Effective data management is essential to high-quality biodiversity research, yet for many researchers, building and sustaining a robust Data Management Plan (DMP) still feels daunting. To support BiodivNBS-funded teams and strengthen data practices across projects, Biodiversa+ held a Data Management Workshop in May 2025, focused on practical guidance, open science principles, and emerging trends such as the use of artificial intelligence (AI) in data workflows.
Please note: Due to technical difficulties during the event, the audio quality is uneven in parts and the second half of the workshop is missing from the recording.
Takeaways
- More than a formality, DMPs are key to project success and continuity.
- Open science and FAIR principles benefit both researchers and society, and are increasingly required.
- AI is already enhancing biodiversity data workflows, but strong data foundations are essential.
- Sharing via platforms like GBIF boosts data value and enables broad reuse.
From burden to opportunity
An initial icebreaker revealed a spectrum of participant feelings, from viewing DMPs as just another obligation to enthusiasm about open data’s potential. This diversity guided the structure of the workshop, with presentations and Q&A sessions addressing concerns such as:
- Where and how to begin a DMP.
- How DMPs evolve over the lifetime of a project.
- What can be gained through open sharing of biodiversity data.
A practical example from the FUNACTION project illustrated how structured data management supports efficient collaboration, reduces risk of data loss, and ensures long-term value of datasets.
The fundamentals of Open Science and FAIR data
Open science isn’t just about data availability, it’s about transparency, reproducibility, and maximising the reach and reuse of research. The workshop explored:
- The benefits of open science for both society and individual researchers.
- The FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles.
- Repositories tailored to biodiversity research, such as GBIF, Movebank, and Zenodo.
Clear, actionable tips were also shared for building FAIR-aligned workflows and boosting visibility of data and outputs, both within and beyond academia.
The role of AI in data management
AI is increasingly embedded in the tools and systems used for biodiversity data collection and processing, but what does that mean for researchers managing data today?
The workshop explored its potential across the data lifecycle:
- Collection & processing: Automating image and sound classification (e.g. via iNaturalist, BirdNET).
- Database management: Detecting anomalies, enriching metadata, unifying taxonomy.
- Data transformation: Assisting with formatting, tagging, and linking across datasets.
The workshop also addressed the ethical and legal considerations of using AI, such as transparency, data bias, and GDPR compliance, as well as the infrastructure and expertise required for successful integration.
The case for global collaboration
The workshop highlighted the Global Biodiversity Information Facility (GBIF), a cornerstone of global biodiversity data sharing. With over 3 billion species occurrences and applications across research, policy, and conservation, GBIF exemplifies the value of open access biodiversity data.
From improved species distribution models to real-time public health insights, the benefits of contributing to shared data platforms are numerous. One study found that for every €1 invested in GBIF, €3 in scientific benefit and up to €12 in societal value were generated.
