Center for Open Science: “The Landscape of Open Data Policies”
Transparency is essential for scientific progress. Access to underlying data and materials allows us to make progress through new discoveries and to better evaluate reported findings, which increases trust in science. However, there are challenges to changing norms of scientific practice. Culture change is a slow process because of inertia and the fear of unintended consequences.
One barrier to change that we encounter as we advocate to journals for more data sharing is an editor’s uncertainty about how their publisher will react to such a change. Will they help implement that policy? Will they discourage it because of uncertainty about how it might affect submission numbers or citation rates? With uncertainty, inaction seems to be easier.
One way for a publisher to overcome that barrier for individual journals is to establish data sharing policies that are available to all of their journals. That directly signals that the publisher will be ready to support editorial policy change. In fact, 2017-2018 saw most major publishers doing just that. This has resulted in a significant number of journals now having policies that can increase transparency. The Transparency and Openness Promotion (TOP) Guidelines provide guidance and template language to use in author instructions. Publishers that adopt TOP policies or their equivalent signal support for any of these actions.
About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.