At FAIRiCUBE, we believe that science should not only be accurate, but also accessible and engaging. Big data, Machine Learning, and causal inference can transform our understanding of biodiversity, agriculture, urban ecosystems, and energy demand—but these insights are often locked away in technical reports or databases.
To make complex information more intuitive, we created storytelling around the work of our Use Cases!
Tackling Invasive Alien Plant Species in Luxembourg (Use Case 1)
Invasive alien plant species (IAPS) are spreading rapidly in Europe’s cities, threatening ecosystems and urban biodiversity. Space4Environment built a storymap that models the probability of IAPS occurrence in Luxembourg. This tool supports local administrations by identifying hotspots where interventions are most needed, while also considering how climate change will accelerate spread.
👉 Explore: Luxembourg Invasive Species Storymap
Agriculture and Farmland Biodiversity (Use Case 2)
Agriculture dominates the Dutch landscape, and farming practices directly affect biodiversity, especially farmland birds. WUR created a storymap that shows how data cubes, machine learning, and causal inference can reveal not just patterns, but also the causal effects of farm-level interventions—like mowing or crop rotation—on species richness.
👉 Explore: Agriculture and Biodiversity Storymap
AI for Building Energy Demand (Use Case 4)
Reaching Europe’s net-zero goals requires better building data. To help prioritize renovations, NILU developed a scrollytelling experience showing how AI can predict building heights from aerial images—improving estimates of internal building volume and energy demand. The interactive story highlights challenges, early results, and the potential for scaling across Europe.
👉 Explore: Building Energy Demand Scrollytelling