Abstract
Data standardization can enable data reuse by streamlining the way data
are collected, providing descriptive metadata, and enabling machine
readability. Standardized open-source data can be more readily reused in
interdisciplinary research that requires large amounts of data, such as
climate modeling. Despite the importance given to both FAIR (Findable,
Accessible, Interoperable, Reusable) data practices and the need for
open-source data, a remaining question is how community data standards
and open-source data can be adopted by research data providers and
ultimately achieve FAIR data practices. In an attempt to answer this
question, we used newly created water quality community data reporting
formats and applied them to open-source water quality data. The
development of this water quality data format was curated with several
other related formats (e.g., CSV, Sample metadata reporting formats),
aimed at targeting the research community that have historically
published water quality data in a variety of formats. The water quality
community data format aims to standardize how these types of data are
stored in the data repository, ESS-DIVE (Environmental Systems Science
Data Infrastructure for a Virtual Ecosystem). Adoption of these formats
will also follow FAIR practices, increase machine readability, and
increase the reuse of this data. We applied this community format to
open-source water quality data produced by the Watershed Function
Scientific Focus Area (WFSFA), a large watershed study in the East River
Colorado, which involves many national laboratories, institutions,
scientists, and disciplines. In this presentation, we provide a
demonstration of a relatively efficient process for converting
open-source water quality data into a format that adheres to a community
data standard. We created examples of water quality data translated to
the reporting formats that demonstrated the functionality of these data
standards; descriptive metadata and sample names, streamlined data
entries, and increased machine readability were products of this
translation. As the community data standards are integrated within the
WFSFA data collection processes, and ultimately all data providers of
ESS-DIVE, these steps may enable interdisciplinary data discovery,
increase reuse, and follow FAIR data practices.