Biodiversity data standards for the organization and dissemination of complex research projects and digital twins: a guide

TL;DR

This paper provides a comprehensive guide on biodiversity data standards, emphasizing their importance for data integration, interoperability, and supporting complex research projects like digital twins in biodiversity science.

Contribution

It introduces a practical framework for understanding and applying biodiversity data standards, including case studies and approaches to enhance data interoperability and computational accessibility.

Findings

Comparison of three data compilation methods

Explanation of data standards' role in interoperability

Case study of Biodiversity Digital Twin (BioDT)

Abstract

Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more commonplace. Open science promotes open sharing and data usage. Data standardization is an instrument for the organization and integration of biodiversity data, which is required for complex research projects and digital twins. However, just like with an actual instrument, there is a learning curve to understanding the data standards field. Here we provide a guide, for data providers and data users, on the logistics of compiling and utilizing biodiversity data. We emphasize data standards, because they are integral to data integration. Three primary avenues for compiling biodiversity data are compared, explaining the importance of research infrastructures for coordinated long-term data aggregation. We exemplify the Biodiversity Digital Twin (BioDT) as a case study. Four approaches to data standardization are presented in terms of the balance between practical constraints and the advancement of the data standards field. We aim for this paper to guide and raise awareness of the existing issues related to data standardization, and especially how data standards are key to data interoperability, i.e., machine accessibility. The future is promising for computational biodiversity advancements, such as with the BioDT project, but it rests upon the shoulders of machine actionability and readability, and that requires data standards for computational communication.