Presented during Uganda Open Data/Open Science National Dialogue 25-26 April 2018.

1. OPEN DATA PUBLISHING AND
INCENTIVES
Presented by Susan Veldsman
Director: Scholarly Publishing Programme
Academy of Science of South Africa (ASSAf)
Kampala Workshop, 25 April 2018

2. Open data 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

3. Data sharing practices…….
• 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
THEREFORE
• 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

4. Embedding a commitment to sharing
data
• 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

5. (Dis)incentives and “choices” for the individual
scientists
Personal research cycle:
Generation of data and preliminary analysis
►Secondary analysis► Curation and storage►
Dissemination (formal/informal), ► long-term
storage or elimination ►Online identification and
re-use
Decision Responsibility for:
WHAT data to share Producing accurate data
WHERE to share Ensuring data are re-usable
HOW to annotate Surveilling data of others
WHEN to share Affording credit for use of others’
data

6. Hindering buy-in
• IP, confidentiality, ownership
• Issues relating to individual credit (scooping,
misuse)
• Confusing and conflicting requirements
• Lack of time and expertise and resources
• No one has asked me?
Ferguson,2015.Why researchers share data.
https:dataone.org

7. Fostering buy-in
• Motivated by community norms and commitment
to advance research
• Influenced by funders, publishers and institutional
code of contact
• Public benefit
• Benefits associated with increased visibility of work
To ends of the scale
• Aspirational and community focused
• Oriented to end products of research
Ferguson,2015.Why researchers share data.
https:dataone.org

8. Recommendations for funders
• All research funders data sharing policy -
expectations for data accessibility; budget
share for RDM
• Funding support services, cf. funding
publication costs
• Invest in data infrastructure with rich context
• Fund data sharing training for students and
doctoral researchers
• Target funding at reuse of existing data
resources

9. Recommendations learned societies
• Research recognition for data sharing and data
publishing
• Data sharing expectations for the disciplines,
e.g. code of conduct.
• Data sharing resources and standards for the
research discipline.

10. Recommendations to research
institutions
• Data impact in PhD career assessment, e.g.
impact portfolio, data CV
• Integrated RDM support services (one-stop-
shop)
• Recognise and value data in research
assessment and career advancement.
• Data sharing training part of standard student
research training

11. Recommendations to publishers
• Boost direct career benefits of data sharing:
• data citation
• data sharing metrics
• micro-citation
• tools: DOIs, ORCID, digital watermarking
• Publication of negative findings, failed experiments
• Full datasets as supplementary material
• All supplementary data openly available
• (Open) standards for file formats and supplemental
documentation

24. Thank you!
Susan Veldsman
susan@assaf.org.za