A Workflow for (Meta)data Collection in Experimental Watersheds and Field Observatories
Abstract
The U.S. Department of Energy's (DOE) East River field observatory in the Upper Colorado River Basin is a mountainous, snow-dominated watershed study site being used to investigate how headwater systems will evolve to perturbations in a changing climate. The observatory is the primary field site for the Watershed Function Science Focus Area (SFA), which generates a wide variety of data. The SFA has developed a workflow for collecting and tracking field sampling metadata throughout data generation. Prior to the development of this workflow, instrument installment, sample collection efforts, and data analysis activities were not universally reported nor standardized, which made it difficult to track and discover data.
This workflow is designed to collect information about field sampling activities within the project and consists of: (1) registering locations with standardized, unique identifiers and detailed metadata; (2) registering samples with globally-unique sample identifiers; (3) registering sensors with detailed metadata; and (4) publishing data with relevant metadata on DOE's ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem). The metadata collection effort has resulted in documentation of field activities at over 1000 individual locations, over 500 samples, and over 900 sensor instruments, thus enabling the SFA to track and understand field sample data. A total of 94 datasets from the SFA are publicly available on ESS-DIVE. Moreover, standardized sampling metadata has enabled the development of publicly available, interactive metadata discovery tools, such as a field information portal for search of field activity information and a downloadable map file for spatial search of sampling locations. Together these tools allow researchers to plan new field activities, discover locations with relevant data, enable streamlined data curation and support advanced queries and linking. Experimental watersheds face challenges tracking diverse and occasionally overlapping field activities, associating samples to instrumentation, and linking related multidisciplinary datasets. The workflow presented here provides an approach to plan (meta)data collection into research activities at field observatories which make data publication, discovery and use easier.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H51C..01O