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Title: A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations
Authors: Christianson, Danielle S.
Varadharajan, Charuleka
Christoffersen, Bradley O.
Detto, Matteo
Faybishenko, Boris A.
Gimenez, Bruno Oliva
Hendrix, Valerie C.
Jardine, Kolby J.
Negrón-Juárez, Robinson I.
Pastorello, Gilberto Z.
Powell, Thomas L.
Sandesh, Megha
Warren, Jeffrey M.
Wolfe, Brett T.
Chambers, Jeffrey Quintin
Kueppers, Lara M.
McDowell, Nathan G.
Agarwal, Deborah A.
Keywords: Data Interpretation
Data Quality
Data Set
Hierarchical System
Interdisciplinary Approach
Numerical Model
Quality Control
Spatial Resolution
Spatio-temporal Analysis
Issue Date: 2017
metadata.dc.publisher.journal: Ecological Informatics
metadata.dc.relation.ispartof: Volume 42, Pags. 148-158
Abstract: Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development. © 2017 Elsevier B.V.
metadata.dc.identifier.doi: 10.1016/j.ecoinf.2017.06.002
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