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Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration. | LitMetric

AI Article Synopsis

  • The ability to understand and predict Earth's natural systems is currently lagging due to issues in integrating observations with ecosystem models, leading to missed opportunities in utilizing available data.
  • The text proposes a roadmap for developing community cyberinfrastructure to bridge the gap between empirical research and modeling, emphasizing the need for tools that are accessible, scalable, and transparent.
  • The roadmap highlights five key opportunities to enhance community tools, including data ingestion, model calibration, benchmarking, data assimilation, and ecological forecasting, aiming to meet the scientific and societal needs of the 21st century.

Article Abstract

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756391PMC
http://dx.doi.org/10.1111/gcb.15409DOI Listing

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