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Wide-Open: Accelerating public data release by automating detection of overdue datasets

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  • Maxim Grechkin
  • Hoifung Poon
  • Bill Howe

Abstract

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.

Suggested Citation

  • Maxim Grechkin & Hoifung Poon & Bill Howe, 2017. "Wide-Open: Accelerating public data release by automating detection of overdue datasets," PLOS Biology, Public Library of Science, vol. 15(6), pages 1-5, June.
    • Handle: RePEc:plo:pbio00:2002477
    DOI: 10.1371/journal.pbio.2002477
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    References listed on IDEAS

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    1. Theodora Bloom & Emma Ganley & Margaret Winker, 2014. "Data Access for the Open Access Literature: PLOS's Data Policy," PLOS Biology, Public Library of Science, vol. 12(2), pages 1-3, February.
    2. Theodora Bloom & Emma Ganley & Margaret Winker, 2014. "Data Access for the Open Access Literature: PLOS's Data Policy," PLOS Medicine, Public Library of Science, vol. 11(2), pages 1-3, February.
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