Identifying data sharing in biomedical literature
release_mlxar2a3c5hw7bddb5wl3536ly
by
Heather A Piwowar, Wendy W Chapman, Wendy Chapman
2008 p596-600
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.
In text/plain
format
Archived Files and Locations
application/pdf 181.8 kB
file_zl3e53ov45faveqdahotekwpc4
|
europepmc.org (repository) web.archive.org (webarchive) |
Open Access Publication
Not in DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:
1559-4076
access all versions, variants, and formats of this works (eg, pre-prints)