The real value of what we share: how open data transforms the environment, health, and agriculture

When it comes to open data, it's easy to get lost in technical mazes. Often the debate focuses on file formats, semantic interoperability, licenses of use or metadata optimization. However, behind every set of data published by a public administration, there is a transformative potential that has a direct impact on people's daily lives.

In this post, we explain three specific projects currently underway in Spain, which use open data as raw material, and which have tangible consequences: in the control of the water quality of a natural park, in how science searches for new drugs against cancer and in the improvement of the response to extreme weather events.

Environment: monitoring the health of the Albufera de València in real time

The Albufera de València is one of the most important wetlands in the Mediterranean and also one of the most pressured. Decades of agricultural, industrial and tourist activity have left their mark on water quality and ecosystem health. Until now, the monitoring of this space was done with discontinuous, expensive methods and with a limited capacity to respond to extreme events. The DANA of October 2024 once again highlighted the need for real-time environmental information in order to act quickly.

In this context, OBEREK, a European project in which the Universitat Politècnica de València (UPV) and the Fundació Assut participate, emerges at the beginning of 2026. The project is developing a platform for real-time monitoring of the health of the Albufera ecosystem and biodiversity. The platform will install transmission nodes and sensors at critical points in the lake such as flow inlets or irrigation outlets to measure key parameters of the water and the natural environment.

What makes this initiative especially relevant from the perspective of open data is its access architecture: the system will have a publicly accessible dashboard so that citizens, researchers, farmers and companies can consult and reuse the data for decision-making. In addition, the project will integrate knowledge diagrams that will translate complex technical information into understandable explanations, expressly designed to facilitate its use as a participatory governance tool. Specifically, the project is key to:

• Crisis prevention: it allows anomalies in water quality to be detected early, avoiding episodes of anoxia (lack of oxygen) that endanger local fauna.
• Efficient water management: provides empirical data to regulate the gates that connect the wetland with the sea and the irrigation canals, optimizing water resources.
• Scientific evidence for public policy: government managers can design protection regulations based on a solid and transparent data history.

The ultimate goal, according to the UPV researchers, is for the solution to be replicable in at least five new European wetlands in the next three years.

Healthcare: artificial intelligence to accelerate cancer drug discovery

The second use case is in the health and biomedical research sector, where open data is beginning to change the rules of the game in one of the most expensive and time-consuming processes in modern science: the discovery of new drugs.

Developing a drug from scratch can take more than a decade and cost billions of euros. One of the reasons is the enormous difficulty in identifying which molecules have therapeutic potential before starting clinical trials. This is where the European Ligand-IA project comes in, in which the Vall d'Hebron Institute of Oncology (VHIO), one of the leading cancer research centres in Spain, participates.

This project uses advanced computational models and artificial intelligence algorithms trained and fed massively through the use of large open chemical, biological and clinical databases of public access.

Open databases provide the volume of biological and chemical information needed to train artificial intelligence algorithms. By analyzing this data, AI is able to perform a massive prediction of molecular interactions in virtual environments, which optimizes compound screening and drastically reduces times and costs in the accelerated discovery of new drugs.

Artificial intelligence requires a massive volume of previous data to learn and make accurate predictions. By reusing global open repositories of molecular structures and results from previous assays, the Ligand-IA consortium can simulate virtually millions of interactions between tumor proteins and different chemical compounds. So Ligand-IA is especially useful for:

• Drastic reduction of deadlines: what previously required years of trial and error in the chemistry lab, AI can virtually sift through in a matter of weeks or months.
• Optimization of research resources:  allows scientists to rule out early those molecules that will not be effective, concentrating economic and human efforts on the candidates with the highest probability of success.
• Democratization of knowledge: by using and enriching the open data ecosystem, a global collaborative science model is fostered that benefits the entire medical community.

Climate resilience: data intelligence in the face of extreme weather events

Predicting short-term weather using conventional weather observation is a standardized practice. However, anticipating with mathematical precision how, when, and where an extreme weather event will hit requires a much higher level of computation. In the current climate change scenario, the key to mitigating the human losses and the millionaire economic costs of these catastrophes lies in transforming the massive flows of global climate data into useful predictive knowledge.

With this strategic purpose, the European CLINT (Climate Intelligence) project was born, a cutting-edge initiative funded by the European Union's Horizon Europe framework programme for research, development and innovation (R+D+i). The Spanish National Research Council (CSIC) participates in the international consortium formed for the project, contributing to the lines of research aimed at the development of algorithms for the detection, causality and attribution of these extreme weather phenomena in future scenarios.

The operational core of CLINT consists of the development of an advanced artificial intelligence (AI) and machine learning framework  that is directly fed by large global open and publicly accessible  data repositories. These include pan-European information flows from the Copernicus Climate Change Service (C3S), as well as historical climate analysis and seasonal prediction models. This project helps to:

• Next-generation early warning systems: enables the creation of web-based operational climate services, providing river basin confederations and civil protection authorities with tools to anticipate extreme droughts or floods weeks in advance in the Iberian Peninsula.
• Efficient management of the water-energy-food nexus: by refining predictive models using open data, both companies in the (hydropower) energy sector and irrigation communities can make informed strategic decisions about water storage and crop planning.
• Scientific support for local adaptation policies: provides planners and public administrations with rigorous data and reliable climate projections at the regional level to design urban and contingency plans adapted to the challenges of global warming.

In summary, these three examples show how by sharing information in an accessible and standardized way, the public sector acts as a catalyst that exponentially multiplies the capabilities of the scientific and business fabric.  By unleashing knowledge, we enable science to advance faster, our natural resources to be managed responsibly, and society to be more resilient to the challenges of tomorrow. Promoting, maintaining and defending the culture of open data is therefore a strategic, intelligent and collaborative investment in our future collective well-being.