Between Developing and Developed
Countries.