A Knowledge Network for Biocomplexity: Building and evaluating a metadata-based framework for integrating heterogeneous scientific data (Hosted by NCEAS)

Complexity is an inherent property of living systems that arises from direct and indirect interactions among the earth's physical, chemical, and biological components. Biocomplexity includes the structural and functional attributes of dynamic systems that arise at all levels of biological organization, including individuals, populations, and communities. Importantly, ecological components of biocomplexity (e.g., biodiversity, ecosystem services) are in crisis, and are undergoing potentially irreversible changes in the face of rapid human population growth and economic development. Wise stewardship, based on all available scientific knowledge concerning these natural systems, is essential. Data Catalyzed by these societal concerns, and facilitated by technology advances, scientists focused on complex ecological systems have generated an explosion of ecological and environmental data. When integrated with data from other disciplines (e.g., meteorology), these data have the potential to greatly enhance understanding of biocomplexity. However, broad-scale and synthetic research is stymied because these data are largely inaccessible due to their spatial dispersion, extreme structural and semantic heterogeneity, and complexity.

Knowledge Networking
We propose to integrate the distributed and heterogeneous information sources required for the development and testing of theory in ecology and its sister fields into a standards-based, open architecture, knowledge network. The network will extend recent advances in metadata representation to provide conceptually sophisticated access to integrated data products drawn from distributed, autonomous data repositories. In addition, the knowledge network will include advanced tools for exploring complex data sets from which multiple formulations of hypotheses can be tested.

The existence of such a network will lead to broadened understanding of biocomplexity and ecological systems, and allow the application of that understanding to societal issues. In developing this network, we will create a new community of environmental scientists who will be able to focus on complex, multi-scale problems that, to date, have proven to be intractable. We will perform foundational research in computer science and informatics to create new tools for discovering, retrieving, interpreting, integrating, and analyzing data from these diverse sources. Our prototype network will be useful across a variety of disciplines and will provide a basis for the growth of multidisciplinary research groups focused on biocomplexity.

Collaboration
To accomplish these goals, we have created an intellectual consortium that comprises the National Center for Ecological Analysis and Synthesis (NCEAS), the Long-Term Ecological Research Network (LTER) and the San Diego Supercomputer Center (SDSC). Our partnership has a successful history and includes (1) advanced expertise in ecology, informatics, and computer science, (2) a comprehensive understanding of the critical obstacles that data heterogeneity and dispersion create for advancing synthesis and understanding, and (3) strong commitments to addressing those obstacles that deter broad-scale and synthetic analyses. Impact The results of the proposed research will have broad implications for our ability to understand and manage sustainably the complex ecological systems and biological resources on which all humans depend. Information on biocomplexity is voluminous and complex, but currently is inaccessible to research scientists and policy makers. The intellectual advances in information science that we propose will, for the first time, provide an accessible infrastructure for identifying, integrating, managing, and, ultimately, synthesizing the nation's ecological and biodiversity information resources.

This material is based upon work supported by the National Science Foundation under Grant No. DEB99-80154. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).