1. Creating a Culture of Open Data in Academia
Charlotte Day
Linked Open Data Professional Development Workshop
CABI Wallingford
22nd May 2018

3. What is open data?
Open data does mean different things according to your perspective. I will include a few well-accepted definitions here.

4. FAIR Data Principles Data should be Findable Data should be Accessible
Data should be Interoperable
Data should be Re-usable
Much data should be open (openly licenced).
All data can benefit from being FAIR - clear licence, metadata, persistent, provenance, machine readable

5. 5 Star Open Data

7. Open Data and Open Access at PUSH Universities
Released June 4, 2018

8. Benefits Provides a public good
Enhances and accelerates research and innovation
Increases transparency
Increases citations and recognition of faculty and their universities
Increases potential of identifying research collaborators

9. Barriers Privacy of research subjects, data security
Concerns on misuse of data, IP
Funders’ contract requirements
Incentives to publish data
Lack of resources and capacity to implement donor policy requirements
Lack of understanding regarding licensing options and ownership of data

10. Recommendations
Create alignment between funders’ expectations and the research institution’s capabilities
Improve faculty’s ability to comply with open access and open data requirements
Refine and/or develop open access and open data standards and protocols

11. Data Wish List Accessible to those who need it Machine-readable
High-quality
Continuously updated
Possess unique identifiers
Able to be linked to other data sources
Have an open license to reuse the data in any way as long as the original source is credited.
These reflect the FAIR Principles!

12. Donors support data sharing.
As funders we intend to promote greater availability and use of data in ways that are:
Equitable​ ​- data generated through our funding should where possible be provided to everyone, whilst respecting privacy concerns, in accordance with applicable laws and regulations and crediting the original data source
Ethical​ - collectors, users and reusers of data must consider its impact on people and communities, while respecting the ‘FAIR principles’
Engaging​ ​- ensuring that a broad range of people are brought into discussions about what data should be collected, how it should be used, and prioritised for release

13. FAIR Data Communities Project
BMGF Ag Dev grantees, regional implementing partners, and local institutions must be able to:
collect high quality data
share data
reuse other’s data
to make better data-driven decisions and create a stronger data sharing community.