Author’s Note: Looking back at this 2017 post brings a mixed bag of thoughts. First, the fortunes being made with collecting, curating, and selling access to consumer data still haven’t spilled across into research data, and that’s likely because a) relatively few research datasets are available, and b) for the most part, the ones that are available have inadequate metadata and incompatible structures, so that combining datasets for meta-analyses is scarcely worthwhile. Until we address the problem of missing research data – which (full disclosure) we’re trying to do with DataSeer – we can’t really make much headway with getting it all into a consistent format.
A great Scholarly Kitchen piece about the value of open data and the merits of institutions investing resources in supporting it. The link to academic success suggests it is worth the effort.
No matter what route we choose, it’s clear that our current incentive structures around open science (mostly strongly worded policies and the lure of extra citations) are not getting the job done, and we need to consider alternatives. Money can enter the equation at a few places: by only funding open science, as exemplified by Aligning Science Across Parkinson’s, or by offsetting the extra effort required by researchers with additional financial resources, by making things cheaper or non-open science more expensive. Let’s see where we go.
Is There a Business Case for Open Data?
On the first day of Open Access Week, Figshare released the 2017 edition of their ‘State of Open Data’ report. The document contains a number of thoughtful pieces from the European Commission, the Wellcome Trust, and Springer Nature (among others), as well as the results of Figshare’s second annual survey of researchers about their experiences with data sharing.
