By: PredictBy
Tackling climate change requires more than good intentions – it requires reliable, accessible, and meaningful data. At NEUROCLIMA, we see data not just as information, but as a resource that can unlock smarter climate decisions, foster innovation, and empower stakeholders.
Our approach to data management is designed to turn raw information into actionable climate intelligence. At the heart of this work lies a structured framework we call the N-Solution Funnel: a step-by-step process, as described in the figure below, to identify, filter, and prepare the most relevant climate data for use in the NEUROCLIMA solution.
Three key types of data power NEUROCLIMA:
- Climate Data – science-based datasets, research papers, news, and policy documents (and more) that provide trusted knowledge on climate challenges.
- Metadata – structured information that makes datasets findable and usable, following the FAIR principles (Findable, Accessible, Interoperable, Reusable).
- Social Tipping Point[1] Data: shaped to train the NEUROCLIMA solution to recognise events or signals that may trigger systemic behavioural shifts in society.
For our Minimum Viable Product (MVP), the consortium chose Agrifood as the first focus area. Through a rigorous filtering process, we curated 61 high-value data sources – ensuring they are open-access, science-driven, and legally compliant under creative commons licensing. These range from research papers and scientific datasets to policy documents and credible news articles.
But collecting data is only the beginning. To make it meaningful, NEUROCLIMA has built a data infrastructure:
- A Data Lake to store unstructured materials like PDFs and datasets.
- An OpenSearch system to manage metadata, enabling rapid and scalable search.
- Automated ingestion pipelines that will, over time, connect directly to APIs, ensuring fresh information without manual intervention.
This combination guarantees that the data driving NEUROCLIMA is both robust and scalable.
Next Steps
Going forward, the next phase of development will focus on enhancing automation through API integration, enabling the retrieval of data directly from identified sources without manual intervention – as was successfully demonstrated in the MVP. This allows to expand the number of data sets relevant within Agrifood and the future climate data themes, such as Energy. Another key step will involve enriching the Agrifood dataset by incorporating sources related to the EU Adaptation Strategy, including relevant practices and projects, and more. Finally, the scope of data types may be expanded to include other considered types of data (e.g., social media content, statistics) offering real-time insights into emerging trends.
[1]A social tipping point in climate adaptation occurs when a small but strategically placed change—such as a new policy, innovative technology, or shift in public perception—overcomes entrenched barriers (economic, cultural or informational) and sparks a rapid, self-reinforcing cascade of behavioural and institutional shifts that embed adaptive practices across entire communities and sectors.
