My talk abstract is below… comments and suggestions are welcome!
Measuring the adoption of Open Science
Why measure the adoption of Open Science?
As we seek to embrace and encourage participation in open science, understanding patterns of adoption will allow us to make informed decisions about tools, policies, and best practices. Measuring adoption over time will allow us to note progress and identify opportunities to learn and improve. It is also just plain interesting to see where we are, where we aren’t, and where we might go!
What can we measure?
Many attributes of open science can be studied, including open access publications, open source code, open protocols, open proposals, open peer-review, open notebook science, open preprints, open licenses, open data, and the publishing of negative results. This presentation will focus on measuring the prevalence with which investigators share their research datasets.
What measurements have been done? How? What have we learned?
Various methods have been used to assess adoption of open science: reviews of policies and mandates, case studies of experiences, surveys of investigators, and analyses of demonstrated data sharing behavior. We’ll briefly summarize key results.
The presentation will conclude by highlighting future research areas for enhancing and applying our understanding of open data adoption.