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Five foundational tools for managing metadata from the USDA Long-Term Agroecosystem Research (LTAR) Network
Nicole E Kaplan1, Gerardo Armendariz2, Shefali Azad3
1USDA-ARS Rangeland Resources and Systems Research Unit, Fort Collins, Colorado, USA.
Abstract:
The United States Department of Agriculture Long-Term Agroecosystem Research (LTAR) Network comprises 19 sites and has collectively produced nearly one petabyte of data. Data include time-series measurements, remotely sensed imagery, and high-throughput environmental data from field and laboratory instrumentation. Currently, network-level analyses leverage multi-decadal data from historical, as well as ongoing, and coordinated data collection from several network sites. Though this multifaceted data facilitates analyses on cross-site, regional, and national levels, its analytical power is constrained by the locally organized and siloed management and storage practices in place. A network information management system is crucial for robust meta-analyses and syntheses exploring the agricultural management impacts on agroecosystems production, structure, and function across the various LTAR sites. Foundational tools described herein provide the framework for an LTAR network information system that will empower users to find, harmonize, map, and share data across all network locations. Standard metadata have been created and implemented for (1) inventorying datasets managed by each site, (2) creating controlled vocabularies for measurements to facilitate cross-site comparisons and analyses, (3) geolocating data collection, site, and experimental boundaries, (4) publishing protocols to describe how data were generated, (5) reporting the quantitative research impact of published literature, and (6) using dashboards to visualize the data collection. These efforts serve as a pivot point around which collective work at cross-site, regional, and national levels can occur. Harmonized data and metadata provide a robust foundation for the development of network information management and synergistic data science solutions.
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