Regional and global efforts to monitor the status of, and identify mitigating actions for, climate change impacts and biodiversity loss requires copious amounts of survey and monitoring data that can only be obtained through the integration and analysis of multiple independently derived primary datasets from across a broad range of disciplines. Despite mandates to make ecological data FAIR and open (Wilkinson et al. 2016), data scientists and researchers attempting to integrate data from heterogeneous sources continue to face a series of pre-analysis hurdles including: finding data fit-for-use, non-interoperability across heterogenous data types and sources, a lack of full openness of data and broad availability through established research institutes, and missing or incomplete machine readable metadata (Blair et al. 2019). While overcoming these hurdles is no small feat, mechanisms do exist to facilitate the process; and two, in particular, are readily implementable.

Darwin Core biodiversity data standard.--Adopting a community-accepted data standard and vocabulary such as the Darwin Core (DwC) biodiversity data standard (https://dwc.tdwg.org/; Wieczorek et al. 2012) addresses three big data hurdles at once. Data standards are the primary mechanism for putting the ‘I’ (interoperable) in ‘FAIR’ data practices, ensuring machine readability of dataset metadata, and enhancing data discovery. Darwin Core is a community-maintained biodiversity data standard maintained by Biodiversity Information Standards (TDWG, https://www.tdwg.org/). The standard is constantly undergoing review and expansion to better accommodate and report facets of biodiversity data in a structured manner.

For example, consider the European Long-Term Ecological Research (eLTER, https://elter-ri.eu/) network. To address the basic tenet that ecological research is a cross-disciplinary field, requiring data from multiple domains to address questions of broad relevance, eLTER implemented their Whole Systems Approach to In-Situ Research on Life-supporting Systems (WAILS; Mirtl et al. 2021). The result is a standardized, systematic collection of monitoring data representing five distinct research areas from across Europe. These data have the potential to play a critical role in facilitating research and big data modeling efforts (for example, digital twins) across a broad range of data communities.

Using an eLTER biosphere dataset integrated into the BioDT Grassland Biodiversity Dynamics prototype digital twin (https://biodt.eu/use-cases/grassland-biodiversity-dynamics) as a case study, I will introduce the Darwin Core biodiversity data standard as an effective and implementable mechanism for improving the FAIRness of biodiversity survey data and demonstrate how sharing DwC formatted biodiversity data through a large global data aggregator such as GBIF is a complementary approach to other data sharing practices that can enhance the visibility and re-use of existing survey and monitoring data.