  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.
