The following article was published today by PLOS Biology.
Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.
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Two popular repositories that offer researchers the option to keep genetics data hidden, for example, are the Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA), both run by the US National Center for Biotechnology Information. Both sites require data sets to be made open when papers are published. But in practice, scientists often forget to do this, says Maxim Grechkin, a computer scientist at the University of Washington in Seattle.
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