Building a disciplinary, world-wide data infrastructure paper

[sjDCC]

Building a Disciplinary, World‐Wide Data Infrastructure, by Francoise Genova et al https://doi.org/10.5334/dsj-2017-016

This paper describes the way several disciplines organised themselves to set up their disciplinary interoperability framework, commonalities and differences. It comes from a panel discussion in a session of SciDataCon 2016. It will be useful for the 'Research data culture' and 'Making FAIR data real' sections of the report.

[asmatspatial]

Thanks for sharing nice paper. I would suggest Use cases such as Agriculture & Innovative Policymaking. It is time to quantify for which sector/discipline , how many datasets should be at least find/discoverable, accessible, and interoperable. I would not include reusable as if the above three conditions are met then certainly, data can be re-used. Am sharing a ppt (paper will follow soon) that quantifies (how many datasets?) are required for making agri-policies. Let us focus on how data can help improve policy decisions, too i.e. practicality of consuming data for the public good. RG Link to the mentioned stuff is:
Information Sharing: The Missing Ingredient in Science, Technology and Innovation Policy of Pakistan –Agriculture Perspective