Author(s): Christianson D; Varadharajan C; Christoffersen B; Detto M; Faybishenko B; Hendrix V; Jardine K; Negron-Juarez R; Gimenez B; Pastorello G; Powell T; Sandesh M; Warren J; Wolfe B; Chambers J; Kueppers L; McDowell N; Agarwal D
Site ID: XX-PTr
Site Name: Pantropical site
Variables: Dendrometry; Leaf temperature; Sapflow; Soil water content (saturation); Solar Radiation
Date Range: March 31, 2017 - None
Description: FRAMES is a a set of Excel metadata files and package-level descriptive metadata that are designed to facilitate and improve capture of desired metadata for ecohydrological observations. The metadata are bundled with data files into a data package and submitted to a data repository (e.g. the NGEE Tropics Data Repository) via a web form. FRAMES standardizes reporting of diverse ecohydrological and biogeochemical data for synthesis across a range of spatiotemporal scales and incorporates many best data science practices. This version of FRAMES supports observations for primarily automated measurements collected by permanently located sensors, including sap flow (tree water use), leaf surface temperature, soil water content, dendrometry (stem diameter growth increment), and solar radiation. Version 1.1 extend the controlled vocabulary and incorporates functionality to facilitate programmatic use of data and FRAMES metadata (R code available at NGEE Tropics Data Repository).
QA/QC: Full QA-QC
Methods Description: For more methods details, please refer to the associated publication. Below is a summary of the main steps of development: Our team-based project supports a dedicated data team that is tightly integrated with an interdisciplinary group of earth scientists. The data team led the development of FRAMES by working closely with data originators (the empiricists collecting the observations), as well as data consumers (the empiricists and also modelers using the data and metadata). We developed FRAMES to support the project’s first coordinated data collection effort centered around tree responses to drought conditions in Central and South America during the El Nino Southern Oscillation (ENSO) event of 2015-2016. Prior to developing FRAMES, we identified relevant aspects of existing protocols and standards to use as design foundations including ISO standards (ISO 8601, ISO 19115-1:2014), FGDC standards (FGDC 1998), Ameriflux/BADM templates (AmeriFlux 2016), ISCN reporting templates (ISCN 2016), STRI-CTFS protocols (Anderson-Teixeira et al. 2014; Condit et al. 2014), RAINFOR-GEM protocols (Marthews et al. 2014; RAINFOR 2016), and Sapfluxnet (Poyatos et al. 2016). The approach we used to develop FRAMES involved a combination of agile development principles and scientist-centered design (Ramakrishnan et al. 2014). Based on requests from members of the project’s science team, we focused our efforts on collecting metadata necessary to provide interpretation, cross-site comparison, and QA/QC for a prioritized list of ENSO observations. These observations were primarily automated measurements collected by permanently located sensors, including sap flow (tree water use), leaf surface temperature, soil water content, dendrometry (stem diameter growth increment), and solar radiation. Working closely with data originators and data consumers, we addressed one or two measurement types at a time, building out FRAMES as we added additional measurement types. Initial template designs were based on existing data collection protocols and informational interviews conducted with data originators to understand the measurement procedure, identify existing metadata collection methods, and discuss additional metadata collection. We iteratively tested FRAMES with data originators, incorporating additional measurement types and feedback based on field metadata entry trials. We also conducted informational interviews with other data originators and consumers of anticipated measurement types (primarily sample-based observations including leaf water potential, gas exchange, and non-structural carbohydrates) to check for compatibility with FRAMES. To minimize the effort of data originators, we transferred information already submitted in previous versions of FRAMES to the newer versions throughout the iterative development. Finally, FRAMES was designed to facilitate submission to data repositories, including the NGEE Tropics Archive, the project’s data repository. The NGEE Tropics Archive has a web portal that allows data originators to upload and download data packages. In general, completeness and accuracy of metadata submitted via FRAMES templates are considered to be the responsibility of the data originator, although the data team manually inspects data package submissions via the NGEE Tropics Archive portal. The peer-review process enabled by datasharing provides input to data originators to make corrections to their data.
Access Level: Public
Originating Institution(s): Lawrence Berkeley National Laboratory
Sponsor Organization(s): None
Contact: Christianson, Danielle - Berkeley Lab (DSChristianson@lbl.gov)
Version: 1.0
Dataset Citation: Christianson D; Varadharajan C; Christoffersen B; Detto M; Faybishenko B; Hendrix V; Jardine K; Negron-Juarez R; Gimenez B; Pastorello G; Powell T; Sandesh M; Warren J; Wolfe B; Chambers J; Kueppers L; McDowell N; Agarwal D (2018): FRAMES Metadata Reporting Templates for Ecohydrological Observations, version 1.1. 1.0. NGEE Tropics Data Collection. (dataset). http://dx.doi.org/10.15486/ngt/1419956
Acknowledgement: This research was supported as part of NGEE-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231.
Data Link: Download Dataset
Christianson, D. S., Varadharajan, C., Christoffersen, B., Detto, M., Faybishenko, B., Gimenez, B. O., Hendrix, V., Jardine, K. J., Negron-Juarez, R., Pastorello, G. Z., Powell, T. L., Sandesh, M., Warren, J. M., Wolfe, B. T., Chambers, J. Q., Kueppers, L. M., McDowell, N. G., & Agarwal, D. A. (2017). A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations. Ecological Informatics, 42, 148–158. https://doi.org/10.1016/j.ecoinf.2017.06.002
