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

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.

Low expression of KCNN3 may affect drug resistance in ovarian cancer

“…Novel technologies have led to an increase in the volume and diversity of large-scale public data ( 43 ), which are a vital pillar of open science and a key enabler of reproducibility and novel discoveries ( 44 ). Reuse of public data may potentially answer questions beyond those originally envisioned ( 45 ), and provide a systems-level approach to predicting treatment response and disease progression, and to developing precision therapies ( 43 , 46 ).…”

What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption

“…Drivers Inhibitors Arza and Fressoli [4] Systematizing benefits of open science practices X X X Arzberger, Schroeder [50] Promoting access to public research data for scientific, economic, and social development X X X X Bezuidenhout [51] Technology Transfer and True Transformation: Implications for Open Data X X Campbell [2] Access to scientific data in the 21st century: Rationale and illustrative usage rights review X X X X da Costa and Leite [47] Factors influencing research data communication on Zika virus: a grounded theory X X X X Cragin, Palmer [52] Data sharing, small science and institutional repositories X X Curty, Crowston [40] Attitudes and norms affecting scientists' data reuse X X X Enke, Thessen [10] The user's view on biodiversity data sharing-Investigating facts of acceptance and requirements to realize a sustainable use of research data X X X X Fecher, Friesike [11] What drives academic data sharing? X X X Ganzevoort, van den Born [53] Sharing biodiversity data: citizen scientists' concerns and motivations X X Grechkin, Poon [6] Wide-Open: Accelerating public data release by automating detection of overdue datasets X X X…”

“…Social responsiveness [4] and standard social norms [41] The culture of open sharing (promotion for academe is tied to publication and not data) [49] Perceived social pressure to share data with others [45] Code of conduct and related normative standards of professional scientists and their communities [50] Subjective norm [41] Perceived normative pressure [42] Peer pressure to share data [8] Attitudes toward data sharing [17,42] World-wide attention to the need to share and preserve data [56] Effort The expectation that data will be reused [40] (Perceived) effort [11,41,42,47,49] Avoidance of duplication of work [2,41,48,57] Required manual efforts [6] Increase efficient use of funding and population resources by avoiding duplicate data collection [8,9] Individual investment needed to preserve and manage data [57] Efficient and optimized use of resources [1,48,56] Time investment (the amount of time they would have to invest to get the data ready to share) [8,10,11,47,49,52] A source for researchers to consult when considering how to build upon existing studies [42] Large amount of work [52] Saving time involved in data collection [41,48] Making data from the long tail discoverable and reusable is emerging as a major challenge …”

“…Adapting the query-result parser to distinguish between invalid UIDs, datasets that have been released, and datasets that remain private [6] To share portions of a dataset rather than to share the whole dataset [59] The researcher has not collected the raw data directly him or herself [48] Quantitative analytic work [42] Researchers' experience and skills…”

Using standard keywords in publications to facilitate updates of new fungal taxonomic names

“…The challenge discussed here exists within the larger context of improving the timely public release of all published data. Recently the development of Wide-Open, a programmatic approach using text mining to detect published but unreleased data was described (Grechkin et al 2017). The first run of this approach focused on records in the Gene Expression Omnibus (GEO) repository and the Sequence Read Archive (SRA) at NCBI.…”