What Is Your Favourite Bird? Using Biodiversity Data to Teach Data Literacy Across Disciplines
What is your favourite bird? At first glance, this question may seem unexpected and confusing, but in our experience, it is a highly effective entry point into a central challenge of bioinformatics and data science education: how can we teach data competences in a way that is accessible, memorable, interdisciplinary, and relevant to researchers with different background?
Nearly everyone has some relationship to birds, whether through childhood memories, urban encounters with pigeons, or simply the visual appeal of colourful plumage. While birding was recently mentioned to be the new trend hobby for millennials in public media, this familiarity can help to cross the gaps between sciences and generations in learning data competences.
In the DataNord project, one of eleven BMFTR funded Data Competence Centers in Germany, we provide training in data literacy for researchers at all career stages and from a wide range of disciplines in the Bremen region. Our workshops cover competences along the entire research data lifecycle. This interdisciplinary scope presents a recurring challenge: participants may come from molecular biology, ecology, marine sciences, social sciences, engineering, humanities or all interdisciplinary projects in between. They may be doctoral researchers working on their first study, experienced working group leaders managing large collaborative projects or postdocs wanting to explore new methods. As a result, examples used in teaching must be scientifically meaningful without being too domain-specific, technically simple enough for beginners, while still offering depth for advanced discussions.
Regional species occurrence data can be a strong foundation of storytelling in such workshops. Bird occurrence data are particularly useful for this purpose, as the underlying subject is familiar and emotionally approachable. This lowers the barriers and allows the workshop to focus on data literacy concepts rather than on explaining the biological system in excessive detail. Therefore, we use standardized occurrence data in our data literacy workshops from the Global Biodiversity Information Facility (GBIF) and the nonprofit umbrella organization of German ornithologists Dachverband Deutscher Avifaunisten (DDA) and their data portal ornitho.de.
Regional biodiversity datasets also support storytelling in teaching. It can tell stories about citizen science, conservation, local habitats, seasonal migration, urban ecology, and changing relationships between humans and nature. These stories can be strengthened through photographs, anecdotes, and recognizable species. Cute or striking images of birds are not simply decorative elements: they support emotional involvement, making abstract data concepts, like FAIR data, more concrete and memorable.
Using bird observation data enables multiple learning objectives within one coherent teaching framework. First, such datasets are usually simple enough to introduce basic principles of tabular data, including variables, data types, missing values, controlled vocabularies, and standardization of data and metadata. Participants can quickly identify common problems such as inconsistent species names, missing values, non-standardized formats, or unclear vocabularies. These examples can lead into discussions about metadata and documentation. Thus, participants can directly experience the importance of (meta)data standardization for interpretation, reuse, and reproducibility and the benefits of open science. Therefore, these datasets are well suited to teaching FAIR data principles. Findability can be discussed through persistent identifiers, descriptive metadata, and searchable repositories. Accessibility can be addressed through open data platforms and licensing. Interoperability becomes visible when comparing taxonomic standards, geospatial formats, or metadata schemas. Reusability can be explored by asking participants what information they would need to answer new research questions using a dataset collected by others. In this way, the FAIR principles become practical workshop questions rather than abstract guidelines.
Furthermore, these datasets offer excellent opportunities to discuss sampling biases, spatial resolutions, and challenges of data protection and sensitive data. Participants learn that open data does not mean publishing everything without reflection, but rather making data as open as possible and as protected as necessary.
Finally, the use of such datasets, promote the value of citizen science and science communication. This is particularly powerful in interdisciplinary teaching, because it highlights that data literacy is not only a technical skill but also a social and collaborative competence.
Here, we want to present our experiences using bird observation data as a versatile dataset in our workshops in the DataNord project. We show how a simple and emotionally engaging question like “What is your favourite bird?”, can open the door to complex topics such as data management plans, FAIR principles, metadata standards, geospatial visualization, sensitive data, open science, and citizen science. Discussing the combination of bird observations with other information like geospatial boundaries of nature reserves, fictive data management plans and sensitive datasets, we will present different workshops concepts we have used in our work. By combining relatable biodiversity examples with practical data exercises, we aim to make data literacy training more inclusive, memorable, and transferable across disciplines. Birds do not only cross borders, but also differences between sciences, generations and research cultures.