casos de uso

Geospatial data has driven improvements in a number of sectors, and energy is no exception. This data allows us to better understand our environment in order to promote sustainability, innovation and informed decision-making.

One of the main providers of open geospatial data is Copernicus, the European Union's Earth observation programme. Through a network of satellites called Sentinel and data from ground, sea and airborne sources, Copernicus provides geospatial information freely accessible through various platforms.

Although Copernicus data is useful in many areas, such as fighting climate change, urban planning or agriculture, in this article we will focus on its role in driving sustainability and energy efficiency. The availability of high quality open data fosters innovation in this sector by promoting the development of new tools and applications that improve energy management and use. Here are some examples.

Climate prediction to improve production

Geospatial data provide detailed information on weather conditions, air quality and other factors, which are essential for understanding and predicting environmental phenomena, such as storms or droughts, that affect energy production and distribution.

One example is this project which provides high-resolution wind forecasts to serve the oil and gas, aviation, shipping and defence sectors. It uses data from satellite observations and numerical models, including information on ocean currents, waves and sea surface temperature from the "Copernicus Marine Service". Thanks to its granularity, it can provide an accurate weather forecasting system at a very local scale, allowing a higher level of accuracy in the behaviour of extreme weather and climate phenomena.

Optimisation of resources

The data provided by Copernicus also allows the identification of the best locations for the installation of energy generation centres, such as solar and wind farms, by facilitating the analysis of factors such as solar radiation and wind speed. In addition, they help monitor the efficiency of these facilities, ensuring that they are operating at maximum capacity.

In this regard, a project has been developed to find the best site for a combined floating wind and wave energy system (i.e. based on wave motion). By obtaining both energies with a single platform, this solution saves space and reduces the impact on the ground, while improving efficiency. Wind and waves arrive at different times at the platform, so capturing both elements helps reduce variability and smoothes overall electricity production. Thanks to the Copernicus data (obtained from the Atlantic Service - Biscay Iberia Ireland - Ocean Wave Reanalysis), the provider of this situation was able to obtain separate components of wind and wave waves, which allowed a more complete understanding of the directionality of both elements. This work led to the selection of Biscay Marine Energy Platform (BiMEP). for the deployment of the device.

Another example is Mon Toit Solaire, an integrated web-based decision support system for the development of rooftop photovoltaic power generation. This tool simulates and calculates the energy potential of a PV project and provides users with reliable technical and financial information. It uses solar radiation data produced by the "Copernicus Atmospheric Monitoring Service", together with three-dimensional urban topographic data and simulations of tax incentives, energy costs and prices, allowing the return on investment to be calculated.

Environmental monitoring and impact assessment.

Geospatial information allows for improved environmental monitoring and accurate impact assessments in the energy sector. This data allows energy companies to identify environmental risks associated with their operations, design strategies to mitigate their impact and optimise their processes towards greater sustainability. In addition, they support environmental compliance by providing objective data-driven reporting, encouraging more responsible and environmentally friendly energy development.

Among the challenges posed by the conservation of ocean biodiversity, man-made underwater noise is recognised as a serious threat and is regulated at European level. In order to assess the impact on marine life of wind farms along the southern coast of France, this project uses high-resolution statistical sound maps, which provide a detailed view of coastal processes, with an hourly time frequency and a high spatial resolution of up to 1.8 km. In particular, they use information from the "Mediterranean Sea Physics Analysis and Forecasting" and "World Ocean Hourly Sea Surface Wind and Stress" services.

Emergency and environmental disaster management.

In disaster situations or extreme weather events, geospatial data can help quickly assess damage and coordinate emergency responses more efficiently.

They can also predict how spills will behave. This is the aim of the Marine Research Institute of the University of Klaipeda, which has developed a system for monitoring and forecasting chemical and microbiological pollution episodes using a high-resolution 3D operational hydrodynamic model. They use the Copernicus "Physical Analysis and Forecasts of the Baltic Sea". The model provides real-time, five-day forecasts of water currents, addressing the challenges posed by shallow waters and port areas. It aims to help manage pollution incidents, particularly in pollution-prone regions such as ports and oil terminals.

These examples highlight the usefulness of geospatial data, especially those provided by programmes such as Copernicus. The fact that companies and institutions can freely access this data is revolutionising the energy sector, contributing to a more efficient, sustainable and resilient system.

Housing is one of the main concerns of Spanish citizens, according to the January 2025 barometer of the Centro de Investigaciones Sociológicas (CIS). In order to know the real situation of access to housing, it is necessary to have public, updated and quality data, which allows all the actors in this ecosystem to carry out analyses and make informed decisions.

In this article we will review some examples of available open data, as well as tools and solutions that have been created based on them to bring this information closer to citizens.

Examples of housing data Open data can have several uses in this sector:

• Enable public bodies to understand citizens' needs and develop policies accordingly.
• Helping individuals to find homes to rent or buy.
• Providing information to builders and companies so that they can develop housing that responds to these needs.

Therefore, in this field, the most used data include those referring to housing, but also to demographic and social aspects, often with a high geospatial component. Some of the most popular datasets in this sense are the Housing and Consumer Price Indexes of the National Statistics Institute (INE) or the Cadastre data.

Different public bodies have made available to the public spaces where they gather various data related to housing. This is the case of Barcelona City Council and its portal "Housing in data", an environment that centralises access to information and data from various sources, including datasets from its open data portal.

Another example is the Madrid City Council data visualisation portal, which includes dashboards with information on the number of residential properties by district or neighbourhood, as well as their cadastral value, with direct access to download the data used.

Further examples of bodies that also provide access to this type of information are the Junta de Castilla y León, the Basque Government or the Comunidad Valenciana. In addition, those who wish to do so can find a multitude of data related to housing in the National Catalogue of Open Data, hosted here, at datos.gob.es.

It should also be noted that it is not only public bodies that open data related to this subject. A few months ago, the real estate portalidealistareleased a dataset with detailed information on thousands of properties in Madrid, Barcelona and Valencia. It is available as a package in R via Github.

Tools and solutions to bring this data closer to citizens

Data such as the above can be reused for multiple purposes, as we showed in previous articles and as we can see in this new approach to the various use cases:

Data journalism

The media use open housing data to provide a more accurate picture of the housing market situation, helping citizens understand the dynamics affecting prices, supply and demand. By accessing data on price developments, housing availability or related public policies, the media can generate reports and infographics that explain in an accessible way the situation and how these factors impact on people's daily lives. These articles provide citizens with relevant information, in a simple way, to make decisions about their housing situation.

One example is this article which allows us to visualise, neighbourhood by neighbourhood, the price of rent and access to housing according to income, for which open data from the Ministry of Housing and Urban Agenda, the Cadastre and the INE, among others, were used. Along the same lines is this article on the percentage of income to be spent on rent.

Reporting and policy development

Open data on housing is used by public bodies such as the Ministry of Housing and Urban Agenda in its Housing and Land Observatory, where electronic statistical bulletins are generated that integrate data available from the main official statistical sources. The aim is to monitor the sector from different perspectives and throughout the different phases of the process (land market, built products, accessibility and financing, etc.). The Ministry of Housing and Urban Agenda also uses data from various sources, such as the Tax Agency, the Cadastre or the INE, for its State Reference System of Housing Rental Prices, which defines ranges of rental price values for housing in areas declared as stressed.

Offer of real estate services

Open data can be valuable for the construction sector: open information on land use and permits is consulted before excavation work is undertaken and new construction starts.

In addition, some of the companies using open data are real estate websites. These portals reuse open data sets to provide users with comparable property prices, neighbourhood crime statistics or proximity to public educational, health and recreational facilities. This is helped, for example, by tools such as Location intelligence, which provides access to census data, rental prices, housing characteristics or urban planning. Public bodies can also help in this field with their own solutions, such as Donde Vivo, from the Government of Aragon, which allows you to obtain an interactive map and related information of the nearest points of interest, educational and health centres as well as geostatistical information of the place where you live.

There are also tools that help to forecast future costs, such as Urban3r, where users can visualise different indicators that help them to know the energy demand data of residential buildings in their current state and after undergoing energy refurbishment, as well as the estimated costs of these interventions.This is a field where data-driven disruptive technologies, such as  artificial intelligence, will play an increasingly important role, optimising processes and facilitating decision-making for both home buyers and suppliers. By analysing large volumes of data, AI can predict market trends, identify areas of high demand or provide personalised recommendations based on the needs of each user. Some companies have already launched chatbots, which answer users' questions, but AI can even help create projects for the development of affordable and sustainable housing.

In short, we are in a field where new technologies are going to make it easier and easier for citizens to find out about the supply of housing, but this supply must be aligned with the needs of users. It is therefore necessary to continue promoting the opening up of quality data, which will help to understand the situation and promote public policies and solutions that facilitate access to housing.

The concept of data commons emerges as a transformative approach to the management and sharing of data that serves collective purposes and as an alternative to the growing number of macrosilos of data for private use. By treating data as a shared resource, data commons facilitate collaboration, innovation and equitable access to data, emphasising the communal value of data above all other considerations. As we navigate the complexities of the digital age - currently marked by rapid advances in artificial intelligence (AI) and the continuing debate about the challenges in data governance- the role that data commons can play is now probably more important than ever.

What are data commons?

The data commons refers to a cooperative framework where data is collected, governed and shared among all community participants through protocols that promote openness, equity, ethical use and sustainability. The data commons differ from traditional data-sharing models mainly in the priority given to collaboration and inclusion over unitary control.

Another common goal of the data commons is the creation of collective knowledge that can be used by anyone for the good of society. This makes them particularly useful in addressing today's major challenges, such as environmental challenges, multilingual interaction, mobility, humanitarian catastrophes, preservation of knowledge or new challenges in health and health care.

In addition, it is also increasingly common for these data sharing initiatives to incorporate all kinds of tools to facilitate data analysis and interpretation , thus democratising not only the ownership of and access to data, but also its use.

For all these reasons, data commons could be considered today as a criticalpublic digital infrastructure for harnessing data and promoting social welfare.

Principles of the data commons

The data commons are built on a number of simple principles that will be key to their proper governance:

• Openness and accessibility: data must be accessible to all authorised persons.
• Ethical governance: balance between inclusion and privacy.
• Sustainability: establish mechanisms for funding and resources to maintain data as a commons over time.
• Collaboration: encourage participants to contribute new data and ideas that enable their use for mutual benefit.
• Trust: relationships based on transparency and credibility between stakeholders.

In addition, if we also want to ensure that the data commons fulfil their role as public domain digital infrastructure, we must guarantee other additional minimum requirements such as: existence of permanent unique identifiers , documented metadata , easy access through application programming interfaces (APIs), portability of the data, data sharing agreements between peers and ability to perform operations on the data.

The important role of the data commons in the age of Artificial Intelligence

AI-driven innovation has exponentially increased the demand for high-quality, diverse data sets  a relatively scarce commodityat a large scale that may lead to bottlenecks in the future development of the technology and, at the same time, makes data commons a very relevant enabler for a more equitable AI. By providing shared datasets governed by ethical principles, data commons help mitigate common risks such as risks, data monopolies and unequal access to the benefits of AI.

Moreover, the current concentration of AI developments also represents a challenge for the public interest. In this context, the data commons hold the key to enable a set of alternative, public and general interest-oriented AI systems and applications, which can contribute to rebalancing this current concentration of power. The aim of these models would be to demonstrate how more democratic, public interest-oriented and purposeful systems can be designed based on public AI governance principles and models.

However, the era of generative AI also presents new challenges for data commons such as, for example and perhaps most prominently, the potential risk of uncontrolled exploitation of shared datasets that could give rise to new ethical challenges due to data misuse and privacy violations.

On the other hand, the lack of transparency regarding the use of the data commons by the AI could also end up demotivating the communities that manage them, putting their continuity at risk. This is due to concerns that in the end their contribution may be benefiting mainly the large technology platforms, without any guarantee of a fairer sharing of the value and impact generated as originally intended".

For all of the above, organisations such as Open Future have been advocating for several years now for Artificial Intelligence to function as a common good, managed and developed as a digital public infrastructure for the benefit of all, avoiding the concentration of power and promoting equity and transparency in both its development and its application.

To this end, they propose a set of principles to guide the governance of the data commons in its application for AI training so as to maximise the value generated for society and minimise the possibilities of potential abuse by commercial interests:

• Share as much data as possible, while maintaining such restrictions as may be necessary to preserve individual and collective rights.
• Be fully transparent and provide all existing documentation on the data, as well as on its use, and clearly distinguish between real and synthetic data.
• Respect decisions made about the use of data by persons who have previously contributed to the creation of the data, either through the transfer of their own data or through the development of new content, including respect for any existing legal framework.
• Protect the common benefit in the use of data and a sustainable use of data in order to ensure proper governance over time, always recognising its relational and collective nature.
• Ensuring the quality of data, which is critical to preserving its value as a common good, especially given the potential risks of contamination associated with its use by AI.
• Establish trusted institutions that are responsible for data governance and facilitate participation by the entire data community, thus going a step beyond the existing models for data intermediaries.

Use cases and applications

There are currently many real-world examples that help illustrate the transformative potential of data commons:

• Health data commons : projects such as the National Institutes of Health's initiative in the United States - NIH Common Fund to analyse and share large biomedical datasets, or the National Cancer Institute's Cancer Research Data Commons , demonstrate how data commons can contribute to the acceleration of health research and innovation.
• AI training and machine learning: the evaluation of AI systems depends on rigorous and standardised test data sets. Initiatives such as OpenML or MLCommons build open, large-scale and diverse datasets, helping the wider community to deliver more accurate and secure AI systems.
• Urban and mobility data commons : cities that take advantage of shared urban data platforms improve decision-making and public services through collective data analysis, as is the case of Barcelona Dades, which in addition to a large repository of open data integrates and disseminates data and analysis on the demographic, economic, social and political evolution of the city. Other initiatives such as OpenStreetMaps itself can also contribute to providing freely accessible geographic data.
• Culture and knowledge preservation: with such relevant initiatives in this field as Mozilla's Common Voice project to preserve and revitalise the world's languages, or Wikidata, which aims to provide structured access to all data from Wikimedia projects, including the popular Wikipedia.

Challenges in the data commons

Despite their promise and potential as a transformative tool for new challenges in the digital age, the data commons also face their own challenges:

• Complexity in governance: Striking the right balance between inclusion, control and privacy can be a delicate task.
• Sustainability: Many of the existing data commons are fighting an ongoing battle to try to secure the funding and resources they need to sustain themselves and ensure their long-term survival.
• Legal and ethical issues: addressing challenges relating to intellectual property rights, data ownership and ethical use remain critical issues that have yet to be fully resolved.
• Interoperability: Ensuring compatibility between datasets and platforms is a persistent technical hurdle in almost any data sharing initiative, and the data commons were to be no exception.

The way forward

To unlock their full potential, the data commons require collective action and a determined commitment to innovation. Key actions include:

• Develop standardised governance models that strike a balance between ethical considerations and technical requirements.
• Apply the principle of reciprocity in the use of data, requiring those who benefit from it to share their results back with the community.
• Protection of sensitive data through anonymisation, preventing data from being used for mass surveillance or discrimination.
• Encourage investment in infrastructure to support scalable and sustainable data exchange.
• Promote awareness of the social benefits of data commons to encourage participation and collaboration.

Policy makers, researchers and civil society organisations should work together to create an ecosystem in which the data commons can thrive, fostering more equitable growth in the digital economy and ensuring that the data commons can benefit all.

Conclusion

The data commons can be a powerful tool for democratising access to data and fostering innovation. In this era defined by AI and digital transformation, they offer us an alternative path to equitable, sustainable and inclusive progress. Addressing its challenges and adopting a collaborative governance approach through cooperation between communities, researchers and regulators will ensure fair and responsible use of data.

This will ensure that data commons become a fundamental pillar of the digital future, including new applications of Artificial Intelligence, and could also serve as a key enabling tool for some of the key actions that are part of the recently announced European competitiveness compass, such as the new Data Union strategy and the AI Gigafactories initiative.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation. The contents and views expressed in this publication are the sole responsibility of the author.

Open data portals are an invaluable source of public information. However, extracting meaningful insights from this data can be challenging for users without advanced technical knowledge.

In this practical exercise, we will explore the development of a web application that democratizes access to this data through the use of artificial intelligence, allowing users to make queries in natural language.

The application, developed using the datos.gob.es portal as a data source, integrates modern technologies such as Streamlit for the user interface and Google's Gemini language model for natural language processing. The modular nature allows any Artificial Intelligence model to be used with minimal changes. The complete project is available in the Github repository.

Access the data laboratory repository on Github.

Run the data preprocessing code on Google Colab.

In this video, the author explains what you will find both on Github and Google Colab.

Application Architecture

The core of the application is based on four main interconnected sections that work together to process user queries:

1. Context Generation
   • Analyzes the characteristics of the chosen dataset.
   • Generates a detailed description including dimensions, data types, and statistics.
   • Creates a structured template with specific guidelines for code generation.
2. Context and Query Combination
   • Combines the generated context with the user's question, creating the prompt that the artificial intelligence model will receive.
3. Response Generation
   • Sends the prompt to the model and obtains the Python code that allows solving the generated question.
4. Code Execution
   • Safely executes the generated code with a retry and automatic correction system.
   • Captures and displays the results in the application frontend.

Figure 1. Request processing flow

Development Process

The first step is to establish a way to access public data. The datos.gob.es portal offers datasets via API. Functions have been developed to navigate the catalog and download these files efficiently.

Figura 2. API de datos.gob

The second step addresses the question: how to convert natural language questions into useful data analysis? This is where Gemini, Google's language model, comes in. However, it's not enough to simply connect the model; it's necessary to teach it to understand the specific context of each dataset.

A three-layer system has been developed:

1. A function that analyzes the dataset and generates a detailed "technical sheet".
2. Another that combines this sheet with the user's question.
3. And a third that translates all this into executable Python code.

You can see in the image below how this process develops and, subsequently, the results of the generated code are shown once executed.

Figure 3. Visualization of the application's response processing

Finally, with Streamlit, a web interface has been built that shows the process and its results to the user. The interface is as simple as choosing a dataset and asking a question, but also powerful enough to display complex visualizations and allow data exploration.

The final result is an application that allows anyone, regardless of their technical knowledge, to perform data analysis and learn about the code executed by the model. For example, a municipal official can ask "What is the average age of the vehicle fleet?" and get a clear visualization of the age distribution.

Figure 4. Complete use case. Visualizing the distribution of registration years of the municipal vehicle fleet of Almendralejo in 2018

What Can You Learn?

This practical exercise allows you to learn:

1. AI Integration in Web Applications:
   • How to communicate effectively with language models like Gemini.
   • Techniques for structuring prompts that generate precise code.
   • Strategies for safely handling and executing AI-generated code.
2. Web Development with Streamlit:
   • Creating interactive interfaces in Python.
   • Managing state and sessions in web applications.
   • Implementing visual components for data.
3. Working with Open Data:
   • Connecting to and consuming public data APIs.
   • Processing Excel files and DataFrames.
   • Data visualization techniques.
4. Development Best Practices:
   • Modular structuring of Python code.
   • Error handling and retries.
   • Implementation of visual feedback systems.
   • Web application deployment using ngrok.

Conclusions and Future

This exercise demonstrates the extraordinary potential of artificial intelligence as a bridge between public data and end users. Through the practical case developed, we have been able to observe how the combination of advanced language models with intuitive interfaces allows us to democratize access to data analysis, transforming natural language queries into meaningful analysis and informative visualizations.

For those interested in expanding the system's capabilities, there are multiple promising directions for its evolution:

• Incorporation of more advanced language models that allow for more sophisticated analysis.
• Implementation of learning systems that improve responses based on user feedback.
• Integration with more open data sources and diverse formats.
• Development of predictive and prescriptive analysis capabilities.

In summary, this exercise not only demonstrates the feasibility of democratizing data analysis through artificial intelligence, but also points to a promising path toward a future where access to and analysis of public data is truly universal. The combination of modern technologies such as Streamlit, language models, and visualization techniques opens up a range of possibilities for organizations and citizens to make the most of the value of open data.

It is possible that our ability to be surprised by new generative artificial intelligence (AI) tools is beginning to wane. The best example is GPT-o1, a new language model with the highest reasoning ability achieved so far, capable of verbalising - something similar to - its own logical processes, but which did not arouse as much enthusiasm at its launch as might have been expected. In contrast to the previous two years, in recent months we have had less of a sense of disruption and have reacted less massively to new developments.

One possible reflection is that we do not need, for now, more intelligence in the models, but to see with our own eyes a landing in concrete uses that make our lives easier: how do I use the power of a language model to consume content faster, to learn something new or to move information from one format to another? Beyond the big general-purpose applications, such as ChatGPT or Copilot, there are free and lesser-known tools that help us think better, and offer AI-based capabilities to discover, understand and share knowledge.

Generate podcasts from a file: NotebookLM

The NotebookLM automated podcasts first arrived in Spain in the summer of 2024 and did raise a significant stir, despite not even being available in Spanish. Following Google's style, the system is simple: simply upload a PDF file as a source to obtain different variations of the content provided by Gemini 2.0 (Google's AI system), such as a summary of the document, a study guide, a timeline or a list of frequently asked questions. In this case, we have used a report on artificial intelligence and democracy published by UNESCO in 2024 as an example.

Figure 1. Different summary options in NotebookLM.

While the study guide is an interesting output, offering a system of questions and answers to memorise and a glossary of terms, the star of NotebookLM is the so-called "audio summary": a completely natural conversational podcast between two synthetic interlocutors who comment in a pleasant way on the content of the PDF.

Figure 2. Audio summary in NotebookLM.

The quality of the content of this podcast still has room for improvement, but it can serve as a first approach to the content of the document, or help us to internalise it more easily from the audio while we take a break from screens, exercise or move around.

The trick: apparently, you can't generate the podcast in Spanish, only in English, but you can try with this prompt: "Make an audio summary of the document in Spanish". It almost always works.

Create visualisations from text: Napkin AI

Napkin offers us something very valuable: creating visualisations, infographics and mind maps from text content. In its free version, the system only asks you to log in with an email address. Once inside, it asks us how we want to enter the text from which we are going to create the visualisations. We can paste it or directly generate with AI an automatic text on any topic.

Figure 3. Starting points in Napkin.ai.

In this case, we will copy and paste an excerpt from the UNESCO report which contains several recommendations for the democratic governance of AI. From the text it receives, Napkin.ai provides illustrations and various types of diagrams. We can find from simpler proposals with text organised in keys and quadrants to others illustrated with drawings and icons.

Figure 4. Proposed scheme in Napkin.ai.

Although they are far from the quality of professional infographics, these visualisations can be useful on a personal and learning level, to illustrate a post in networks, to explain concepts internally to our team or to enrich our own content in the educational field.

The trick: if you click on Stylesin each scheme proposal, you will find more variations of the scheme with different colours and lines. You can also modify the texts by simply clicking on them once you select a visualisation.

Automatic presentations and slides: Gamma

Of all the content formats that AI is capable of generating, slideshows are probably the least successful. Sometimes the designs are not too elaborate, sometimes we don't get the template we want to use to be respected, almost always the texts are too simple. The particularity of Gamma, and what makes it more practical than other options such as Beautiful.ai, is that we can create a presentation directly from a text content that we can paste, generate with AI or upload in a file.

Figure 5. Starting points for Gamma.

If we paste the same text as in the previous example, about UNESCO's recommendations for democratic governance of AI, in the next step Gamma gives us the choice between "free form" or "card by card". In the first option, the system's AI organises the content into slides while preserving the full meaning of each slide. In the second, it proposes that we divide the text to indicate the content we want on each slide.

Figure 6. Text automatically split into slides by Gamma.

We select the second option, and the text is automatically divided into different blocks that will be our future slides. By clicking on "Continue", we are asked to select a base theme. Finally, by clicking on "Generate", the complete presentation is automatically created.

Figure 7. Example of a slide created with Gamma.

Gamma accompanies the slides with AI-created images that keep a certain coherence with the content, and gives us the option of modifying the texts or generating different images. Once ready, we can export it directly to Power Point format.

A trick: in the "edit with AI" button on each slide we can ask it to automatically translate it into another language, correct the spelling or even convert the text into a timeline.

Summarise from any format: NoteGPT

The aim of NoteGPT is very clear: to summarise content that we can import from many different sources. We can copy and paste a text, upload a file or an image, or directly extract the information from a link, something very useful and not so common in AI tools. Although the latter option does not always work well, it is one of the few tools that offer it.

Figure 8. Starting points for NoteGPT.

In this case, we introduce the link to a YouTube video containing an interview with Daniel Innerarity on the intersection between artificial intelligence and democratic processes. On the results screen, the first thing you get on the left is the full transcript of the interview, in good quality. We can locate the transcript of a specific fragment of the video, translate it into different languages, copy it or download it, even in an SRT file of time-mapped subtitles.

Figure 9. Example of transcription with minutes in NoteGPT

Meanwhile, on the right, we find the summary of the video with the most important points, sorted and illustrated with emojis. Also in the "AI Chat" button we can interact with a conversational assistant and ask questions about the content.

Figure 10. NoteGPT summary from a YouTube interview.

And although this is already very useful, the best thing we can find in NoteGPT are the flashcards, learning cards with questions and answers to internalise the concepts of the video.

Figure 11. NoteGPT learning card (question and answer).

A trick: if the summary only appears in English, try changing the language in the three dots on the right, next to "Summarize" and click "Summarize" again. The summary will appear in English below. In the case of flashcards, to generate them in English, do not try from the home page, but from "AI flashcards". Under "Create" you can select the language.

Figure 12. Creation of flashcards in NoteGPT.

Create videos about anything: Lumen5

Lumen5 makes it easy to create videos with AI by creating the script and images automatically from text or voice content. The most interesting thing about Lumen5 is the starting point, which can be a text, a document, simply an idea or also an existing audio recording or video.

Figure 13. Lumen5 options.

The system allows us, before creating the video and also once created, to change the format from 16:9 (horizontal) to 1:1 (square) or 9:16 (vertical), even with a special 9:16 option for Instagram stories.

Figure 14. Video preview and aspect ratio options.

In this case, we will start from the same text as in previous tools: UNESCO's recommendations for democratic governance of AI. Select the starting option "Text on media", paste it directly into the box and click on "Compose script". The result is a very simple and schematic script, divided into blocks with the basic points of the text, and a very interesting indication: a prediction of the length of the video with that script, approximately 1 minute and 19 seconds.

An important note: the script is not a voice-over, but the text that will be written on the different screens. Once the video is finished, you can translate the whole video into any other language.

Figure 15. Script proposal in Lumen5.

Clicking on "Continue" will take you to the last opportunity to modify the script, where you can add new text blocks or delete existing ones. Once ready, click on "Convert to video" and you will find the story board ready to modify images, colours or the order of the screens. The video will have background music, which you can also change, and at this point you can record your voice over the music to voice the script. Without too much effort, this is the end result:

Figura 16. Resultado final de un vídeo creado con Lumen5.

From the wide range of AI-based digital products that have flourished in recent years, perhaps thousands of them, we have gone through just five examples that show us that individual and collaborative knowledge and learning are more accessible than ever before. The ease of converting content from one format to another and the automatic creation of study guides and materials should promote a more informed and agile society, not only through text or images but also through information condensed in files or databases.

It would be a great boost to collective progress if we understood that the value of AI-based systems is not as simple as writing or creating content for us, but to support our reasoning processes, objectify our decision-making and enable us to handle much more information in an efficient and useful way. Harnessing new AI capabilities together with open data initiatives may be key to the next step in the evolution of human thinking.

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.

The EU Open Data Days 2025 is an essential event for all those interested in the world of open data and innovation in Europe and the world. This meeting, to be held on 19-20 March 2025, will bring together experts, practitioners, developers, researchers and policy makers to share knowledge, explore new opportunities and address the challenges facing the open data community.

The event, organised by the European Commission through data.europa.eu, aims to promote the re-use of open data. Participants will have the opportunity to learn about the latest trends in the use of open data, discover new tools and discuss the policies and regulations that are shaping the digital landscape in Europe.

Where and when does it take place?

El evento se celebrará en el Centro Europeo de Convenciones de Luxemburgo, aunque también se podrá seguir online, con el siguiente horario:

• Wednesday 19 March 2025, from 13:30 to 18:30.
• Thursday 20 March 2025, from 9:00 to 15:30.

What issues will be addressed?

The agenda of the event is already available, where we find different themes, such as, for example:

• Success stories and best practices: the event will be attended by professionals working at the frontline of European data policy to share their experience. Among other issues, these experts will provide practical guidance on how to inventory and open up a country's public sector data, address the work involved in compiling high-value datasets or analyse perspectives on data reuse in business models. Good practices for quality metadata or improved data governance and interoperability will also be explained.
• Focus on the use of artificial intelligence (AI): open data offers an invaluable source for the development and advancement of AI. In addition, AI can optimise the location, management and use of this data, offering tools to help streamline processes and extract greater insight. In this regard, the event will address the potential of AI to transform open government data ecosystems, fostering innovation, improving governance and enhancing citizen participation. The managers of Norway's national data portal will tell how they use an AI-based search engine to improve data localisation. In addition, the advances in linguistic data spaces and their use in language modelling will be explained, and how to creatively combine open data for social impact will be explored.
• Learning about data visualisation: event attendees will be able to explore how data visualisation is transforming communication, policy making and citizen engagement. Through various cases (such as the family tree of 3,000 European royals or UNESCO's Intangible Cultural Heritage relationships) it will show how iterative design processes can uncover hidden patterns in complex networks, providing insights into storytelling and data communication. It will also address how design elements such as colour, scale and focus influence the perception of data.
• Examples and use cases: multiple examples of concrete projects based on the reuse of data will be shown, in fields such as energy, urban development or the environment. Among the experiences that will be shared is a Spanish company, Tangible Data, which will tell how physical data sculptures turn complex datasets into accessible and engaging experiences.

These are just some of the topics to be addressed, but there will also be discussions on open science, the role of open data in transparency and accountability, etc.

Why are EU Open Data Days so important?

Access to open data has proven to be a powerful tool for improving decision-making, driving innovation and research, and improving the efficiency of organisations. At a time when digitisation is advancing rapidly, the importance of sharing and reusing data is becoming increasingly crucial to address global challenges such as climate change, public health or social justice.

The EU Open Data Days 2025 are an opportunity to explore how open data can be harnessed to build a more connected, innovative and participatory Europe.

In addition, for those who choose to attend in person, the event will also be an opportunity to establish contacts with other professionals and organisations in the sector, creating new collaborations that can lead to innovative projects.

How can I attend?

To attend in person, it is necessary to register through this link. However, registration is not required to attend the event online.

If you have any queries, an e-mail address has been set up to answer any questions you may have about the event: EU-Open-Data-Days@ec.europa.eu.

More information on the event website.

Promoting the data culture is a key objective at the national level that is also shared by the regional administrations. One of the ways to achieve this purpose is to award those solutions that have been developed with open datasets, an initiative that enhances their reuse and impact on society.

On this mission, the Junta de Castilla y León and the Basque Government have been organising open data competitions for years, a subject we talked about in our first episode of the datos.gob.es podcast that you can listen to here.

In this post, we take a look at the winning projects in the latest editions of the open data competitions in the Basque Country and Castilla y León.

Winners of the 8th Castile and Leon Open Data Competition

In the eighth edition of this annual competition, which usually opens at the end of summer, 35 entries were submitted, from which 8 winners were chosen in different categories.

Ideas category: participants had to describe an idea to create studies, services, websites or applications for mobile devices. A first prize of 1,500€ and a second prize of 500€ were awarded.

• First prize: Green Guardians of Castilla y León presented by Sergio José Ruiz Sainz. This is a proposal to develop a mobile application to guide visitors to the natural parks of Castilla y León. Users can access information (such as interactive maps with points of interest) as well as contribute useful data from their visit, which enriches the application.
• Second prize: ParkNature: intelligent parking management system in natural spaces presented by Víctor Manuel Gutiérrez Martín. It consists of an idea to create an application that optimises the experience of visitors to the natural areas of Castilla y León, by integrating real-time data on parking and connecting with nearby cultural and tourist events.

Products and Services Category: Awarded studies, services, websites or applications for mobile devices, which must be accessible to all citizens via the web through a URL. In this category, first, second and third prizes of €2,500, €1,500 and €500 respectively were awarded, as well as a specific prize of €1,500 for students.

• First prize: AquaCyL from Pablo Varela Vázquez. It is an application that provides information about the bathing areas in the autonomous community.
• Second prize: ConquistaCyL presented by Markel Juaristi Mendarozketa and Maite del Corte Sanz. It is an interactive game designed for tourism in Castilla y León and learning through a gamified process.
• Third prize: All the sport of Castilla y León presented by Laura Folgado Galache. It is an app that presents all the information of interest associated with a sport according to the province.
• Student prize: Otto Wunderlich en Segovia by Jorge Martín Arévalo. It is a photographic repository sorted according to type of monuments and location of Otto Wunderlich's photographs.

Didactic Resource Category: consisted of the creation of new and innovative open didactic resources to support classroom teaching. These resources were to be published under Creative Commons licences. A single first prize of €1,500 was awarded in this category.

• First prize: StartUp CyL: Business creation through Artificial Intelligence and Open Data presented by José María Pérez Ramos. It is a chatbot that uses the ChatGPT API to assist in setting up a business using open data.

Data Journalism category: awarded for published or updated (in a relevant way) journalistic pieces, both in written and audiovisual media, and offered a prize of €1,500.

• First prize: Codorniz, perdiz y paloma torcaz son las especies más cazadas en Burgos, presented by Sara Sendino Cantera, which analyses data on hunting in Burgos.

Winners of the 5th edition of the Open Data Euskadi Open Data Competition

As in previous editions, the Basque open data portal opened two prize categories: an ideas competition and an applications competition, each of which was divided into several categories. On this occasion, 41 applications were submitted for the ideas competition and 30 for the applications competition.

Idea competition: In this category, two prizes of €3,000 and €1,500 have been awarded in each category.

Health and Social Category

• First prize: Development of a Model for Predicting the Volume of Patients attending the Emergency Department of Osakidetza by Miren Bacete Martínez. It proposes the development of a predictive model using time series capable of anticipating both the volume of people attending the emergency department and the level of severity of cases.
• Second prize: Euskoeduca by Sandra García Arias. It is a proposed digital solution designed to provide personalised academic and career guidance to students, parents and guardians.

Category Environment and Sustainability

• First prize: Baratzapp by Leire Zubizarreta Barrenetxea. The idea consists of the development of a software that facilitates and assists in the planning of a vegetable garden by means of algorithms that seek to enhance the knowledge related to the self-consumption vegetable garden, while integrating, among others, climatological, environmental and plot information in a personalised way for the user.
• Second prize: Euskal Advice by Javier Carpintero Ordoñez. The aim of this proposal is to define a tourism recommender based on artificial intelligence.

General Category

• First prize: Lanbila by Hodei Gonçalves Barkaiztegi. It is a proposed app that uses generative AI and open data to match curriculum vitae with job offers in a semantic way.. It provides personalised recommendations, proactive employment and training alerts, and enables informed decisions through labour and territorial indicators.
• Second prize: Development of an LLM for the interactive consultation of Open Data of the Basque Government by Ibai Alberdi Martín. The proposal consists in the development of a Large Scale Language Model (LLM) similar to ChatGPT, specifically trained with open data, focused on providing a conversational and graphical interface that allows users to get accurate answers and dynamic visualisations.

Applications competition: this modality has selected one project in the web services category, awarded with €8,000, and two more in the General Category, which have received a first prize of €8,000 and a second prize of €5,000.

Category Web Services

• First prize: Bizidata: Plataforma de visualización del uso de bicicletas en Vitoria-Gasteiz by Igor Díaz de Guereñu de los Ríos. It is a platform that visualises, analyses and downloads data on bicycle use in Vitoria-Gasteiz, and explores how external factors, such as the weather and traffic, influence bicycle use.

General Category

• First prize: Garbiñe AI by Beatriz Arenal Redondo. It is an intelligent assistant that combines Artificial Intelligence (AI) with open data from Open Data Euskadi to promote the circular economy and improve recycling rates in the Basque Country.
• Second prize: Vitoria-Gasteiz Businessmap by Zaira Gil Ozaeta. It is an interactive visualisation tool based on open data, designed to improve strategic decisions in the field of entrepreneurship and economic activity in Vitoria-Gasteiz.

All these award-winning solutions reuse open datasets from the regional portal of Castilla y León or Euskadi, as the case may be. We encourage you to take a look at the proposals that may inspire you to participate in the next edition of these competitions. Follow us on social media so you don't miss out on this year's calls!

The Ministry for the Digital Transformation and Civil Service, on 17 December, announced the publication of the call for proposals for products and services for data spaces, an initiative that seeks to promote innovation and development in various sectors through financial aid. These grants are designed to support companies and organisations in the implementation of advanced technological solutions, thus promoting competitiveness and digital transformation in Spain.

In addition, on 30 December, the Ministry also launched the second call for proposals for demonstrators and use cases. This call aims to encourage the creation and development of sectoral data spaces, promoting collaboration and the exchange of information between the different actors in the sector.

The Ministry has been conducting promotions through online workshops to inform and prepare stakeholders about the opportunities and benefits of the data space sector. It is expected that these events will continue throughout January, providing further opportunities for stakeholders to inform themselves and participate.

The following material is of interest to you:

Call for demonstrators and use cases

• Data space demonstrators and use cases (2nd call for proposals).
• Enquiry mailbox: dcu2.espaciosdedatos@digital.gob.es
• Presentations and helpful videos: 
  • Digital Transformation Ministry Grants Portal -DCU2- Support material.

Products and services

• Call for proposals for products and services for data spaces.
• Consultation mailbox:    ps.espaciosdedatos@digital.gob.es
• Presentations and helpful videos:
  •  Digital Transformation Ministry Grants Portal -PSED Grants Portal- Support material.

Researchers and students from various centers have also reported advances resulting from working with data:The last days of the year are always a good time to look back and assess the progress made. If a few weeks ago we took stock of what happened in the Aporta initiative, now it is time to compile the news related to data sharing, open data and the technologies linked to them.

Six months ago, we already made a first collection of milestones in the sector. On this occasion, we will summarise some of the innovations, improvements and achievements of the last half of the year.

Regulating and driving artificial intelligence

La inteligencia artificial (IA) continúa siendo uno de los campos donde cada día se aprecian nuevos avances. Se trata de un sector cuyo auge es relativamente nuevo y que necesita regulación. Por ello, la Unión Europea publicó el pasado julio el Reglamento de inteligencia artificial, una norma que marcará el entorno regulatorio europeo y global. Alineada con Europa, España ya presentó unos meses antes su nueva Estrategia de inteligencia artificial 2024, con el fin de establecer un marco para acelerar el desarrollo y expansión de la IA en España.

Artificial intelligence (AI) continues to be one of the fields where new advances are being made every day. This is a relatively new and booming sector in need of regulation. Therefore, last July, the European Union published the Artificial Intelligence Regulation, a standard that will shape the European and global regulatory environment. Aligned with Europe, Spain had already presented its new Artificial Intelligence Strategy 2024 a few months earlier, with the aim of establishing a framework to accelerate the development and expansion of AI in Spain.

On the other hand, in October, Spain took over the co-presidency of the Open Government Partnership. Its roadmap includes promoting innovative ideas, taking advantage of the opportunities offered by open data and artificial intelligence. As part of the position, Spain will host the next OGP World Summit in Vitoria.

Innovative new data-driven tools

Data drives a host of disruptive technological tools that can generate benefits for all citizens. Some of those launched by public bodies in recent months include:

• The Ministry of Transport and Sustainable Mobility has started to use Big Data technology to analyse road traffic and improve investments and road safety.
• The Principality of Asturias announces a plan to use Artificial Intelligence to end traffic jams during the summer, through the development of a digital twin.
• The Government of Aragon presented a new tourism intelligence system, which uses Big Data and AI to improve decision-making in the sector.
• The Region of Murcia has launched “Murcia Business Insight”, a business intelligence application that allows dynamic analysis of data on the region's companies: turnover, employment, location, sector of activity, etc.
• The Granada City Council has used Artificial Intelligence to improve sewerage. The aim is to achieve "more efficient" maintenance planning and execution, with on-site data.
• The Segovia City Council and Visa have signed a collaboration agreement to develop an online tool with real, aggregated and anonymous data on the spending patterns of foreign Visa cardholders in the capital. This initiative will provide relevant information to help tailor strategies to promote international tourism.

Researchers and students from various centers have also reported advances resulting from working with data:

• Researchers from the Center for Genomic Regulation (CRG) in Barcelona, the University of the Basque Country (UPV/EHU), the Donostia International Physics Center (DIPC) and the Fundación Biofísica Bizkaia have trained an algorithm to detect tissue alterations in the early stages and improve cancer diagnosis.
• Researchers from the Spanish National Research Council (CSIC) and KIDO Dynamics have launched a project to extract metadata from mobile antennas to understand the flow of people in natural landscapes. The objective is to identify and monitor the impact of tourism.
• A student at the University of Valladolid (UVa) has designed a project to improve the management and analysis of forest ecosystems in Spain at the local level, by converting municipal boundaries into a linked open data format. The results are available for re-use.

Advances in data spaces

The Ministry for Digital Transformation and the Civil Service and, specifically, the Secretariat of State for Digitalisation and Artificial Intelligence continues to make progress in the implementation of data spaces, through various actions:

• A Plan for the Promotion of Sectoral Data Spaces has been presented to promote secure data sharing.
• The development of Data Spaces for Intelligent Urban Infrastructures (EDINT) has been launched. This project, which will be carried out through the Spanish Federation of Municipalities and Provinces (FEMP), contemplates the creation of a multi-sectoral data space that will bring together all the information collected by local entities.
• In the field of digitalisation, aid has been launched for the digital transformation of strategic productive sectors through the development of technological products and services for data spaces.

Functionalities that bring data closer to reusers

The open data platforms of the various agencies have also introduced new developments, as new datasets, functionalities, strategies or reports:

• The Ministry for Ecological Transition and the Demographic Challenge has launched a new application for viewing the National Air Quality Index (AQI) in real time. It includes health recommendations for the general population and the sensitive population.
• The Andalusian Government has published a "Guide for the design of Public Policy Pilot Studies". It proposes a methodology for designing pilot studies and a system for collecting evidence for decision-making.
• The Government of Catalonia has initiated steps to implement a new data governance model that will improve relations with citizens and companies.
• The Madrid City Council is implementing a new 3D cartography and thermal map. In the Blog IDEE (Spatial Data Infrastructure of Spain) they explain how this 3D model of the capital was created using various data capture technologies.
• The Canary Islands Statistics Institute (ISTAC) has published 6,527 thematic maps with labor indicators on the Canary Islands in its open data catalog.
• Open Data Initiative and the Democratic Union of Pensioners and Retirees of Spain, with support from the Ministry of Social Rights, Consumption and Agenda 2030, presented the first Data website of the Data Observatory x Seniors. Its aim is to facilitate the analysis of healthy ageing in Spain and strategic decision-making. The Barcelona Initiative also launched a challenge to identify 50 datasets related to healthy ageing, a project supported by the Barcelona Provincial Council.
• The Centre for Technological Development and Innovation (CDTI) has presented a dashboard in beta phase with open data in exploitable format.

In addition, work continues to promote the opening up of data from various institutions:

• Asedie and the King Juan Carlos University (Madrid) have launched the Open Data Reuse Observatory to promote the reuse of open data. It already has the commitment of the Madrid City Council and they are looking for more institutions to join their Manifesto.
• The Cabildo of Tenerife and the University of La Laguna have developed a Sustainable Mobility Strategy in the Macizo de Anaga Biosphere Reserve. The aim is to obtain real-time data in order to take measures adapted to demand.

Data competitions and events to encourage the use of open data

Summer was the time chosen by various public bodies to launch competitions for products and/or services based on open data. This is the case of:

• The Community of Madrid held DATAMAD 2024 at the Universidad Rey Juan Carlos de Madrid. The event included a workshop on how to reuse open data and a datathon.
• More than 200 students registered for the I Malackathon, organised by the University of Malaga, a competition that awarded projects that used open data to propose solutions for water resource management.
• The Junta de Castilla y León held the VIII Open Data Competition, whose winners were announced in November.
• The II UniversiData Datathon was also launched. 16 finalists have been selected. The winners will be announced on 13 February 2025.
• The Cabildo of Tenerife also organised its I Open Data Competition: Ideas for reuse. They are currently evaluating the applications received. They will later launch their 2nd Open Data Competition: APP development.
• The Government of Euskadi held its V Open Data Competition. The finalists in both the Applications and Ideas categories are now known.

Also in these months there have been multiple events, which can be seen online, such as:

• The III GeoEuskadi Congress and XVI Iberian Conference on Spatial Data Infrastructures (JIIDE).
• DATAforum Justice 2024.

Other examples of events that were held but are not available online are the III Congress & XIV Conference of R Users, the Novagob 2024 Public Innovation Congress, DATAGRI 2024 or the Data Governance for Local Entities Conference, among others.

These are just a few examples of the activity carried out during the last six months in the Spanish data ecosystem. We encourage you to share other experiences you know of in the comments or via our email address dinamizacion@datos.gob.es.​

The ability to collect, analyse and share data plays a crucial role in the context of the global challenges we face as a society today. From pollution and climate change, through poverty and pandemics, to sustainable mobility and lack of access to basic services. Global problems require solutions that can be adapted on a large scale. This is where open data can play a key role, as it allows governments, organisations and citizens to work together in a transparent way, and facilitates the process of achieving effective, innovative, adaptable and sustainable solutions.

The World Bank as a pioneer in the comprehensive use of open data

One of the most relevant examples of good practices that we can find when it comes to expressing the potential of open data to tackle major global challenges is, without a doubt, the case of the World Bank, a benchmark in the use of open data for more than a decade now as a fundamental tool for sustainable development.

Since the launch of its open data portal in 2010, the institution has undergone a complete transformation process in terms of data access and use. This portal, totally innovative at the time, quickly became a reference model by offering free and open access to a wide range of data and indicators covering more than 250 economies. Moreover, its platform is constantly being updated and bears little resemblance to the initial version at present, as it is continuously improving and providing new datasets and complementary and specialised tools with the aim of making data always accessible and useful for decision making. Examples of such tools include:

• The Poverty and Inequality Platform (PIP): designed to monitor and analyse global poverty and inequality. With data from more than 140 countries, this platform allows users to access up-to-date statistics and better understand the dynamics of collective well-being. It also facilitates data visualisation through interactive graphs and maps, helping users to gain a clear and quick understanding of the situation in different regions and over time.
• The Microdata Library: provides access to household and enterprise level survey and census data in several countries. The library has more than 3,000 datasets from studies and surveys conducted by the Bank itself, as well as by other international organisations and national statistical agencies. The data is freely available and fully accessible for downloading and analysis.
• The World Development Indicators (WDI): are an essential tool for tracking progress on the global development agenda. This database contains a vast collection of economic, social and environmental development indicators, covering more than 200 countries and territories. It has data covering areas such as poverty, education, health, environmental sustainability, infrastructure and trade. The WDIs provide us with a reliable frame of reference for analysing global and regional development trends.

Figure 1. Screenshots of the web portals Poverty and Inequality Platform (PIP), Microdata Library and World Development Indicators (WDI).

Data as a transformative element for change

A major milestone in the World Bank's use of data was the publication of the World Development Report 2021, entitled "data for better lives". This report has become a flagship publication that explores the transformative potential of data to address humanity's grand challenges, improve the results of development efforts and promote inclusive and equitable growth. Through the report, the institution advocates a new social agenda for data, including robust, ethical and responsible governance of data, maximising its value in order to generate significant economic and social benefit.

The report examines how data can be integrated into public policy and development programmes to address global challenges in areas such as education, health, infrastructure and climate change. But it also marked a turning point in reinforcing the World Bank's commitment to data as a driver of change in tackling major challenges, and has since adopted a new roadmap with a more innovative, transformative and action-oriented approach to data use. Since then, they have been moving from theory to practice through their own projects, where data becomes a fundamental tool throughout the strategic cycle, as in the following examples:

• Open Data and Disaster Risk Reduction: the report "Digital Public Goods for Disaster Risk Reduction in a Changing Climate" highlights how open access to geospatial and meteorological data facilitates more effective decision-making and strategic planning. Reference is also made to tools such as OpenStreetMap that allow communities to map vulnerable areas in real time. This democratisation of data strengthens emergency response and builds the resilience of communities at risk from floods, droughts and hurricanes.
• Open data in the face of agri-food challenges: the report "What's cooking?" shows how open data is revolutionising global agri-food systems, making them more inclusive, efficient and sustainable. In agriculture, access to open data on weather patterns, soil quality and market prices empowers smallholder farmers to make informed decisions. In addition, platforms that provide open geospatial data serve to promote precision agriculture, enabling the optimisation of key resources such as water and fertilisers, while reducing costs and minimising environmental impact.
• Optimising urban transport systems: in Tanzania, the World Bank has supported a project that uses open data to improve the public transport system. The rapid urbanisation of Dar es Salaam has led to considerable traffic congestion in several areas, affecting both urban mobility and air quality. This initiative addresses traffic congestion through a real-time information system that improves mobility and reduces environmental impact. This approach, based on open data, not only increases transport efficiency, but also contributes to a better quality of life for city dwellers.

Leading by example

Finally, and within this same comprehensive vision, it is worth noting how this international organization closes the circle of open data through its use as a tool for transparency and communication of its own activities.That is why among the outstanding data tools in its catalogue we can find some of them:

• Its project and operations portal: a tool that provides detailed access to the development projects that the institution funds and implements around the world. This portal acts as a window into all its global initiatives, providing information on objectives, funding, expected results and progress for the Bank's thousands of projects.
• The Finances One platform: on which they centralise all their financial data of public interest and those corresponding to the project portfolio of all the group's entities. It aims to simplify the presentation of financial information, facilitating its analysis and sharing by customers and partners.

The future impact of open data on major global challenges

As we have also seen above, opening up data offers immense potential to advance the sustainable development agenda and thus be able to address global challenges more effectively. The World Bank has been demonstrating how this practice can evolve and adapt to current challenges. Its leadership in this area has served as a model for other institutions, showing the positive impact that open data can have on sustainable development and in tackling the major challenges affecting the lives of millions of people around the world.

However, there is still a long way to go, as transparency and access to information policies need to be further improved so that data can reach the benefit of society as a whole in a more equitable way. In addition, another key challenge is to strengthen the capacities needed to maximise the use and impact of this data, particularly in developing countries. This implies not only going beyond facilitating access, but also working on data literacy and supporting the creation of the right tools to enable information to be used effectively.

The use of open data is enabling more and more actors to participate in the creation of innovative solutions and bring about real change. All this gives rise to a new and expanding area of work that, in the right hands and with the right support, can play a crucial role in creating a safer, fairer and more sustainable future for all. We hope that many organisations will follow the World Bank's example and also adopt a holistic approach to using data to address humanity's grand challenges.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation. The contents and views reflected in this publication are the sole responsibility of the author.