Biodiversity data encompass a vast and interdisciplinary collection of information, spanning all living species and the entire spectrum of life that has ever existed. This remarkable diversity within biodiversity data makes it a highly heterogeneous field. These datasets range from laboratory-generated data, such as results from chemical assays, biological tests, and DNA sequencing, to taxonomic records, as well as spatial and temporal data collected during field experiments, and comprehensive insights into entire ecosystems [1][2][3]. Furthermore, biodiversity data extend their reach into time, including data collected from the distant past, like fossils, and continue into the present while even venturing into predictive models of the future. The complexity of biodiversity data doesn't stop there; these datasets manifest in various formats, including images, photographs, sounds, sensor data, and more. The confluence of this diversity, alongside the increasing automation processes and digitalization of data acquisition, has resulted in a deluge of heterogeneous data, necessitating progressively complex organisation, coordination, and analysis methods.

To address these challenges, Germany has established the National Research Data Infrastructure (NFDI), a pivotal initiative. Within this framework, the consortium "NFDI4Biodiversity" plays a central role. This collaborative effort brings together experts and resources to develop innovative strategies and tools for managing, coordinating, and extracting meaningful insights from the wealth of biodiversity data available today. These efforts are essential in supporting research and conservation endeavours in this field.

In the ever-evolving realms of ecology and environmental science, data play a pivotal role for generating new knowledge. From collecting data in the field to conducting experiments in the lab, researchers generate vast amounts of data. This data holds the key to understanding our natural world, from the intricate ecosystems that surround us to the impact of human activities on the environment. However, the value of this data is fully realised only when it is properly managed, organised, and shared, according to the FAIR [4] and CARE-principles [5]. This self-learning unit is designed to empower you with the knowledge and skills you need to learn independently and effectively. Throughout this document, we will explore topics such as data collection best practices, data organisation and documentation, data storage and security, ethical considerations, and the importance of data sharing and collaboration in advancing ecological and environmental research. Whether you are a seasoned researcher looking to enhance your data management practices or a student taking your first steps into this exciting domain, you'll find valuable insights and practical guidance here, to promote data diversity for biodiversity.

As you progress through this unit, you'll become proficient in managing data effectively and contribute to the advancement of ecological and environmental science by ensuring that your research data is findable, accessible, interoperable, and reusable. These skills are not only valuable for your work but also for the broader scientific community and society as a whole. Ecology and environmental science rely heavily on data to understand the natural world and address environmental challenges - data diversity for biodiversity.