Heather A. Piwowar and Wendy Chapman, Identifying data sharing in biomedical literature, AIMA Annual Symposium Proceedings, November 2008. Since the 2008 proceedings are not yet online (OA or TA) at the AIMA web site, I'm linking to the abstract at PubMed.
Abstract: Many policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation.
PS: See our blog post linking to two OA versions of the preprint.
Posted by
Peter Suber at 11/17/2008 01:28:00 PM.
The open access movement:
Putting peer-reviewed scientific and scholarly literature
on the internet. Making it available free of charge and
free of most copyright and licensing restrictions.
Removing the barriers to serious research.
I recommend the OA tracking project (OATP) as the best way to stay on top of new OA developments. You can read the OATP feed on a blog-like web page or subscribe to it by RSS, email, or Twitter. You can also help build the feed by tagging new developments you encounter.
