long as they are scientifically useful. They also require long-term commitments from scientists and institutions to house them. Research museums may be particularly well suited as homes for databases because curatorial and archiving activities are routine functions in these institutions.

  1. Databases involve trade-offs between posterity-driven archival functions and the immediate needs of scientific users. There is a risk that shortcuts taken in data formatting, database architecture, and metadata assimilation may facilitate short-term and ongoing applications at the expense of future applications that may have different requirements.


The various models discussed in this chapter (e.g., IGERT, LTER, NCEAS, and community-wide databases) promote collaboration through training, research, and analysis and synthesis. Although most of these particular examples are supported by NSF, they—and other models that encourage collaborative research—are easily exportable to other research and education entities, from individual campuses to government agencies. Indeed, successful large-scale multidisciplinary efforts in genetic sciences (e.g., National Center for Biotechnology’s GenBank,18 the Human Genome Project19) and NASA’s Astrobiology Institute20 demonstrate that effective collaborations among disciplines can be stimulated and supported though the creation of appropriate programs, centers, and other community-based efforts. Future progress in understanding the geologic record of ecological dynamics will require not only new and better data but also better capacity for analyzing and synthesizing the data that we already possess. Although the collective cultures of the relevant disciplines are evolving in this direction, much more can be done to facilitate this evolution at levels ranging from individual institutions to funding agencies.

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