Wageningen Data Competence Center
The WDCC is established to support developments in the field of (big) data and data science at Wageningen University & Research.
WDCC brings education, research, value creation, infrastructure and data management together. The WDCC facilitates developments in these fields and serves as an internal and external contact point.
This way, WUR works on new applications 'To explore the potential of data to help improve the quality of life'.
- Unfortunately, your cookie settings do not allow videos to be displayed. - check your settings
Embracing Digital Transformation in Agri-Food: Course Data-Driven Agri-Food Business
Digitalisation is reshaping the agri-food sector, turning it into a data-driven economy. Technologies like generative AI are advancing rapidly, offering both opportunities and challenges for businesses.
Success requires more than technical expertise—it demands strategic collaboration, data-sharing, and transparency. Join us to explore new business models and navigate the digital future of agri-food.
Find out more
Read more
News
-
New BSc Data Science for Global Challenges: AI and data science for a better world
- Want to contribute to solutions for global challenges using artificial intelligence and data science? Starting next academic year, the first students... -
AI doesn’t need to stay a black box
- Artificial Intelligence may yield impressive results, but its inner workings often remain a mystery. Is our only option to blindly trust these... -
RDM support: wrap-up of 2024 & expert tips
- In 2024, the Research Data Management team supported many researchers. In this article, we look back at our main activities and highlight our plans...
Latest datasets
-
Data underlying the publication "Nile crocodiles in Lake Nasser, Egypt, are found close to fishermen’s camps indicating potential conflicts"
-
Dataset underlying the study "Observations of Estuarine Salt Intrusion Dynamics During a Prolonged Drought Event in the Rhine-Meuse Delta"
-
Data underlying the publication: Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery