Abstract
This chapter discusses the management of biological data. Biological research and drug development are routinely producing terabytes of data that need to be organized, queried, and reduced to useful scientific knowledge. Although data management technology can provide solutions to problems, in practice the data needs of biomedical research are not well served. The goal of this panel is to expose the barriers blocking the effective application of advanced data management technology to biological data. Management of biological data involves acquisition, modeling, storage, integration, analysis, and interpretation of diverse data types including analog signals, digital images, sequences, spreadsheets, taxonomies, structured records, and unstructured text data. Existing data management technology is often challenged by the lack of stability, evolving nature, diversity, and implicit scientific context that characterize biological data.
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