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Three years after the acceleration of the massive deployment of Artificial Intelligence began with the launch of ChatGPT, a new term emerges strongly: Agentic AI. In the last three years, we have gone from talking about language models (such as LLMs) and chatbots (or conversational assistants) to designing the first systems capable not only of answering our questions, but also of acting autonomously to achieve objectives, combining data, tools and collaborations with other AI agents or with humans. That is, the global conversation about AI is moving from the ability to "converse" to the ability to "act" of these systems.

In the private sector, recent reports from large consulting firms describe AI agents that resolve customer incidents from start to finish, orchestrate supply chains, optimize inventories in the retail sector  or automate business reporting. In the public sector, this conversation is also beginning to take shape and more and more administrations are exploring how these systems can help simplify procedures or improve citizen service. However, the deployment seems to be somewhat slower because logically the administration must not only take into account technical excellence but also strict compliance with the regulatory framework, which in Europe is set by the AI Regulation, so that autonomous agents are, above all, allies of citizens.

What is Agentic AI?

Although it is a recent concept that is still evolving, several administrations and bodies are beginning to converge on a definition. For example, the UK government describes agent AI as systems made up of AI agents that "can autonomously behave and interact to achieve their goals." In this context, an AI agent would be a specialized piece of software that can make decisions and operate cooperatively or independently to achieve the system's goals.

We might think, for example, of an AI agent in a local government who receives a request from a person to open a small business. The agent, designed in accordance with the corresponding administrative procedure, would check the applicable regulations, consult urban planning and economic activity data, verify requirements, fill in draft documents, propose appointments or complementary procedures and prepare a summary so that the civil servants could review and validate the application. That is, it would not replace the human decision, but would automate a large part of the work between the request made by the citizen and the resolution issued by the administration.

Compared to a conversational chatbot – which answers a question and, in general, ends the interaction there – an AI agent can chain multiple actions, review results, correct errors, collaborate with other AI agents and continue to iterate until it reaches the goal that has been defined for it. This does not mean that autonomous agents decide on their own without supervision, but that they can take over a good part of the task always following well-defined rules and safeguards.

Key characteristics of a freelance agent include:

• Perception and reasoning: is the ability of an agent to understand a complex request, interpret the context, and break down the problem into logical steps that lead to solving it.
• Planning and action: it is the ability to order these steps, decide the sequence in which they are going to be executed, and adapt the plan when the data changes or new constraints appear.
• Use of tools: An agent can, for example, connect to various APIs, query databases, open data catalogs, open and read documents, or send emails as required by the tasks they are trying to solve.
• Memory and context: is the ability of the agent to maintain the memory of interactions in long processes, remembering past actions and responses and the current state of the request it is resolving.
• Supervised autonomy: an agent can make decisions within previously established limits to advance towards the goal without the need for human intervention at each step, but always allowing the review and traceability of decisions.

We could summarize the change it entails with the following analogy: if LLMs are the engine of reasoning, AI agents are systems that , in addition to the ability to "think" about the actions that should be done, have "hands" to interact with the digital world and even with the physical world and execute those same actions.

The potential of AI agents in public services

Public services are organized, to a large extent, around processes of a certain complexity such as the processing of aid and subsidies, the management of files and licenses or the citizen service itself through multiple channels. They are processes with many different steps, rules and actors, where repetitive tasks and manual work of reviewing documentation abound.

As can be seen in the  European Union's eGovernment Benchmark, eGovernment initiatives in recent decades have made it possible to move towards greater digitalisation of public services. However, the new wave of AI technologies, especially when foundational models are combined with agents, opens the door to a new leap to intelligently automate and orchestrate a large part of administrative processes.

In this context, autonomous agents would allow:

• Orchestrate end-to-end processes such as collecting data from different sources, proposing forms already completed, detecting inconsistencies in the documentation provided, or generating draft resolutions for validation by the responsible personnel.
• Act as "co-pilots" of public employees, preparing drafts, summaries or proposals for decisions that are then reviewed and validated, assisting in the search for relevant information or pointing out possible risks or incidents that require human attention.
• Optimise citizen service processes by  supporting tasks such as managing medical appointments, answering queries about the status of files, facilitating the payment of taxes or guiding people in choosing the most appropriate procedure for their situation.

Various analyses on AI in the public sector suggest that this type of intelligent automation, as in the private sector, can reduce waiting times, improve the quality of decisions and free up staff time for more value-added tasks. A recent report by PWC and Microsoft exploring the potential of Agent AI for the public sector sums up the idea well, noting that by incorporating Agent AI into public services, governments can improve responsiveness and increase citizen satisfaction, provided that the right safeguards are in place.

In addition, the implementation of autonomous agents allows us to dream of a transition from a reactive administration (which waits for the citizen to request a service) to a proactive administration that offers to do part of those same actions for us: from notifying us that a grant has been opened for which we probably meet the requirements,  to proposing the renewal of a license before it expires or reminding us of a medical appointment.

An illustrative example of the latter could be an AI agent that, based on data on available services and the information that the citizen himself has authorised to use, detects that a new aid has been published for actions to improve energy efficiency through the renovation of homes and sends a personalised notice to those who could meet the requirements. Even offering them a pre-filled draft application for review and acceptance. The final decision is still human, but the effort of seeking information, understanding conditions, and preparing documentation could be greatly reduced.

The role of open data

For an AI agent to be able to act in a useful and responsible way, they need to leverage on an environment rich in quality data and a robust data governance system. Among those assets needed to develop a good autonomous agent strategy, open data is important in at least three dimensions:

1. Fuel for decision-making: AI agents need information on current regulations, service catalogues, administrative procedures, socio-economic and demographic indicators, data on transport, environment, urban planning, etc. To this end, data quality and structure is of great importance as outdated, incomplete, or poorly documented data can lead agents to make costly mistakes. In the public sector, these mistakes can translate into unfair decisions that could ultimately lead to a loss of public trust.
2. Testbed for evaluating and auditing agents: Just as open data is important for evaluating generative AI models, it can also be important for testing and auditing autonomous agents. For example, simulating fictitious files with synthetic data based on real distributions to check how an agent acts in different scenarios. In this way, universities, civil society organizations and the administration itself can examine the behavior of agents and detect problems before scaling their use.
3. Transparency and explainability: Open data could help document where the data an agent uses came from, how it has been transformed, or which versions of the datasets were in place when a decision was made. This traceability contributes to explainability and accountability, especially when an AI agent intervenes in decisions that affect people's rights or their access to public services. If citizens can consult, for example, the criteria and data that are applied to grant aid, confidence in the system is reinforced.

The panorama of agent AI in Spain and the rest of the world

Although the concept of agent AI is recent, there are already initiatives underway in the public sector at an international level and they are also beginning to make their way in the European and Spanish context:

• The Government Technology Agency (GovTech) of Singapore has published an Agentic AI Primer guide  to guide developers and public officials on how to apply this technology, highlighting both its advantages and risks. In addition, the government is piloting the use of agents in various settings to reduce the administrative burden on social workers and support companies in complex licensing processes. All this in a controlled environment (sandbox) to test these solutions before scaling them.
• The UK government has published a specific note within its "AI Insights" documentation to explain what agent AI is and why it is relevant to government services. In addition, it has announced a tender to develop a "GOV.UK Agentic AI Companion" that will serve as an intelligent assistant for citizens from the government portal.
• The European Commission, within the framework of the Apply AI strategy and the GenAI4EU initiative, has launched calls to finance pilot projects that introduce scalable and replicable generative AI solutions in public administrations, fully integrated into their workflows. These calls seek precisely to accelerate the pace of digitalization through AI (including specialized agents) to improve decision-making, simplify procedures and make administration more accessible.

In Spain, although the label "agéntica AI" is not yet widely used, some experiences that go in that direction can already be identified. For example, different administrations are incorporating co-pilots based on generative AI to support public employees in tasks of searching for information, writing and summarizing documents, or managing files, as shown by initiatives of regional governments such as that of Aragon and local entities such as Barcelona City Council that are beginning to document themselves publicly.

The leap towards more autonomous agents in the public sector therefore seems to be a natural evolution on the basis of the existing e-government. But this evolution must, at the same time, reinforce the commitment to transparency, fairness, accountability, human oversight and regulatory compliance required by the AI Regulation and the rest of the regulatory framework and which should guide the actions of the public administration.

Looking to the Future: AI Agents, Open Data, and Citizen Trust

The arrival of agent AI once again offers the public administration new tools to reduce bureaucracy, personalize care and optimize its always scarce resources. However, technology is only a means, the ultimate goal is still  to generate public value by reinforcing the trust of citizens.

In principle, Spain is in a good position: it has an Artificial Intelligence Strategy 2024 that is committed to transparent, ethical and human-centred AI, with specific lines to promote its use in the public sector; it has aconsolidated open data infrastructure; and it has created the Spanish Agency for the Supervision of Artificial Intelligence (AESIA) as a body in charge of ensuring an ethical and safe use of AI, in accordance with the European AI Regulation.

We are, therefore, facing a new opportunity for modernisation that can build more efficient, closer and even proactive public services. If we are able to adopt the Agent AI properly, the agents that are deployed will not be a "black box" that acts without supervision, but digital, transparent and auditable "public agents", designed to work with open data, explain their decisions and leave a trace of the actions they take. Tools, in short, inclusive, people-centred and aligned with the values of public service.

Content created by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalisation. The contents and views expressed in this publication are the sole responsibility of the author.

Rampa is a route service for people with reduced mobility. This project, developed as part of the Madrid City Council's 2025 Open Data Reuse Awards, combines open data and geospatial technology to facilitate the search for accessible routes and services for people with reduced mobility.

The web application provides the following data:

• Accessibility: it offers optimised routes for wheelchairs, walkers and people with reduced mobility.
• Demographic data: it allows you to view ageing, dependency and population density indices to better understand the needs of each area.
• Control panel: it allows you to view the areas with the highest and lowest accessibility in Madrid and detects the most accessible neighbourhoods and districts.

Web platform for filtering and organizing proposals from the Congress of Deputies according to party votes, with the ability to show how one or more parties voted collectively or individually.

As proposals may have several votes, the main vote for each proposal is extracted using a ChatGPT AI model. AI is also used to extract information on the different points of a proposal and generate an explanatory description of the proposal that is understandable to anyone.

The aim is to have a platform where the activity of the Congress of Deputies can be seen clearly and simply.

The reuse of open data makes it possible to generate innovative solutions that improve people's lives, boost citizen participation and strengthen public transparency. Proof of this are the competitions promoted this year by the Junta de Castilla y León and the Madrid City Council.

Being the IX edition of the Castilla y León Competition and the first edition of the Madrid Competition, both administrations have presented the prizes to the selected projects, recognising both students and startups as well as professionals and researchers who have been able to transform public data into useful tools and knowledge. In this post, we review the award-winning projects in each competition and the context that drives them.

Castilla y León: ninth edition of consolidated awards in a more open administration

At the awards ceremony of the IX Open Data Contest of the Junta de Castilla y León,  the budget reinforcement (+65%) in the General Directorate of Transparency and Good Governance, the expansion of active advertising content and a continuous improvement of the right of access to public information, which has reduced requests and rejection resolutions, were highlighted. The Open Data Portal of Castilla y León has 776 datasets that allow the development of services, applications and studies each year.

The Open Data Awards recognize initiatives in four categories: Ideas, Products and Services, Teaching Resources, Data Journalism.

Ideas

• First prize: CyL Rural Hub. Proposal to develop a comprehensive platform for the rural territory that centralises services, infrastructures, job opportunities and educational offer. Its objective is to provide families and professionals with useful information to plan a life project in the villages of the community.

• Second prize: Cultural App of Castilla y León. An idea aimed at boosting cultural activity through an application that centralises events, activities and locations, also offering an intuitive and close experience based on open data.

Products & Services

• First prize: CyL Bridge. Application designed to support the integration of migrants through personalized routes, an artificial intelligence assistant and a resource center powered by public data.

• Second prize: MuniCyL. A tool that brings together dispersed municipal information and presents it on a single clear, accessible and up-to-date platform.

• Third prize: Interactive map of Natural Spaces. A resource that allows citizens to explore the protected areas of the territory dynamically and in real time.

• Student awards: Info Salamanca. Platform that offers interactive maps, thematic filters and a conversational assistant to bring provincial information closer and facilitate the consultation of citizen data.

Teaching Resource

• First prize: Use of open data from the Junta de Castilla y León in web development. A project that introduces open data into the learning of web development, with practical exercises and an AI search engine to work directly with real data from the portal.

Data Journalism

• First prize: Heart attacks are no longer a matter of age, a report on the increase in heart attacks among young people.

• Second prize: Burgos maintains regional leadership with 79 wind farms: an analysis of the deployment of renewable energies in the region.

Madrid: first edition of awards that promote reuse in the urban environment

On the other hand, the Madrid City Council has held the first edition of the Open Data Reuse Awards 2025. The ceremony highlighted the quality and diversity of the 65 applications submitted, many of them driven by university students and startups.

The awards seek to promote the use of data  from the Madrid City Council's Open Data Portal, support the creation of services and studies that contribute to knowledge of the city and reinforce the role of the city council as a benchmark administration in transparency and accountability.

In this case, the awards are structured into four categories: Web Services and Applications, Visualizations, Studies and Ideas, and Portal Improvement.

Web Services & Applications

• First prize: Madriwa. Find your place in Madrid. A tool that facilitates the search for housing through data on neighbourhoods, services and prices, allowing an informed and simplified comparison.

• Second prize: The guardians of the air. Application developed by Tangible Data to check the city's air quality, especially designed to raise awareness among young people and educational centers.

Data viz

• First prize: Ramp. Routes for people with reduced mobility. It presents accessible itineraries based on geospatial and orography data, offering alternative routes adapted to people with reduced mobility.

• Second prize: AccesibiliMad. It shows public services available in each urban environment, with special attention to the specific needs of different groups.

Studies, Research and Ideas

• First prize: Fifteen-minute cities for children. Analysis of the availability of essential services for minors within a maximum radius of 15 minutes, providing an innovative vision of urban planning.

• Second prize: The impact of tourism in urban areas. This study delves into the relationship between tourist housing, the commercial fabric and labour dynamics, using urban and socio-economic data.

Improving Portal Quality

• First prize: Your Open Data. Improving harvesting in data.europa.eu. Proposal that improves the way data is provided, raising the quality of metadata and boosting European interoperability.

• Second prize: Discovery, observability and intelligent governance of open data. Solution that introduces an automated layer of intelligence and control over the municipal catalog.

Both Castilla y León, with a consolidated track record, and the Madrid City Council, which inaugurates its own recognition, contribute decisively to strengthening the Spanish open data ecosystem. Its calls are an example of how collaboration between administrations, citizens, academia and the private sector can transform public data into knowledge, participation and innovation at the service of society as a whole.

In the public sector ecosystem, subsidies represent one of the most important mechanisms for promoting projects, companies and activities of general interest. However, understanding how these funds are distributed, which agencies call for the largest grants or how the budget varies according to the region or beneficiaries is not trivial when working with hundreds of thousands of records.

In this line, we present a new practical exercise in the series "Step-by-step data exercises", in which we will learn how to explore and model open data using Apache Spark, one of the most widespread platforms for distributed processing and large-scale machine learning.

In this laboratory we will work with real data from the National System of Advertising of Subsidies and Public Aid (BDNS) and we will build a model capable of predicting the budget range of new calls based on their main characteristics.

All the code used is available in the corresponding GitHub repository so that you can run it, understand it, and adapt it to your own projects.

Access the datalab repository on GitHub

Run the data pre-processing code on Google Colab

Context: why analyze public subsidies?

The BDNS collects detailed information on hundreds of thousands of calls published by different Spanish administrations: from ministries and regional ministries to provincial councils and city councils. This dataset is an extraordinarily valuable source for:

• analyse the evolution of public spending,
• understand which organisms are most active in certain areas,
• identify patterns in the types of beneficiaries,
• and to study the budget distribution according to sector or territory.

In our case, we will use the dataset to address a very specific question, but of great practical interest:

Can we predict the budget range of a call based on its administrative characteristics?

This capability would facilitate initial classification, decision-making support or comparative analysis within a public administration.

Objective of the exercise

The objective of the laboratory is twofold:

1. Learn how to use Spark in a practical way:

• Upload a real high-volume dataset
• Perform transformations and cleaning
• Manipulate categorical and numeric columns
• Structuring a  machine learning pipeline

2. Building a predictive model

We will train a classifier capable of estimating whether a call belongs to one of these ranges of low budget (up to €20k), medium (between €20 and €150k) or high (greater than €150k), based on variables such as:

• Granting body
• Autonomous community
• Type of beneficiary
• Year of publication
• Administrative descriptions

Resources used

To complete this exercise we use:

Analytical tools

• Python, the main language of the project
• Google Colab, to run Spark and create Notebooks in a simple way
• PySpark, for data processing in the cleaning and modeling stages
• Pandas, for small auxiliary operations
• Plotly, for some interactive visualizations

Data

Official dataset of the National System of Advertising of Subsidies (BDNS), downloaded from the subsidy portal of the Ministry of Finance.

The data used in this exercise were downloaded on August 28, 2025. The reuse of data from the National System for the Publicity of Subsidies and Public Aid is subject to the legal conditions set out in https://www.infosubvenciones.es/bdnstrans/GE/es/avisolegal.

Development of the exercise

The project is divided into several phases, following the natural flow of a real Data Science case.

5.1. Data Dump and Transformation

In this first section we are going to automatically download the subsidy dataset from the API of the portal of the National System of Publicity of Subsidies (BDNS). We will then transform the data into an optimized format such as Parquet (columnar data format) to facilitate its exploration and analysis. 

In this process we will use some complex concepts, such as:

• Asynchronous functions: allows two or more independent operations to be processed in parallel, which makes it easier to make the process more efficient.
• Rotary writer: when a limit on the amount of information is exceeded, the file being processed is closed and a new one is opened with an auto-incremental index (after the previous one). This avoids processing files that are too large and improves efficiency.

Figure 1. Screenshot of the API of the National System for Advertising Subsidies and Public Aid

5.2. Exploratory analysis

The aim of this phase is to get a first idea of the characteristics of the data and its quality.

We will analyze, among others, aspects such as:

• Which types of subsidies have the highest number of calls.

Figure 2. Types of grants with the highest number of calls for applications.

• What is the distribution of subsidies according to their purpose (i.e. Culture, Education, Promotion of employment...).

Figure 3. Distribution of grants according to their purpose.

• Which purposes add a greater budget volume.

Figure 4. Purposes with the largest budgetary volume.

5.3. Modelling: construction of the budget classifier

At this point, we enter the most analytical part of the exercise: teaching a machine to predict whether a new call will have a low, medium or high budget based on its administrative characteristics. To achieve this, we designed a  complete machine learning pipeline in Spark that allows us to transform the data, train the model, and evaluate it in a uniform and reproducible way.

First, we prepare all the variables – many of them categorical, such as the convening body – so that the model can interpret them. We then combine all that information into a single vector that serves as the starting point for the learning phase.

With that foundation built, we train a classification model that learns to distinguish subtle patterns in the data: which agencies tend to publish larger calls or how specific administrative elements influence the size of a grant.

Once trained, we analyze their performance from different angles. We evaluate their ability to correctly classify the three budget ranges and analyze their behavior using metrics such as accuracy or the confusion matrix.

Figure 5. Accuracy metrics.

But we do not stop there: we also study which variables have had the greatest weight in the decisions of the model, which allows us to understand which factors seem most decisive when it comes to anticipating the budget of a call.

Figure 6. Variables that have had the greatest weight in the model's decisions.

Conclusions of the exercise

This laboratory will allow us to see how Spark simplifies the processing and modelling of high-volume data, especially useful in environments where administrations generate thousands of records per year, and to better understand the subsidy system after analysing some key aspects of the organisation of these calls.

Do you want to do the exercise?

If you're interested in learning more about using Spark and advanced public data analysis, you can access the repository and run the full Notebook step-by-step.

Content created by Juan Benavente, senior industrial engineer and expert in technologies related to the data economy. The content and views expressed in this publication are the sole responsibility of the author.

We live in an age where more and more phenomena in the physical world can be observed, measured, and analyzed in real time. The temperature of a crop, the air quality of a city, the state of a dam, the flow of traffic or the energy consumption of a building are no longer data that are occasionally reviewed: they are continuous flows of information that are generated second by second.

This revolution would not be possible without cyber-physical systems (CPS), a technology that integrates sensors, algorithms and actuators to connect the physical world with the digital world. But CPS does not only generate data: it can also be fed by open data, multiplying its usefulness and enabling evidence-based decisions.

In this article, we will explore what CPS is, how it generates massive data in real time, what challenges it poses to turn that data into useful public information, what principles are essential to ensure its quality and traceability, and what real-world examples demonstrate the potential for its reuse. We will close with a reflection on the impact of this combination on innovation, citizen science and the design of smarter public policies.

What are cyber-physical systems?

A cyber-physical system is a tight integration between digital components – such as software, algorithms, communication and storage – and physical components – sensors, actuators, IoT devices or industrial machines. Its main function is to observe the environment, process information and act on it.

Unlike traditional monitoring systems, a CPS is not limited to measuring: it closes a complete loop between perception, decision, and action. This cycle can be understood through three main elements:

Figure 1. Cyber-physical systems cycle. Source: own elaboration

An everyday example that illustrates this complete cycle of perception, decision and action very well is smart irrigation, which is increasingly present in precision agriculture and home gardening systems. In this case, sensors distributed throughout the terrain continuously measure soil moisture, ambient temperature, and even solar radiation. All this information flows to the computing unit, which analyzes the data, compares it with previously defined thresholds or with more complex models – for example, those that estimate the evaporation of water or the water needs of each type of plant – and determines whether irrigation is really necessary.

When the system concludes that the floor has reached a critical level of dryness, the third element of CPS comes into play: the actuators. They are the ones who open the valves, activate the water pump or regulate the flow rate, and they do so for the exact time necessary to return the humidity to optimal levels. If conditions change—if it starts raining, if the temperature drops, or if the soil recovers moisture faster than expected—the system itself adjusts its behavior accordingly.

This whole process happens without human intervention, autonomously. The result is a more sustainable use of water, better cared for plants and a real-time adaptability that is only possible thanks to the integration of sensors, algorithms and actuators characteristic of cyber-physical systems.

CPS as real-time data factories

One of the most relevant characteristics of cyber-physical systems is their ability to generate data continuously, massively and with a very high temporal resolution. This constant production can be seen in many day-to-day situations:

• A hydrological station can record level and flow every minute.
• An urban mobility sensor can generate hundreds of readings per second.
• A smart meter records electricity consumption every few minutes.
• An agricultural sensor measures humidity, salinity, and solar radiation several times a day.
• A mapping drone captures decimetric GPS positions in real time.

Beyond these specific examples, the important thing is to understand what this capability means for the system as a whole: CPS become true data factories, and in many cases come to function as digital twins of the physical environment they monitor. This almost instantaneous equivalence between the real state of a river, a crop, a road or an industrial machine and its digital representation allows us to have an extremely accurate and up-to-date portrait of the physical world, practically at the same time as the phenomena occur.

This wealth of data opens up a huge field of opportunity when published as open information. Data from CPS can drive innovative services developed by companies, fuel high-impact scientific research, empower citizen science initiatives that complement institutional data, and strengthen transparency and accountability in the management of public resources.

However, for all this value to really reach citizens and the reuse community, it is necessary to overcome a series of technical, organisational and quality challenges that determine the final usefulness of open data. Below, we look at what those challenges are and why they are so important in an ecosystem that is increasingly reliant on real-time generated information.

The challenge: from raw data to useful public information

Just because a CPS generates data does not mean that it can be published directly as open data. Before reaching the public and reuse companies, the information needs prior preparation , validation, filtering and documentation. Administrations must ensure that such data is understandable, interoperable and reliable. And along the way, several challenges appear.

One of the first is standardization. Each manufacturer, sensor and system can use different formats, different sample rates or its own structures. If these differences are not harmonized, what we obtain is a mosaic that is difficult to integrate. For data to be interoperable, common models, homogeneous units, coherent structures, and shared standards are needed. Regulations such as INSPIRE or the OGC (Open Geospatial Consortium) and IoT-TS standards are key so that data generated in one city can be understood, without additional transformation, in another administration or by any reuser.

The next big challenge is quality. Sensors can fail, freeze always reporting the same value, generate physically impossible readings, suffer electromagnetic interference or be poorly calibrated for weeks without anyone noticing. If this information is published as is, without a prior review and cleaning process, the open data loses value and can even lead to errors. Validation – with automatic checks and periodic review – is therefore indispensable.

Another critical point is contextualization. An isolated piece of information is meaningless. A "12.5" says nothing if we don't know if it's degrees, liters or decibels. A measurement of "125 ppm" is useless if we do not know what substance is being measured. Even something as seemingly objective as coordinates needs a specific frame of reference. And any environmental or physical data can only be properly interpreted if it is accompanied by the date, time, exact location and conditions of capture. This is all part of metadata, which is essential for third parties to be able to reuse information unambiguously.

It's also critical to address privacy and security. Some CPS can capture information that, directly or indirectly, could be linked to sensitive people, property, or infrastructure. Before publishing the data, it is necessary to apply anonymization processes, aggregation techniques, security controls and impact assessments that guarantee that the open data does not compromise rights or expose critical information.

Finally, there are operational challenges such as refresh rate and robustness of data flow. Although CPS generates information in real time, it is not always appropriate to publish it with the same granularity: sometimes it is necessary to aggregate it, validate temporal consistency or correct values before sharing it. Similarly, for data to be useful in technical analysis or in public services, it must arrive without prolonged interruptions or duplication, which requires a stable infrastructure and monitoring mechanisms.

Quality and traceability principles needed for reliable open data

Once these challenges have been overcome, the publication of data from cyber-physical systems must be based on a series of principles of quality and traceability. Without them, information loses value and, above all, loses trust.

The first is accuracy. The data must faithfully represent the phenomenon it measures. This requires properly calibrated sensors, regular checks, removal of clearly erroneous values, and checking that readings are within physically possible ranges. A sensor that reads 200°C at a weather station or a meter that records the same consumption for 48 hours are signs of a problem that needs to be detected before publication.

The second principle is completeness. A dataset should indicate when there are missing values, time gaps, or periods when a sensor has been disconnected. Hiding these gaps can lead to wrong conclusions, especially in scientific analyses or in predictive models that depend on the continuity of the time series.

The third key element is traceability, i.e. the ability to reconstruct the history of the data. Knowing which sensor generated it, where it is installed, what transformations it has undergone, when it was captured or if it went through a cleaning process allows us to evaluate its quality and reliability. Without traceability, trust erodes and data loses value as evidence.

Proper updating is another fundamental principle. The frequency with which information is published must be adapted to the phenomenon measured. Air pollution levels may need updates every few minutes; urban traffic, every second; hydrology, every minute or every hour depending on the type of station; and meteorological data, with variable frequencies. Posting too quickly can generate noise; too slow, it can render the data useless for certain uses.

The last principle is that of rich metadata. Metadata explains the data: what it measures, how it is measured, with what unit, how accurate the sensor is, what its operating range is, where it is located, what limitations the measurement has and what this information is generated for. They are not a footnote, but the piece that allows any reuser to understand the context and reliability of the dataset. With good documentation, reuse isn't just possible: it skyrockets.

Examples: CPS that reuses public data to be smarter

In addition to generating data, many cyber-physical systems also consume public data to improve their performance. This feedback makes open data a central resource for the functioning of smart territories. When a CPS integrates information from its own sensors with external open sources, its anticipation, efficiency, and accuracy capabilities are dramatically increased.

Precision agriculture: In agriculture, sensors installed in the field allow variables such as soil moisture, temperature or solar radiation to be measured. However, smart irrigation systems do not rely solely on this local information: they also incorporate weather forecasts from AEMET, open IGN maps  on slope or soil types, and climate models published as public data. By combining their own measurements with these external sources, agricultural CPS can determine much more accurately which areas of the land need water, when to plant, and how much moisture should be maintained in each crop. This fine management allows water and fertilizer savings that, in some cases, exceed 30%.

Water management: Something similar happens in water management. A cyber-physical system that controls a dam or irrigation canal needs to know not only what is happening at that moment, but also what may happen in the coming hours or days. For this reason, it integrates its own level sensors with open data on river gauging, rain and snow predictions, and even public information on ecological flows. With this expanded vision, the CPS can anticipate floods, optimize the release of the reservoir, respond better to extreme events or plan irrigation sustainably. In practice, the combination of proprietary and open data translates into safer and more efficient water management.

Impact: innovation, citizen science, and data-driven decisions

The union between cyber-physical systems and open data generates a multiplier effect that is manifested in different areas.

• Business innovation: Companies have fertile ground to develop solutions based on reliable and real-time information. From open data and CPS measurements, smarter mobility applications, water management platforms, energy analysis tools, or predictive systems for agriculture can emerge. Access to public data lowers barriers to entry and allows services to be created without the need for expensive  private datasets, accelerating innovation and the emergence of new business models.
• Citizen science: the combination of SCP and open data also strengthens social participation. Neighbourhood communities, associations or environmental groups can deploy low-cost sensors to complement public data and better understand what is happening in their environment. This gives rise to initiatives that measure noise in school zones, monitor pollution levels in specific neighbourhoods, follow the evolution of biodiversity or build collaborative maps that enrich official information.
• Better public decision-making: finally, public managers benefit from this strengthened data ecosystem. The availability of reliable and up-to-date measurements makes it possible to design low-emission zones, plan urban transport more effectively, optimise irrigation networks, manage drought or flood situations or regulate energy policies based on real indicators. Without open data that complements and contextualizes the information generated by the CPS, these decisions would be less transparent and, above all, less defensible to the public.

In short, cyber-physical systems have become an essential piece of understanding and managing the world around us. Thanks to them, we can measure phenomena in real time, anticipate changes and act in a precise and automated way. But its true potential unfolds when its data is integrated into a quality open data ecosystem, capable of providing context, enriching decisions and multiplying uses.

The combination of SPC and open data allows us to move towards smarter territories, more efficient public services and more informed citizen participation. It provides economic value, drives innovation, facilitates research and improves decision-making in areas as diverse as mobility, water, energy or agriculture.

For all this to be possible, it is essential to guarantee the quality, traceability and standardization of the published data, as well as to protect privacy and ensure the robustness of information flows. When these foundations are well established, CPS not only measure the world: they help it improve, becoming a solid bridge between physical reality and shared knowledge.

Content created by Dr. Fernando Gualo, Professor at UCLM and Government and Data Quality Consultant. The content and views expressed in this publication are the sole responsibility of the author.

The European open data portal has published the third volume of its Use Case Observatory, a report that compiles the evolution of data reuse projects across Europe. This initiative highlights the progress made in four areas: economic, governmental, social and environmental impact.

The closure of a three-year investigation

Between 2022 and 2025, the European Open Data Portal has systematically monitored the evolution of various European projects. The research began with an initial selection of 30 representative initiatives, which were analyzed in depth to identify their potential for impact.

After two years, 13 projects continued in the study, including three Spanish ones: Planttes, Tangible Data and UniversiDATA-Lab. Its development over time was studied to understand how the reuse of open data can generate real and sustainable benefits.

The publication of volume III in October 2025 marks the closure of this series of reports, following volume I (2022) and volume II (2024). This last document offers a longitudinal view, showing how the projects have matured in three years of observation and what concrete impacts they have generated in their respective contexts.

Common conclusions

This third and final report compiles a number of key findings:

Economic impact

Open data drives growth and efficiency across industries. They contribute to job creation, both directly and indirectly, facilitate smarter recruitment processes and stimulate innovation in areas such as urban planning and digital services.

The report shows the example of:

•  Naar Jobs (Belgium): an application for job search close to users' homes and focused on the available transport options.

This application demonstrates how open data can become a driver for regional employment and business development.

Government impact

The opening of data strengthens transparency, accountability and citizen participation.

Two use cases analysed belong to this field:

• Waar is mijn stemlokaal? (Netherlands): platform for the search for polling stations.
• Statsregnskapet.no (Norway): website to visualize government revenues and expenditures.

Both examples show how access to public information empowers citizens, enriches the work of the media, and supports evidence-based policymaking. All of this helps to strengthen democratic processes and trust in institutions.

Social impact

Open data promotes inclusion, collaboration, and well-being.

The following initiatives analysed belong to this field:

• UniversiDATA-Lab (Spain): university data repository that facilitates analytical applications.
• VisImE-360 (Italy): a tool to map visual impairment and guide health resources.
• Tangible Data (Spain): a company focused on making physical sculptures that turn data into accessible experiences.
• EU Twinnings (Netherlands): platform that compares European regions to find "twin cities"
• Open Food Facts (France): collaborative database on food products.
• Integreat (Germany): application that centralizes public information to support the integration of migrants.

All of them show how data-driven solutions can amplify the voice of vulnerable groups, improve health outcomes and open up new educational opportunities. Even the smallest effects, such as improvement in a single person's life, can prove significant and long-lasting.

Environmental impact

Open data acts as a powerful enabler of sustainability.

As with environmental impact, in this area we find a large number of use cases:

• Digital Forest Dryads (Estonia): a project that uses data to monitor forests and promote their conservation.
• Air Quality in Cyprus (Cyprus): platform that reports on air quality and supports environmental policies.
• Planttes (Spain): citizen science app that helps people with pollen allergies by tracking plant phenology.
• Environ-Mate (Ireland): a tool that promotes sustainable habits and ecological awareness.

These initiatives highlight how data reuse contributes to raising awareness, driving behavioural change and enabling targeted interventions to protect ecosystems and strengthen climate resilience.

Volume III also points to common challenges: the need for sustainable financing, the importance of combining institutional data with citizen-generated data, and the desirability of involving end-users throughout the project lifecycle. In addition, it underlines the importance of European collaboration and transnational interoperability to scale impact.

Overall, the report reinforces the relevance of continuing to invest in open data ecosystems as a key tool to address societal challenges and promote inclusive transformation.

The impact of Spanish projects on the reuse of open data

As we have mentioned, three of the use cases analysed in the Use Case Observatory have a Spanish stamp. These initiatives stand out for their ability to combine technological innovation with social and environmental impact, and highlight Spain 's relevance within the European open data ecosystem. His career demonstrates how our country actively contributes to transforming data into solutions that improve people's lives and reinforce sustainability and inclusion. Below, we zoom in on what the report says about them.

Planks

This citizen science initiative helps people with pollen allergies through real-time information about allergenic plants in bloom. Since its appearance in Volume I of the Use Case Observatory,  it has evolved as a participatory platform in which users contribute photos and phenological data to create a personalized risk map. This participatory model has made it possible to maintain a constant flow of information validated by researchers and to offer increasingly complete maps. With more than 1,000 initial downloads and about 65,000 annual visitors to its website, it is a useful tool for people with allergies, educators and researchers.

The project has strengthened its digital presence, with increasing visibility thanks to the support of institutions such as the Autonomous University of Barcelona and the University of Granada, in addition to the promotion carried out by the company Thigis.

Its challenges include expanding geographical coverage beyond Catalonia and Granada and sustaining data participation and validation. Therefore, looking to the future, it seeks to extend its territorial reach, strengthen collaboration with schools and communities, integrate more data in real time and improve its predictive capabilities.

Throughout this time, Planttes has established herself as an example of how citizen-driven science can improve public health and environmental awareness, demonstrating the value of citizen science in environmental education, allergy management, and climate change monitoring.

Tangible data

The project transforms datasets into physical sculptures that represent global challenges such as climate change or poverty, integrating QR codes and NFC to contextualize the information. Recognized at the EU Open Data Days 2025, Tangible Data has inaugurated its installation Tangible climate at the National Museum of Natural Sciences in Madrid.

Tangible Data has evolved in three years from a prototype project based on 3D sculptures to visualize sustainability data to become an educational and cultural platform that connects open data with society. Volume III of the Use Case Observatory reflects its expansion into schools and museums, the creation of an educational program for 15-year-old students, and the development of interactive experiences with artificial intelligence, consolidating its commitment to accessibility and social impact.

Its challenges include funding and scaling up the education programme, while its future goals include scaling up school activities, displaying large-format sculptures in public spaces,  and strengthening collaboration with artists and museums. Overall, it remains true to its mission of making data tangible, inclusive, and actionable.

UniversiDATA-Lab

UniversiDATA-Lab is a dynamic repository of analytical applications based on open data from Spanish universities, created in 2020 as a public-private collaboration and currently made up of six institutions. Its unified infrastructure facilitates the publication and reuse of data in standardized formats, reducing barriers and allowing students, researchers, companies and citizens to access useful information for education, research and decision-making.

Over the past three years, the project has grown from a prototype to a consolidated platform, with active applications such as the budget and retirement viewer, and a hiring viewer in beta. In addition, it organizes a periodic datathon that promotes innovation and projects with social impact.

Its challenges include internal resistance at some universities and the complex anonymization of sensitive data, although it has responded with robust protocols and a focus on transparency. Looking to the future, it seeks to expand its catalogue, add new universities and launch applications on emerging issues such as school dropouts, teacher diversity or sustainability, aspiring to become a European benchmark in the reuse of open data in higher education.

Conclusion

In conclusion, the third volume of the Use Case Observatory confirms that open data has established itself as a key tool to boost innovation, transparency and sustainability in Europe. The projects analysed – and in particular the Spanish initiatives Planttes, Tangible Data and UniversiDATA-Lab – demonstrate that the reuse of public information can translate into concrete benefits for citizens, education, research and the environment.

embalses.info is a web platform that provides up-to-date information on the status of Spain’s reservoirs and dams. The application offers real-time hydrological data with weekly updates, allowing citizens, researchers, and public managers to consult water levels, capacities, and historical trends for more than 400 reservoirs organized into 16 river basins.

The application includes an interactive dashboard showing the overall status of Spanish reservoirs, an interactive (coming soon) basin map with filling levels, and detailed pages for each reservoir with weekly trend charts, comparisons with previous years, and historical records dating back to the 1980s. It features a powerful search engine, data analysis with interactive charts, and a contact form for suggestions.

From a technical standpoint, the platform uses Next.js 14+ with TypeScript on the frontend, Prisma ORM for data access, and PostgreSQL/SQL Server as the database. It is SEO-optimized with a dynamic XML sitemap, optimized meta tags, structured data, and friendly URLs. The site is fully responsive, accessible, and includes automatic light/dark mode.

The public value of the application lies in providing transparency and accessible information on Spain’s water resources, enabling farmers, public administrations, researchers, and the media to make informed decisions based on reliable and up-to-date data.

In any data management environment (companies, public administration, consortia, research projects), having data is not enough: if you don't know what data you have, where it is, what it means, who maintains it, with what quality, when it changed or how it relates to other data, then the value is very limited. Metadata —data about data—is essential for:

• Visibility and access: Allow users to find what data exists and can be accessed.

• Contextualization: knowing what the data means (definitions, units, semantics).

• Traceability/lineage: Understanding where data comes from and how it has been transformed.

• Governance and control: knowing who is responsible, what policies apply, permissions, versions, obsolescence.

• Quality, integrity, and consistency: Ensuring data reliability through rules, metrics, and monitoring.

• Interoperability:  ensuring that different systems or domains can share data, using a common vocabulary, shared definitions, and explicit relationships.

In short, metadata is the lever that turns "siloed" data into a governed information ecosystem. As data grows in volume, diversity, and velocity, its function goes beyond simple description: metadata adds context, allows data to be interpreted, and makes  it findable, accessible, interoperable, and reusable (FAIR).

In the new context driven by artificial intelligence, this metadata layer becomes even more relevant, as it provides the provenance information needed to ensure traceability, reliability, and reproducibility of results. For this reason, some recent frameworks extend these principles to FAIR-R, where the additional "R" highlights the importance of data being AI-ready, i.e. that it meets a series of technical, structural and quality requirements that optimize its use by artificial intelligence algorithms.

Thus, we are talking about enriched metadata, capable of connecting technical, semantic and contextual information to enhance machine learning, interoperability between domains and the generation of verifiable knowledge.

From traditional metadata to "rich metadata"

Traditional metadata

In the context of this article, when we talk about metadata with a traditional use, we think of catalogs, dictionaries, glossaries, database data models, and rigid structures (tables and columns). The most common types of metadata are:

• Technical metadata: column type, length, format, foreign keys, indexes, physical locations.

• Business/Semantic Metadata: Field Name, Description, Value Domain, Business Rules, Business Glossary Terms.

• Operational/execution metadata: refresh rate, last load, processing times, usage statistics.

• Quality metadata: percentage of null values, duplicates, validations.

• Security/access metadata: access policies, permissions, sensitivity rating.

• Lineage metadata: Transformation tracing in data pipelines .

This metadata is usually stored in repositories or cataloguing tools, often with tabular structures or relational bases, with predefined links.

Why "rich metadata"?

Rich metadata is a layer that not only describes attributes, but also:

• They discover and infer implicit relationships, identifying links that are not expressly defined in data schemas. This allows, for example, to recognize that two variables with different names in different systems actually represent the same concept ("altitude" and "elevation"), or that certain attributes maintain a hierarchical relationship ("municipality" belongs to "province").
• They facilitate semantic queries and automated reasoning, allowing users and machines to explore relationships and patterns that are not explicitly defined in databases. Rather than simply looking for exact matches of names or structures, rich metadata allows you to ask questions based on meaning and context. For example, automatically identifying all datasets related to "coastal cities" even if the term does not appear verbatim in the metadata.
• They adapt and evolve flexibly, as they can be extended with new entity types, relationships, or domains without the need to redesign the entire catalog structure. This allows new data sources, models or standards to be easily incorporated, ensuring the long-term sustainability of the system.
• They incorporate automation into tasks that were previously manual or repetitive, such as duplication detection, automatic matching of equivalent concepts, or semantic enrichment using machine learning. They can also identify inconsistencies or anomalies, improving the quality and consistency of metadata.
• They explicitly integrate the business context, linking each data asset to its operational meaning and its role within organizational processes. To do this, they use controlled vocabularies, ontologies or taxonomies that facilitate a common understanding between technical teams, analysts and business managers.
• They promote deeper interoperability between heterogeneous domains, which goes beyond the syntactic exchange facilitated by traditional metadata. Rich metadata adds a semantic layer that allows you to understand and relate data based on its meaning, not just its format. Thus, data from different sources or sectors – for example, Geographic Information Systems (GIS), Building Information Modeling (BIM) or the Internet of Things (IoT) – can be linked in a coherent way within a shared conceptual framework. This semantic interoperability is what makes it possible to integrate knowledge and reuse information between different technical and organizational contexts.

This turns metadata into a living asset, enriched and connected to domain knowledge, not just a passive "record".

The Evolution of Metadata: Ontologies and Knowledge Graphs

The incorporation of ontologies and knowledge graphs represents a conceptual evolution in the way metadata is described, related and used, hence we speak of enriched metadata. These tools not only document the data, but connect them within a network of meaning, allowing the relationships between entities, concepts, and contexts to be explicit and computable.

In the current context, marked by the rise of artificial intelligence, this semantic structure takes on a fundamental role:  it provides algorithms with the contextual knowledge necessary to interpret, learn and reason about data in a more accurate and transparent way. Ontologies and graphs allow AI systems not only to process information, but also  to understand the relationships between elements and to generate grounded inferences, opening the way to more explanatory and reliable models.

This paradigm shift transforms metadata into a dynamic structure, capable of reflecting the complexity of knowledge and facilitating semantic interoperability between different domains and sources of information. To understand this evolution, it is necessary to define and relate some concepts:

Ontologies

In the world of data, an ontology is a highly organized conceptual map that clearly defines:

• What entities exist (e.g., city, river, road).
• What properties they have (e.g. a city has a name, town, zip code).
• How they relate to each other (e.g. a river runs through a city, a road connects two municipalities).

The goal is for people and machines to share the same vocabulary and understand data in the same way. Ontologies allow:

• Define concepts and relationships: for example, "a plot belongs to a municipality", "a building has geographical coordinates".
• Set rules and restrictions: such as "each building must be exactly on a cadastral plot".
• Unify vocabularies: if in one system you say "plot" and in another "cadastral unit", ontology helps to recognize that they are analogous.
• Make inferences: from simple data, discover new knowledge (if a building is on a plot and the plot in Seville, it can be inferred that the building is in Seville).
• Establish a common language: they work as a dictionary shared between different systems or domains (GIS, BIM, IoT, cadastre, urban planning).

In short: an ontology is the dictionary and the rules of the game that allow different geospatial systems (maps, cadastre, sensors, BIM, etc.) to understand each other and work in an integrated way.

Knowledge Graphs

A knowledge graph is a way of organizing information as if it were a network of concepts connected to each other.

• Nodes represent things or entities, such as a city, a river, or a building.

• The edges (lines) show the relationships between them, for example: "is in", "crosses" or "belongs to".

• Unlike a simple drawing of connections, a knowledge graph also explains the meaning of those relationships: it adds semantics.

A knowledge graph combines three main elements:

1. Data: specific cases or instances, such as "Seville", "Guadalquivir River" or "Seville City Hall Building".

2. Semantics (or ontology): the rules and vocabularies that define what kinds of things exist (cities, rivers, buildings) and how they can relate to each other.

3. Reasoning: the ability to discover new connections from existing ones (for example, if a river crosses a city and that city is in Spain, the system can deduce that the river is in Spain).

In addition, knowledge graphs make it possible to connect information from different fields (e.g. data on people, places and companies) under the same common language, facilitating analysis and interoperability between disciplines.

In other words, a knowledge graph is the result of applying an ontology (the data model) to several individual datasets (spatial elements, other territory data, patient records or catalog products, etc.). Knowledge graphs are ideal for integrating heterogeneous data, because they do not require a previously complete rigid schema: they can be grown flexibly. In addition, they allow semantic queries and navigation with complex relationships. Here's an example for spatial data to understand the differences:

Use Cases

To better understand the value of smart metadata and semantic catalogs, there is nothing better than looking at examples where they are already being applied. These cases show how the combination of ontologies and knowledge graphs makes it possible to connect dispersed information, improve interoperability and generate actionable knowledge in different contexts.

From emergency management to urban planning or environmental protection, different international projects have shown that semantics is not just theory, but a practical tool that transforms data into decisions.

Some relevant examples include:

• LinkedGeoData that converted OpenStreetMap data into Linked Data, linking it to other open sources.
• Virtual Singapore is a 3D digital twin that integrates geospatial, urban and real-time data for simulation and planning.
• JedAI-spatial is a tool for interconnecting 3D spatial data using semantic relationships.
• SOSA Ontology, a standard widely used in sensor and IoT projects for environmental observations with a geospatial component.
• European projects on digital building permits (e.g. ACCORD), which combine semantic catalogs, BIM models, and GIS data to automatically validate building regulations.

Conclusions

The evolution towards rich metadata, supported by ontologies, knowledge graphs and FAIR-R principles, represents a substantial change in the way data is managed, connected and understood. This new approach makes metadata an active component of the digital infrastructure, capable of providing context, traceability and meaning, and not just describing information.

Rich metadata allows you to learn from data, improve semantic interoperability between domains, and facilitate more expressive queries, where relationships and dependencies can be discovered in an automated way. In this way, they favor the integration of dispersed information and support both informed decision-making and the development of more explanatory and reliable artificial intelligence models.

In the field of open data, these advances drive the transition from descriptive repositories to ecosystems of interconnected knowledge, where data can be combined and reused in a flexible and verifiable way. The incorporation of semantic context and provenance reinforces transparency, quality and responsible reuse.

This transformation requires, however, a progressive and well-governed approach: it is essential to plan for systems migration, ensure semantic quality, and promote the participation of multidisciplinary communities.

In short, rich metadata is the basis for moving from isolated data to connected and traceable knowledge, a key element for interoperability, sustainability and trust in the data economy.

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

Open data has great potential to transform the way we interact with our cities. As they are available to all citizens, they allow the development of applications and tools that respond to urban challenges such as accessibility, road safety or citizen participation. Facilitating access to this information not only drives innovation, but also contributes to improving the quality of life in urban environments.

This potential becomes even more relevant if we consider the current context. Accelerated urban growth has brought with it new challenges, especially in the area of public health. According to data from the United Nations, it is estimated that by 2050 more than 68% of the world's population will live in cities. Therefore, the design of healthy urban environments is a priority in which open data is consolidated as a key tool: it allows  planning more resilient, inclusive and sustainable cities, putting people's well-being at the center of decisions. In this post, we tell you what healthy urban environments are and how open data can help build and maintain them.

What are Healthy Urban Environments? Uses and examples

Healthy urban environments go beyond simply the absence of pollution or noise. According to the World Health Organization (WHO), these spaces must actively promote healthy lifestyles, facilitate physical activity, encourage social interaction, and ensure equitable access to basic services. As established in the Ministry of Health's "Guide to Planning Healthy Cities", these environments are characterized by three key elements:

• Cities designed for walking: they must be spaces that prioritize pedestrian and cycling mobility, with safe, accessible and comfortable streets that invite active movement.

• Incorporation of nature: they integrate green areas, blue infrastructure and natural elements that improve air quality, regulate urban temperature and offer spaces for recreation and rest.

• Meeting and coexistence spaces: they have areas that facilitate social interaction, reduce isolation and strengthen the community fabric.

The role of open data in healthy urban environments

In this scenario, open data acts as the nervous system of smart cities, providing valuable information on usage patterns, citizen needs, and public policy effectiveness. Specifically, in the field of healthy urban spaces, data from:

• Analysis of physical activity patterns: data on mobility, use of sports facilities and frequentation of green spaces reveal where and when citizens are most active, identifying opportunities to optimize existing infrastructure.

• Environmental quality monitoring: urban sensors that measure air quality, noise levels, and temperature provide real-time information on the health conditions of different urban areas.

• Accessibility assessment: public transport, pedestrian infrastructure and service distribution allow for the identification of barriers to access and the design of more inclusive solutions.

• Informed citizen participation: open data platforms facilitate participatory processes where citizens can contribute local information and collaborate in decision-making.

The Spanish open data ecosystem has solid platforms that feed healthy urban space projects. For example, the Madrid City Council's Open Data Portal offers real-time information on air quality as well as a complete inventory of green areas. Barcelona also publishes data on air quality, including the locations and characteristics of measuring stations.

These portals not only store information, but structure it in a way that developers, researchers and citizens can create innovative applications and services.

Use Cases: Applications That Reuse Open Data

Several projects demonstrate how open data translates into tangible improvements for urban health. On the one hand, we can highlight some applications or digital tools such as:

• AQI Air Quality Index: uses government data to provide real-time information on air quality in different Spanish cities.

• GV Aire: processes official air quality data to generate citizen alerts and recommendations.

• National Air Quality Index: centralizes information from measurement stations throughout the country.

• Valencia Verde: uses municipal data to show the location and characteristics of parks and gardens in Valencia.

On the other hand, there are initiatives that combine multisectoral open data to offer solutions that improve the interaction between cities and citizens. For example:

• Supermanzanas Program: uses maps showing air quality pollution levels  and traffic data available in open formats such as CSV and GeoPackage from Barcelona Open Data and Barcelona City Council to identify streets where reducing road traffic can maximize health benefits, creating safe spaces for pedestrians and cyclists.

• The DataActive platform: seeks to establish an international infrastructure in which researchers, public and private sports entities participate. The topics it addresses include land management, urban planning, sustainability, mobility, air quality and environmental justice. It aims to promote more active, healthy and accessible urban environments through the implementation of strategies based on open data and research.

Data availability is complemented by advanced visualization tools. The Madrid Spatial Data Infrastructure (IDEM) offers geographic viewers specialized in air quality and the National Geographic Institute (IGN) offers the national street map CartoCiudad with information on all cities in Spain.

Effective governance and innovation ecosystem

However, the effectiveness of these initiatives depends on new governance models that integrate multiple actors. To achieve proper coordination between public administrations at different levels, private companies, third sector organizations and citizens, it is essential to have quality open data.

Open data not only powers specific applications but creates an entire ecosystem of innovation. Independent developers, startups, research centers, and citizen organizations use this data to:

• Develop urban health impact studies.

• Create participatory planning tools.

• Generate early warnings about environmental risks.

• Evaluate the effectiveness of public policies.

• Design personalized services according to the needs of different population groups.

Healthy urban spaces projects based on open data generate multiple tangible benefits:

• Efficiency in public management: data makes it possible to optimize the allocation of resources, prioritize interventions and evaluate their real impact on citizen health.

• Innovation and economic development: the open data ecosystem stimulates the creation of innovative startups and services that improve the quality of urban life, as demonstrated by the multiple applications available in datos.gob.es.

• Transparency and participation: the availability of data facilitates citizen control and strengthens democratic decision-making processes.

• Scientific evidence: Urban health data contributes to the development of evidence-based public policies and the advancement of scientific knowledge.

• Replicability: successful solutions can be adapted and replicated in other cities, accelerating the transformation towards healthier urban environments.

In short, the future of our cities depends on our ability to integrate technology, citizen participation and innovative public policies. The examples analyzed demonstrate that open data is not just information; They are the foundation for building urban environments that actively promote health, equity, and sustainability.