July 22, 2017
A Metadata Reporting Framework (FRAMES) for Synthesis of Ecohydrological Observations
A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations.
FRAMES is a set of Excel and online templates that standardize reporting of diverse ecohydrological data and the necessary metadata required for data synthesis to study Earth systems.
Detailed metadata—information that describes when, where, and how data is generated—are required for interpreting, comparing, validating, and synthesizing ecohydrological observations collected with diverse methods in different ecosystems. FRAMES bridges the gap between complex data information models that are needed to organize detailed metadata and specific ecohydrological data reporting protocols that lack enough detail for Earth system science research.
FRAMES templates standardize reporting of diverse ecohydrological data and metadata for data synthesis required for Earth system science research. This research team developed FRAMES iteratively with data providers and consumers who are developing a predictive understanding of carbon cycling in the tropics. Key features include: (1) Best data science practices, (2) Modular design that allows for addition of new measurement types, (3) Data entry formats that enable efficient reporting, (4) Multiscale hierarchy that links observations across spatiotemporal scales, and (5) Collection of metadata for integrating data with Earth system models.
Lawrence Berkeley National Laboratory
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
Research supported by Next-Generation Ecosystem Experiments (NGEE)–Tropics project, funded by the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science.
Christianson, D. S., C. Varadharajan, B. Christoffersen, and M. Detto, et al. "A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations". Ecological Informatics 42 148–158 (2017). https://doi.org/10.1016/j.ecoinf.2017.06.002.