User:Heather A Piwowar/Notebook/PhD thesis/iEvoBio2010submission

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Data for reuse: factors associated with the public availability of gene expression microarray datasets

Heather A Piwowar and Wendy W Chapman

University of Pittsburgh, Department of Biomedical Informatics

Progress in evolution, systematics, and biodiversity research is facilitated by exchange of raw research data. Many initiatives encourage research investigators to share their raw research datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp on how often data is publicly shared, how representative shared datasets are of all datasets, or how we can effect change. In this study, we evaluated and used bibliometric methods to understand the impact, prevalence, and patterns with which investigators publicly share their raw gene expression microarray datasets after study publication.

To begin, we analyzed the citation history of 85 clinical trials published between 1999 and 2003. Almost half of the trials had shared their microarray data publicly on the internet. Publicly available data was significantly (p=0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and the country in the corresponding author's address.

Digging deeper into data sharing patterns required methods for automatically identifying data creation and data sharing. We derived a full-text query to identify studies that generated gene expression microarray data. Issuing the query in PubMed Central, Highwire Press, and Google Scholar found 56% of the data-creation studies in our gold standard, with 90% precision. Next, we established that searching ArrayExpress and the Gene Expression Omnibus databases for PubMed article identifiers retrieved 77% of associated publicly-accessible datasets.

Using these methods, we identified 11603 publications that created gene expression microarray data. Authors of at least 25% of these publications deposited their data in the predominant public databases. We collected a wide set of variables about these studies and derived 15 factors that describe their authorship, funding, institution, publication, and domain environments. In second-order analysis, authors with a history of sharing and reusing shared gene expression microarray data were most likely to share their data, and those studying human subjects and cancer were least likely to share.

We hope these methods and results will contribute to a deeper understanding of data sharing behavior and eventually more effective data sharing initiatives.

Data collection code and statistical scripts can be found at