Collecting biodiversity data takes a lot of energy and resources. This makes biodiversity data not only valuable for the scientist who collected it, but also to support further research and to inform policy on conservation, natural resources, land use, agriculture and more [1][2]. It is important to create a maximum output of this valuable data to protect nature, wildlife and endangered species, to prevent overfishing and extinctions as a way to counteract the climate crisis and biodiversity crisis [3]. 

Thus, biodiversity data need to be carefully handled, preserved, and shared, by making them Findable, Accessible, Interoperable and Reusable (FAIR) [4], and considering CARE-Principles (Collective Benefit, Authority to Control, Responsibility, and Ethics) [5]. 

Figure 1: Global extinction risk in different species groups. Assessments of extinction risks are only possible for taxa with sufficient data. Here, there is a difference between * comprehensive, ** sampled and *** selected coverage of taxa. Large and important taxa like e.g. insects are almost completely missing in this analysis, due to insufficient data. Source: IPBES 2019, figure SPM3, p. 26, Copyright © IPBES 2019

To put good research practice [6] and the FAIR principles into practice effective research data management (RDM) is needed. This requires a comprehensive approach that encompasses the planning, collection, storage, analysis, and collaborative sharing of diverse biodiversity data through a well-structured and coordinated research data management strategy [7]. For research in the biodiversity field, research data management is even more important as non-repeatability and the need to rely on old data, e.g. for trend analysis, is of utmost relevance.

This video (published by GFBio, 2020) explains the challenges and solutions of dealing with heterogeneous data.