aplicaciones

Open SDG Index is a platform for managing and visualising results and progress in the fulfilment of the Sustainable Development Goals and the 2030 Agenda. It allows reporting and systematizing the progress and effort made by public and private entities to meet the SDGs. On the one hand, it facilitates the self-evaluation of this progress and, on the other, it makes the results known to the general public. It is part of the SDG system and allows any entity to incorporate and update its profile. 

The portal includes advanced searches, sectoral and territorial filters and organisation profiles for open consultation. The information is verified by independent agents and presented geolocalized to facilitate comparison between organizations and territories.

For the deployment of the Open SDG Index, a proprietary methodology has been developed in collaboration with the UNDP (United Nations Development Programme). The project won the First Prize for Social Entrepreneurship "La Noria" from the Diputación de Málaga in 2020.

The Smart Agro – Irrigation Recommendations website is a free platform developed by the Cabildo de La Palma as part of the La Palma Smart Island project. It aims to improve the efficiency of water use in local agriculture, especially for banana and avocado crops.

To get a personalized recommendation, the user must select a crop and an area of the island through a drop-down menu or on the map. Instead, the app:

• It provides detailed graphs showing the recent evolution of precipitation and evapotranspiration (ETo) over the past 7 days in the selected area.
• It generates irrigation recommendations adjusted to the municipality and local climatic conditions.

To carry out the calculations, data from the island's network of weather and air quality stations are used, together with a calculation engine that processes the information to generate weekly recommendations. Likewise, the data generated by this engine is integrated into the open data portal, promoting an open innovation ecosystem that feeds back on each other.

CLIMA TERRA is a progressive web application (PWA) that provides real-time environmental information in a clear and accessible way. It allows users to view key parameters such as temperature, relative humidity, wind speed and UV index, based on open meteorological and geospatial data.
The app has been designed with a minimalist and bilingual (Spanish/English) approach, with the aim of bringing open data closer to the public and promoting more informed and sustainable everyday decisions.

  MOVACTIVA is a digital platform developed by the Department of Geography of the Universitat Autònoma de Barcelona, which works as an interactive atlas focused on active mobility, i.e. the transport of people using non-motorised means, such as walking or cycling. The atlas collects information from five Spanish cities: Barcelona, Granada, Madrid, Palma de Mallorca and Valencia.

The project maps five urban indicators that are decisive for active mobility, based on 57 georeferenced variables:

• Urban densities for walkability
• Cyclability
• Presence of green spaces
• Urban vitality
• 15-minute city

The combination of these five elements makes it possible to create an objective and standardised indicator: the Global Active Mobility Indicator. 

In addition, the website also offers information on:

• Micromobility, which includes electric, small and light modes of transport (Personal Mobility Vehicles or PMVs), such as electric bicycles and scooters, hoverboards, segways and monowheels.
• Intermodality, which involves the use of two or more modes of transport within the framework of a single trip.

To bring information closer to users, it has an interactive viewer that allows geographic data to be explored visually, facilitating comparison between cities and promoting a healthier and more sustainable urban approach. The indicators are superimposed on open access base maps such as the PNOA orthophotography (from the IGN) and OpenStreetMap.

Edalitics is a cloud-based analytics service that allows you to connect data, model it, create reports and dashboards without deploying your own infrastructure and without technical knowledge. It is based on EDA (Enterprise Data Analytics), the open source platform  of the company Jortilles and is offered as SaaS (Software as a Service), which reduces technical complexity: the user accesses through a browser, selects their sources and builds visualizations by simply dragging and dropping, or through SQL. 

Edalitics works as a corporate and public data platform: it can connect to databases and web services, and it also supports CSV files that the user uploads to enrich their model. From there, dashboards, KPIs and email alerts are created, and private or public reports are published for different decision profiles, with access control and traceability. It allows you to have unlimited users, which makes it interesting for large organizations with many users. 

It is important to clarify that Edalitics does not incorporate datasets by default, but integrates with APIs or open portals. Organisations such as the Baix Empordà County Council have used Edalitics to deploy their open data catalogues. 

Edalitics offers two modes of use: 

• Cloud version. The platform can be used directly in the cloud, with a tiered pricing model. This version is free for organizations with limited usage. Organizations with higher data usage or volume demands can access a paid version for a monthly fee.
• Installation on own servers (On-Premise). For those organizations that prefer to host Edalitics on their own infrastructure, Jortilles offers:
  • Assistance in installation and configuration, adapting to the customer's environment.
  • Possibility of contracting annual maintenance that includes: direct technical support from the developer team and access to updates and improvements proactively, ensuring the proper functioning and evolution of the platform. 

How many times have you had a dataset in your hands that you needed to analyze, but you've run into errors, inconsistencies, or formatting issues that have caused you to lose hours of work? The reality is that, although we have more data available every day, we do not always have the necessary tools or knowledge to work with it efficiently.

There are several options to address this process. One of them is Open Data Editor, a free and open-source tool that the Open Knowledge Foundation (OKFN) has designed with  the aim of democratizing access to and exploitation of data.

Key features and functionalities

As indicated by OKFN, this application is designed for people who work with tabular data (Excel, Google Sheets, CSV) and who do not know how to program or do not have access to specialized technical tools. Its no-code approach makes it an accessible alternative that focuses specifically on tabular data cleansing and validation.

The tool implements a process known as "data validation," which involves finding errors in datasets and correcting them efficiently. Also, verify that spreadsheets or datasets contain all the information necessary for others to use. Therefore, it also takes into account interoperability, a very relevant value when it comes to dataset reuse.

Beyond guaranteeing reuse, Open Data Editor also ensures privacy and security thanks to its local architecture, i.e. the data remains on the user's device.

Pilot projects: global impact and tangible results

Although it is a very intuitive tool, the organization makes available to the user a free online course  to learn how to get the most out of it. The course is currently in English, but Spanish translation will be available soon.

In addition to the main course, the Open Knowledge Foundation has implemented a "train the trainer" program  that trains people to teach the course locally in different regions of the world. Within the framework of this training programme, pilot projects are being implemented in different sectors and communities. These pilot projects have focused especially on encouraging access to basic training in quality data analysis tools, something that OKFN believes should not be limited by economic or technological barriers.

The documented use cases show diverse applications ranging from human rights organizations to local government institutions, all leveraging the data validation and cleansing capabilities offered by the tool. The educational approach of Open Data Editor goes beyond the simple use of the tool: it is about training in open data and promoting open and accessible knowledge.

Next steps: Integrating artificial intelligence

The results of this first phase have been so promising that the Open Knowledge Foundation has decided to move towards a second stage, this time incorporating artificial intelligence technologies to further expand the capabilities of the tool. The new version, which offers validation-focused AI support and trust-building features, has just been announced and released.

The philosophy behind this AI integration is to maintain the educational character of the tool. Rather than creating a "black box" that simply provides results, the new functionality will explain every step that artificial intelligence takes, allowing users to understand not only what is being done with their data, but also why certain decisions are being made.

This transparent approach to AI is especially important in the context of open and government data, as we explain in this episode of the datos.gob.es podcast. Open Data Editor users will be able to see how AI identifies potential problems, suggests corrections, and validates data quality, becoming a learning tool as well as a practical utility.

Impact on the open data ecosystem

This new functionality will add to the purpose of offering a sustainable and open tool. It is precisely this commitment to open source that makes Open Data Editor adaptable and improved by the global developer community. To do this, they use the Frictionless Framework as a technological basis, which ensures that the standards used are open and widely adopted in the open data ecosystem.

There is no doubt that the tool is especially aligned with government open data principles, providing public administrations with a way to improve the quality of their data publications without requiring significant investments in technical infrastructure or specialized training. For data journalists and civil society organizations, Open Data Editor offers the ability to work with complex datasets more efficiently, allowing them to focus on analysis and interpretation rather than technical data cleansing.

In short, more than a technical tool, Open Data Editor symbolizes a paradigmatic shift towards the democratization of data analysis. Because its impact extends beyond its immediate functionalities, contributing to a broader ecosystem of open and accessible data.

In the usual search for tricks to make our prompts more effective, one of the most popular is the activation of the chain of thought. It consists of posing a multilevel problem and asking the AI system to solve it, but not by giving us the solution all at once, but by making visible step by step the logical line necessary to solve it. This feature is available in both paid and free AI systems, it's all about knowing how to activate it.

Originally, the reasoning string was one of many tests of semantic logic that developers put language models through. However, in 2022, Google Brain researchers demonstrated for the first time that providing examples of chained reasoning in the prompt could unlock greater problem-solving capabilities in models.

From this moment on, little by little, it has positioned itself as a useful technique to obtain better results from use, being very questioned at the same time from a technical point of view. Because what is really striking about this process is that language models do not think in a chain: they are only simulating human reasoning before us.

How to activate the reasoning chain

There are two possible ways to activate this process in the models: from a button provided by the tool itself, as in the case of DeepSeek with the "DeepThink" button that activates the R1 model:

Figure 1. DeepSeek with the "DeepThink" button that activates the R1 model.

Or, and this is the simplest and most common option, from the prompt itself. If we opt for this option, we can do it in two ways: only with the instruction (zero-shot prompting) or by providing solved examples (few-shot prompting).

• Zero-shot prompting: as simple as adding at the end of the prompt an instruction such as "Reason step by step", or "Think before answering". This assures us that the chain of reasoning will be activated and we will see the logical process of the problem visible.

Figure 2. Example of Zero-shot prompting.

• ​Few-shot prompting: if we want a very precise response pattern, it may be interesting to provide some solved question-answer examples. The model sees this demonstration and imitates it as a pattern in a new question.​

​

Figure 3. Example of Few-shot prompting.

Benefits and three practical examples

When we activate the chain of reasoning, we are asking the system to "show" its work in a visible way before our eyes, as if it were solving the problem on a blackboard. Although not completely eliminated, forcing the language model to express the logical steps reduces the possibility of errors, because the model focuses its attention on one step at a time. In addition, in the event of an error, it is much easier for the user of the system to detect it with the naked eye.

When is the chain of reasoning useful? Especially in mathematical calculations, logical problems, puzzles, ethical dilemmas or questions with different stages and jumps (called multi-hop). In the latter, it is practical, especially in those in which you have to handle information from the world that is not directly included in the question.

Let's see some examples in which we apply this technique to a chronological problem, a spatial problem and a probabilistic problem.

• Chronological reasoning

​Let's think about the following prompt:

If Juan was born in October and is 15 years old, how old was he in June of last year?

Figure 5. Example of chronological reasoning.

For this example we have used the GPT-o3 model, available in the Plus version of ChatGPT and specialized in reasoning, so the chain of thought is activated as standard and it is not necessary to do it from the prompt. This model is programmed to give us the information of the time it has taken to solve the problem, in this case 6 seconds. Both the answer and the explanation are correct, and to arrive at them the model has had to incorporate external information such as the order of the months of the year, the knowledge of the current date to propose the temporal anchorage, or the idea that age changes in the month of the birthday, and not at the beginning of the year.

• Spatial reasoning

• A person is facing north. Turn 90 degrees to the right, then 180 degrees to the left. In what direction are you looking now?

  

  Figure 6. Example of spatial reasoning.

  This time we have used the free version of ChatGPT, which uses the GPT-4o model by default (although with limitations), so it is safer to activate the reasoning chain with an indication at the end of the prompt: Reason step by step. To solve this problem, the model needs general knowledge of the world that it has learned in training, such as the spatial orientation of the cardinal points, the degrees of rotation, laterality and the basic logic of movement.

• Probabilistic reasoning

• In a bag there are 3 red balls, 2 green balls and 1 blue ball. If you draw a ball at random without looking, what's the probability that it's neither red nor blue?

  

  Figure 7. Example of probabilistic reasoning.

  To launch this prompt we have used Gemini 2.5 Flash, in the Gemini Pro version of Google. The training of this model was certainly included in the fundamentals of both basic arithmetic and probability, but the most effective for the model to learn to solve this type of exercise are the millions of solved examples it has seen. Probability problems and their step-by-step solutions are the model to imitate when reconstructing this reasoning.

  The Great Simulation

  And now, let's go with the questioning. In recent months, the debate about whether or not we can trust these mock explanations has grown, especially since, ideally, the chain of thought should faithfully reflect the internal process by which the model arrives at its answer. And there is no practical guarantee that this will be the case.

  The Anthropic team (creators of Claude, another great language model) has carried out a trap experiment with Claude Sonnet in 2025, to which they suggested a key clue for the solution before activating the reasoned response.

  Think of it like passing a student a note that says "the answer is [A]" before an exam. If you write on your exam that you chose [A] at least in part because of the grade, that's good news: you're being honest and faithful. But if you write down what claims to be your reasoning process without mentioning the note, we might have a problem.

  The percentage of times Claude Sonnet included the track among his deductions was only 25%. This shows that sometimes models generate explanations that sound convincing, but that do not correspond to their true internal logic to arrive at the solution, but are rationalizations a posteriori: first they find the solution, then they invent the process in a coherent way for the user. This shows the risk that the model may be hiding steps or relevant information for the resolution of the problem.

  Closing

  Despite the limitations exposed, as we see in the study mentioned above, we cannot forget that in the original Google Brain research, it was documented that, when applying the reasoning chain, the PaLM model improved its performance in mathematical problems from 17.9% to 58.1% accuracy. If, in addition, we combine this technique with the search in open data to obtain information external to the model, the reasoning improves in terms of being more verifiable, updated and robust.

  However, by making language models "think out loud", what we are really improving in 100% of cases is the user experience in complex tasks. If we do not fall into the excessive delegation of thought to AI, our own cognitive process can benefit. It is also a technique that greatly facilitates our new work as supervisors of automatic processes.

Content prepared by Carmen Torrijos, expert in AI applied to language and communication. The contents and points of view reflected in this publication are the sole responsibility of the author.

Generative artificial intelligence is beginning to find its way into everyday applications ranging from virtual agents (or teams of virtual agents) that resolve queries when we call a customer service centre to intelligent assistants that automatically draft meeting summaries or report proposals in office environments.

These applications, often governed by foundational language models (LLMs), promise to revolutionise entire industries on the basis of huge productivity gains. However, their adoption brings new challenges because, unlike traditional software, a generative AI model does not follow fixed rules written by humans, but its responses are based on statistical patterns learned from processing large volumes of data. This makes its behaviour less predictable and more difficult to explain, and sometimes leads to unexpected results, errors that are difficult to foresee, or responses that do not always align with the original intentions of the system's creator.

Therefore, the validation of these applications from multiple perspectives such as ethics, security or consistency is essential to ensure confidence in the results of the systems we are creating in this new stage of digital transformation.

What needs to be validated in generative AI-based systems?

Validating generative AI-based systems means rigorously checking that they meet certain quality and accountability criteria before relying on them to solve sensitive tasks.

It is not only about verifying that they ‘work’, but also about making sure that they behave as expected, avoiding biases, protecting users, maintaining their stability over time, and complying with applicable ethical and legal standards. The need for comprehensive validation is a growing consensus among experts, researchers, regulators and industry: deploying AI reliably requires explicit standards, assessments and controls.

We summarize four key dimensions that need to be checked in generative AI-based systems to align their results with human expectations:

• Ethics and fairness: a model must respect basic ethical principles and avoid harming individuals or groups. This involves detecting and mitigating biases in their responses so as not to perpetuate stereotypes or discrimination. It also requires filtering toxic or offensive content that could harm users. Equity is assessed by ensuring that the system offers consistent treatment to different demographics, without unduly favouring or excluding anyone.
• Security and robustness: here we refer to both user safety (that the system does not generate dangerous recommendations or facilitate illicit activities) and technical robustness against errors and manipulations. A safe model must avoid instructions that lead, for example, to illegal behavior, reliably rejecting those requests. In addition, robustness means that the system can withstand adversarial attacks (such as requests designed to deceive you) and that it operates stably under different conditions.
• Consistency and reliability: Generative AI results must be consistent, consistent, and correct. In applications such as medical diagnosis or legal assistance, it is not enough for the answer to sound convincing; it must be true and accurate. For this reason, aspects such as the logical coherence of the answers, their relevance with respect to the question asked and the factual accuracy of the information are validated. Its stability over time is also checked (that in the face of two similar requests equivalent results are offered under the same conditions) and its resilience (that small changes in the input do not cause substantially different outputs).
• Transparency and explainability: To trust the decisions of an AI-based system, it is desirable to understand how and why it produces them. Transparency includes providing information about training data, known limitations, and model performance across different tests. Many companies are adopting the practice of publishing "model cards," which summarize how a system was designed and evaluated, including bias metrics, common errors, and recommended use cases. Explainability goes a step further and seeks to ensure that the model offers, when possible, understandable explanations of its results (for example, highlighting which data influenced a certain recommendation). Greater transparency and explainability increase accountability, allowing developers and third parties to audit the behavior of the system.

Open data: transparency and more diverse evidence

Properly validating AI models and systems, particularly in terms of fairness and robustness, requires representative and diverse datasets that reflect the reality of different populations and scenarios.

On the other hand, if only the companies that own a system have data to test it, we have to rely on their own internal evaluations. However, when open datasets and public testing standards exist, the community (universities, regulators, independent developers, etc.) can test the systems autonomously, thus functioning as an independent counterweight that serves the interests of society.

A concrete example was given by Meta (Facebook) when it released its Casual Conversations v2 dataset in 2023. It is an open dataset, obtained with informed consent, that collects videos from people from 7 countries (Brazil, India, Indonesia, Mexico, Vietnam, the Philippines and the USA), with 5,567 participants who provided attributes such as age, gender, language and skin tone.

Meta's objective with the publication was precisely to make it easier for researchers to evaluate the impartiality and robustness of AI systems in vision and voice recognition. By expanding the geographic provenance of the data beyond the U.S., this resource allows you to check if, for example, a facial recognition model works equally well with faces of different ethnicities, or if a voice assistant understands accents from different regions.

The diversity that open data brings also helps to uncover neglected areas in AI assessment. Researchers from  Stanford's Human-Centered Artificial Intelligence (HAI) showed in the HELM (Holistic Evaluation of Language Models) project  that many language models are not evaluated in minority dialects of English or in underrepresented languages, simply because there are no quality data in the  most well-known benchmarks.

The community can identify these gaps and create new test sets to fill them (e.g., an open dataset of FAQs in Swahili to validate the behavior of a  multilingual chatbot). In this sense, HELM has incorporated broader evaluations precisely thanks to the availability of open data, making it possible to measure not only the performance of the models in common tasks, but also their behavior in other linguistic, cultural and social contexts. This has contributed to making visible the current limitations of the models and to promoting the development of more inclusive and representative systems of the real world or models more adapted to the specific needs of local contexts, as is the case of the ALIA foundational model, developed in Spain.

In short, open data contributes to democratizing the ability to audit AI systems, preventing the power of validation from residing only in a few. They allow you to reduce costs and barriers as a small development team can test your model with open sets without having to invest great efforts in collecting their own data. This not only fosters innovation, but also ensures that local AI solutions from small businesses are also subject to rigorous validation standards.

The validation of applications based on generative AI is today an unquestionable necessity to ensure that these tools operate in tune with our values and expectations. It is not a trivial process, it requires new methodologies, innovative metrics and, above all, a culture of responsibility around AI. But the benefits are clear, a rigorously validated AI system will be more trustworthy, both for the individual user who, for example, interacts with a chatbot without fear of receiving a toxic response, and for society as a whole who can accept decisions based on these technologies knowing that they have been properly audited. And open data helps to cement this trust by fostering transparency, enriching evidence with diversity, and involving the entire community in the validation of AI systems.

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

Imagine you want to know how many terraces there are in your neighbourhood, how the pollen levels in the air you breathe every day are evolving or whether recycling in your city is working well. All this information exists in your municipality's databases, but it sits in spreadsheets and technical documents that only experts know how to interpret.

This is where open data visualisation initiativescome in: they transform those seemingly cold numbers into stories that anyone can understand at a glance.  A colourful graph showing the evolution of traffic on your street, an interactive map showing the green areas of your city, or an infographic explaining how the municipal budget is spent. These tools make public information accessible, useful and, moreover, comprehensible to all citizens.

Moreover, the advantages of this type of solution are not only for the citizens, but also benefit the Administration that carries out the exercise, because it allows:

• Detect and correct data errors.
• Add new sets to the portal.
• Reduce the number of questions from citizens.
• Generate more trust on the part of society.

Therefore, visualising open data brings government closer to citizens, facilitates informed decision-making, helps public administrations to improve their open data offer and creates a more participatory society where we can all better understand how the public sector works. In this post, we present some examples of open data visualisation initiatives in regional and municipal open data portals.

Visualiza Madrid: bringing data closer to the public

Madrid City Council's open data portal has developed the initiative "Visualiza Madrid", a project born with the specific objective of making open data and its potential reach the general public , transcending specialised technical profiles. As Ascensión Hidalgo Bellota, Deputy Director General for Transparency of Madrid City Council, explained during the IV National Meeting on Open Data, "this initiative responds to the need to democratise access to public information".

Visualiza Madrid currently has 29 visualisations that cover different topics of interest to citizens, from information on hotel and restaurant terraces to waste management and urban traffic analysis. This thematic diversity demonstrates the versatility of visualisations as a tool for communicating information from very diverse sectors of public administration.

 In addition, the initiative has received external recognition this year through the Audaz 2,025 Awards, an initiative of the Spanish chapter of the Open Government Academic Network (RAGA Spain).The initiative has also received external recognition through the Audaz 2,025 Awards.

Castilla y León: comprehensive analysis of regional data

 The Junta de Castilla y León has also developed a portal specialised in analysis and visualisations that stands out for its comprehensive approach to the presentation of regional data. Its visualisation platform offers a systematic approach to the analysis of regional information, allowing users to explore different dimensions of the reality of Castilla y Leónthrough interactive and dynamic tools.

This initiative allows complex information to be presented in a structured and understandable way, facilitating both academic analysis and citizen use of the data. The platform integrates different sources of regional information, creating a coherent ecosystem of visualisations that provides a panoramic view of different aspects of regional management. Among the topics it offers are data on tourism, the labour market and budget execution. All the visualisations are made with open data sets from the regional portal of Castilla y León .

The Castilla y León approach demonstrates how visualisations can serve as a tool for territorial analysis, providing valuable insights on economic, social and demographic dynamics that are fundamental for the planning and evaluation of regional public policies.

Canary Islands: technological integration with interactive widgets .

On the other hand, the Government of the Canary Islands has opted for an innovative strategy through the implementation of widgets that allow the integration of open data visualisations of the Instituto Canario de Estadística (ISTAC) in different platforms and contexts. This technological approach represents a qualitative leap in the distribution and reuse of public data visualisations.

The widgets developed by the Canary Islands make it easier for third parties to embed official visualisations in their own applications, websites or analyses, exponentially expanding the scope and usefulness of Canary Islands open data. This strategy not only multiplies the points of access to public information, but also fosters the creation of a collaborative ecosystem where different actors can benefit from and contribute to the value of open data.

The Canarian initiative illustrates how technology can be used to create scalable and flexible solutions that maximise the impact of investments in open data visualisation, establishing a replicable model for other administrations seeking to amplify the reach of their transparency initiatives.

Lessons learned and best practices

By way of example, the cases analysed reveal common patterns that can serve as a guide for future initiatives. The orientation towards the general public, beyond specialised technical users, emerges as an opportunity factor for the success of these platforms. To maintain the interest and relevance of the visualisations, it is important to offer thematic diversity and to update the data regularly.

Technological integration and interoperability, as demonstrated in the case of the Canary Islands, open up new possibilities to maximise the impact of public investments in data visualisation. Likewise, external recognition and participation in professional networks, as evidenced in the case of Madrid, contribute to continuous improvement and the exchange of best practices between administrations.

In general terms, open data visualisation initiatives represent a very valuable opportunity in the transparency and open government strategy of Spanish public administrations. The cases of Madrid, Castilla y León, as well as the Canary Islands, are examples of the enormous potential for transforming public data into tools for citizen empowerment and improved public management.

The success of these initiatives lies in their ability to connect government information with the real needs of citizens, creating bridges of understanding that strengthen the relationship between administration and society. As these experiences mature and consolidate, it will be crucial to keep the focus on the usability, accessibility and relevance of visualisations, ensuring that open data truly delivers on its promise to contribute to a more informed, participatory and democratic society.

Open data visualisation is not just a technical issue, but a strategic opportunity to redefine public communication and strengthen the foundations of a truly open and transparent administration.

More Cancer Data is an initiative that brings together 24 scientific organisations and patient associations, with the aim of developing an integrated model of knowledge about cancer. This project makes 69 key indicators available to the public, organised into six fundamental dimensions of the cancer journey: health promotion and primary prevention, secondary prevention, early detection, diagnosis, health care, follow-up and end of life.

The main purpose of the application is to promote equity in access to cancer information throughout the country. Through the use of open data, it seeks to improve the accessibility, homogeneity and quality of the information available, thus facilitating more informed decision-making for both professionals and patients.

The indicators have been compiled from primary and official sources, which guarantees the reliability and quality of the data. The sources used include the National Statistics Institute (INE), the information system of the Ministry of Haematology and Haemotherapy (SEHH), the Spanish Society of Palliative Care (SECPAL), as well as other data from the Observatory of the Spanish Association Against Cancer and grey literature. All these resources make it possible to generate a broad and detailed overview of the cancer situation in Spain, which is accessible through this open data portal.

See here the report of the project results and here the methodological information.