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Issues in Biomedical Research Data Management and Analysis: Needs and Barriers

^a Division of Biomedical and Health Informatics, Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, WA
^b Department of Biological Structure, University of Washington, Seattle, WA
^c Health Sciences Libraries and Information Center, University of Washington, Seattle, WA
^d Department of Health Services, School of Public Health and Community Medicine, University of Washington, Seattle, WA
^e Department of Pediatrics, University of Washington, Seattle, WA
^f Department of Computer Science and Engineering, University of Washington, Seattle, WA.

^_* Correspondences and reprints: Nicholas Anderson, University of Washington, Department of Medical Education and Biomedical Informatics, Box 357240, Seattle, WA 98195-7420 (Email: nicka{at}u.washington.edu).

Received for publication: 03/29/06; accepted for publication: 03/27/07.

Objectives: A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs.

Design: A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest.

Measurements: The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews.

Results: Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support.

Conclusion: Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities.

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