Identifying Data Sharing in Biomedical Literature

TitleIdentifying Data Sharing in Biomedical Literature
Publication TypeJournal Article
Year of Publication2008
AuthorsPiwowar, H. A., & Chapman W. W.
JournalAMIA Annual Symposium Proceedings
Pagination596 - 600
Date Published2008///
ISBN Number1942-597X
Keywordssg_data_universe, sg_journals

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 find 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.

Short TitleAMIA Annu Symp Proc

Gap Area Study Type:

High-level Gap Areas:

Investigated extent of data sharing in biomedical research
Used national language processing (NLP) techniques to find evidence of dataset sharing within 1,028 open access research that mentioned one or more of five databases