Author
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Peters, Debra |
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Submitted to: American Geophysical Union
Publication Type: Abstract Only Publication Acceptance Date: 11/30/2014 Publication Date: 12/18/2014 Citation: Peters, D.C. 2014. Insights and challenges to Intergrating data from diverse ecological networks [abstract]. 2014 American Geophysical Union Fall Meeting. December 18, 2014, San Francisco, CA. U43A-04. Interpretive Summary: Technical Abstract: Many of the most dramatic and surprising effects of global change occur across large spatial extents, from regions to continents, that impact multiple ecosystem types across a range of interacting spatial and temporal scales. The ability of ecologists and interdisciplinary scientists to understand and predict these dynamics depend, in large part, on existing site-based research infrastructures that developed in response to historic events. Integrating these diverse sources of data is critical to addressing these broad-scale questions. A conceptual approach is presented to synthesize and integrate diverse sources and types of data from different networks of research sites. This approach focuses on developing derived data products through spatial and temporal aggregation that allow datasets collected with different methods to be compared. The approach is illustrated through the integration, analysis, and comparison of hundreds of long term datasets from 50 ecological sites in the US that represent ecosystem types commonly found globally. New insights were found by comparing multiple sites using common derived data. In addition to “bringing to light” many dark data in a standardized, open access, easy to use format, a suite of lessons were learned that can be applied to up and coming research networks in the US and internationally. These lessons will be described along with the challenges, including cyber infrastructure, cultural, and behavioral constraints associated with the use of big and little data, that may keep ecologists and interdisciplinary scientists from taking full advantage of the vast amounts of existing and yet-to-be exposed data. |
