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. 2007 Jul-Aug;14(4):478-88.
doi: 10.1197/jamia.M2114. Epub 2007 Apr 25.

Issues in biomedical research data management and analysis: needs and barriers

Nicholas R Anderson  1 , E Sally Lee, J Scott Brockenbrough, Mark E Minie, Sherrilynne Fuller, James Brinkley, Peter Tarczy-Hornoch
Affiliations

Issues in biomedical research data management and analysis: needs and barriers

Nicholas R Anderson et al. J Am Med Inform Assoc. 2007 Jul-Aug.

Abstract

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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Figures

Figure 1
Figure 1
Primary roles by percentage of respondents.
Figure 2
Figure 2
Primary research interest by percentage of respondents.
Figure 3
Figure 3
Individuals reporting experiencing computational and informatics problem by lab size and percentage of respondents.
Figure 4
Figure 4
Image archiving by sub-discipline by percentage of respondents.
Figure 5
Figure 5
Employee hours spent per-week by sub-discipline and percentage of respondents.
See this image and copyright information in PMC

References

    1. NIH NIH Roadmap on Translational Research. 2005. Available at: http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp. Accessed February 22, 2006.
    1. NIH NIH RFP for Institutional Clinical and Translational Science award. 2005. Available at: http://grants.nih.gov/grants/guide/rfa-files/RFA-RM-06-002.html. Accessed: March 14, 2006.
    1. NIH Biomedical Information Science and Technology Initiative (BISTI). 2001. Available at: http://www.bisti.nih.gov/. Accessed: March 14, 2007.
    1. NSF Science and Engineering Information Integration and Informatics. 2004. Available at: http://www.nsf.gov/pubs/2004/nsf04528/nsf04528.htm. Accessed: March 14, 2007.
    1. NIH Announces Draft Statement on Sharing Research Data. 2002. Available at: http://grants.nih.gov/grants/guide/notice-files/NOT-OD-02-035.html. Accessed: March 14, 2007.

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