Data anonymization defines the methodology and set of best practices and techniques that reduce the risk of identifying individuals, the irreversibility of the anonymization process, and the auditing of the exploitation of anonymized data by monitoring who, when, and for what purpose they are used. 

This process is essential, both when we talk about open data and general data, to protect people's privacy, guarantee regulatory compliance, and fundamental rights. 

The report "Introduction to Data Anonymization: Techniques and Practical Cases," prepared by Jose Barranquero, defines the key concepts of an anonymization process, including terms, methodological principles, types of risks, and existing techniques. 

The objective of the report is to provide a sufficient and concise introduction, mainly aimed at data publishers who need to ensure the privacy of their data. It is not intended to be a comprehensive guide but rather a first approach to understand the risks and available techniques, as well as the inherent complexity of any data anonymization process. 

What techniques are included in the report?  

After an introduction where the most relevant terms and basic anonymization principles are defined, the report focuses on discussing three general approaches to data anonymization, each of which is further integrated by various techniques: 

• Randomization: data treatment, eliminating correlation with the individual, through the addition of noise, permutation, or Differential Privacy.
• Generalization: alteration of scales or orders of magnitude through aggregation-based techniques such as K-Anonymity, L-Diversity, or T-Closeness.
• Pseudonymization: replacement of values with encrypted versions or tokens, usually through HASH algorithms, which prevent direct identification of the individual unless combined with additional data, which must be adequately safeguarded. 

The document describes each of these techniques, as well as the risks they entail, providing recommendations to avoid them. However, the final decision on which technique or set of techniques is most suitable depends on each particular case. 

The report concludes with a set of simple practical examples that demonstrate the application of K-Anonymity and pseudonymization techniques through encryption with key erasure. To simplify the execution of the case, users are provided with the code and data used in the exercise, available on GitHub. To follow the exercise, it is recommended to have minimal knowledge of the Python language. 

You can now download the complete report, as well as the executive summary and a summary presentation. 

After several months of tests and different types of training, the first massive Artificial Intelligence system in the Spanish language is capable of generating its own texts and summarising existing ones. MarIA is a project that has been promoted by the Secretary of State for Digitalisation and Artificial Intelligence and developed by the National Supercomputing Centre, based on the web archives of the National Library of Spain (BNE).

This is a very important step forward in this field, as it is the first artificial intelligence system expert in understanding and writing in Spanish. As part of the Language Technology Plan, this tool aims to contribute to the development of a digital economy in Spanish, thanks to the potential that developers can find in it.

The challenge of creating the language assistants of the future

MarIA-style language models are the cornerstone of the development of the natural language processing, machine translation and conversational systems that are so necessary to understand and automatically replicate language. MarIA is an artificial intelligence system made up of deep neural networks that have been trained to acquire an understanding of the language, its lexicon and its mechanisms for expressing meaning and writing at an expert level.

Thanks to this groundwork, developers can create language-related tools capable of classifying documents, making corrections or developing translation tools.

The first version of MarIA was developed with RoBERTa, a technology that creates language models of the "encoder" type, capable of generating an interpretation that can be used to categorise documents, find semantic similarities in different texts or detect the sentiments expressed in them.

Thus, the latest version of MarIA has been developed with GPT-2, a more advanced technology that creates generative decoder models and adds features to the system. Thanks to these decoder models, the latest version of MarIA is able to generate new text from a previous example, which is very useful for summarising, simplifying large amounts of information, generating questions and answers and even holding a dialogue.

Advances such as the above make MarIA a tool that, with training adapted to specific tasks, can be of great use to developers, companies and public administrations. Along these lines, similar models that have been developed in English are used to generate text suggestions in writing applications, summarise contracts or search for specific information in large text databases in order to subsequently relate it to other relevant information.

In other words, in addition to writing texts from headlines or words, MarIA can understand not only abstract concepts, but also their context.

More than 135 billion words at the service of artificial intelligence

To be precise, MarIA has been trained with 135,733,450,668 words from millions of web pages collected by the National Library, which occupy a total of 570 Gigabytes of information. The MareNostrum supercomputer at the National Supercomputing Centre in Barcelona was used for the training, and a computing power of 9.7 trillion operations (969 exaflops) was required.

Bearing in mind that one of the first steps in designing a language model is to build a corpus of words and phrases that serves as a database to train the system itself, in the case of MarIA, it was necessary to carry out a screening to eliminate all the fragments of text that were not "well-formed language" (numerical elements, graphics, sentences that do not end, erroneous encodings, etc.) and thus train the AI correctly.

Due to the volume of information it handles, MarIA is already the third largest artificial intelligence system for understanding and writing with the largest number of massive open-access models. Only the language models developed for English and Mandarin are ahead of it. This has been possible mainly for two reasons. On the one hand, due to the high level of digitisation of the National Library's heritage and, on the other hand, thanks to the existence of a National Supercomputing Centre with supercomputers such as the MareNostrum 4.

The role of BNE datasets

Since it launched its own open data portal (datos.bne.es) in 2014, the BNE has been committed to bringing the data available to it and in its custody closer: data on the works it preserves, but also on authors, controlled vocabularies of subjects and geographical terms, among others.

In recent years, the educational platform BNEscolar has also been developed, which seeks to offer digital content from the Hispánica Digital Library's documentary collection that may be of interest to the educational community.

Likewise, and in order to comply with international standards of description and interoperability, the BNE data are identified by means of URIs and linked conceptual models, through semantic technologies and offered in open and reusable formats. In addition, they have a high level of standardisation.

Next steps

Thus, and with the aim of perfecting and expanding the possibilities of use of MarIA, it is intended that the current version will give way to others specialised in more specific areas of knowledge. Given that it is an artificial intelligence system dedicated to understanding and generating text, it is essential for it to be able to cope with lexicons and specialised sets of information.

To this end, the PlanTL will continue to expand MarIA to adapt to new technological developments in natural language processing (more complex models than the GPT-2 now implemented, trained with larger amounts of data) and will seek ways to create workspaces to facilitate the use of MarIA by companies and research groups.

Content prepared by the datos.gob.es team.

The rise of smart cities, the distribution of resources during pandemics or the fight against natural disasters has awakened interest in geographic data. In the same way that open data in the healthcare field helps to implement social improvements related to the diagnosis of diseases or the reduction of waiting lists, Geographic Information Systems help to streamline and simplify some of the challenges of the future, with the aim of making them more environmentally sustainable, more energy efficient and more livable for citizens. 

As in other fields, professionals dedicated to optimizing Geographic Information Systems (GIS) also build their own working groups, associations and training communities. GIS communities are groups of volunteers interested in using geographic information to maximize the social benefits that this type of data can bring in collective terms.  

Thus, by addressing the different approaches offered by the field of geographic information, data communities work on the development of applications, the analysis of geospatial information, the generation of cartographies and the creation of informative content, among others.  

In the following lines, we will analyze step by step what is the commitment and objective of three examples of GIS communities that are currently active. 

Gis and Beers 

What is it and what is its objective? 

Gis and Beers is an association focused on the dissemination, analysis and design of tools linked to geographic information and cartographic data. Specialized in sustainability and environment, they use open data to propose and disseminate solutions that seek to design a sustainable and nature-friendly environment. 

What functions does it perform? 

In addition to disseminating specialized content such as reports and data analysis, the members of Gis and Beers offer training resources dedicated to facilitating the understanding of geographic information systems from an environmental perspective. It is common to read articles on their website focused on new environmental data or watch tutorials on how to access open data platforms specialized in the environment or the tools available for their management. Likewise, every time they detect the publication of a new open data catalog, they share on their website the necessary instructions for downloading the data, managing it and representing it cartographically. 

Next steps  

In line with the environmental awareness that marks the project, Gis and Beers is devoting more and more effort to strengthening two key pillars for its content: raising awareness of the importance of citizen science (a collaborative movement that provides data observed by citizens) and promoting access to data that facilitate modeling without previously adapting them to cartographic analysis needs. 

The role of open data 

The origin of most of the open data they use comes from state sources such as the IIGN, Aemet or INE, although they also draw on other options such as those offered by Google Earth Engine and Google Public Data.  

How to contact them? 

If you are interested in learning more about the work of this community or need to contact Gis and Beers, you can visit their website or write directly to this email account.  

Geovoluntarios 

What is it and what is its objective? 

It is a non-profit Organization formed by professionals experienced in the use and remote application of geospatial technology and whose objective is to cooperate with other organizations that provide support in emergency situations and in projects aligned with the Sustainable Development Goals. 

The association's main objectives are: 

• To provide help to organizations in any of the phases of an emergency, prioritizing help to non-profit, life-saving organizations or those supporting the third sector. Some of them are Red Cross, Civil Protection, humanitarian organizations, etc. 
• Encourage digital volunteering among people with knowledge or interest in geospatial technologies and working with geolocated data. 
• Find ways to support organizations working towards the Sustainable Development Goals (SDGs). 
• Provide geospatial tools and geolocated data to non-profit projects that would otherwise not be technically or economically feasible. 

What functions does it perform? 

The professional experience accumulated by the members of geovolunteers allows them to offer support in tasks related to the analysis of geographic data, the design of models or the monitoring of special emergency situations. Thus, the most common functions carried out as an NGO can be summarized as follows: 

• Training and providing means to volunteers and organizations in all the necessary aspects to provide aid with guarantees: geographic information systems, spatial analysis, RGPD, security, etc. 
• Facilitate the creation of temporary work teams to respond to requests for assistance received and that are in line with the organization's goals. 
• Create working groups that maintain data that serve a general purpose. 
• Seek collaboration agreements with other entities, organize and participate in events and carry out campaigns to promote digital volunteering.

From a more specific point of view, among all the projects in which Geovolunteers has participated, two initiatives in which the members were particularly involved are worth mentioning. On the one hand, the Covid data project, where a community of digital volunteers committed to the search and analysis of reliable data was created to provide quality information on the situation being experienced in each of the different autonomous communities of Spain. Another initiative to highlight was Reactiva Madrid, an event organized by the Madrid City Council and Esri Spain, which was created to identify and develop work that, through citizen participation, would help to prevent and/or solve problems related to the pandemic caused by COVID-19 in the areas of the economy, mobility and society. 

Next steps 

After two years focused on solving part of the problems generated by the Covid-19 crisis, Geovolunteers continues to focus on collaborating with organizations that are committed to assisting the most vulnerable people in emergency situations, without forgetting the commitment that links them to meeting the Sustainable Development Goals.  

Thus, one of the projects in which the volunteers are most active is the implementation and improvement of GeoObs, an app to geolocate different observation projects on: dirty spots, fire danger, dangerous areas for bikers, improving a city, safe cycling, etc. 

The role of open data 

For an NGO like Geovolunteers, open data is essential both to develop the solidarity tasks they carry out together with other associations, as well as to design their own services and applications. Hence, these resources are part of the new functionalities on which the Association wants to focus.  

So much so that data collection marks a starting point for the pilot projects that can currently be found under the Geovolunteers umbrella. Without going any further, the application mentioned above is an example that demonstrates how generating data by observation can contribute to enriching the available open data catalogs. 

GIS Community 

What is it and what is its objective? 

GIS Community is a virtual collective that brings together professionals in the field of geographic data and information systems related to the same sector. Founded in 2009, they disseminate their work through social networks such as Facebook, Twitter or Instagram from where, in addition, they share news and relevant information on geotechnology, geoprocessing or land use planning among other topics. 

Its objective is none other than to contribute to expand the informative and interesting knowledge for the geographic data community, a virtual space with little presence when this project began its work on the Internet.  

What functions does it perform? 

In line with the objectives mentioned above, the tasks developed by SIG are focused on the sharing and generation of content related to Geographic Information Systems. Given the diversity of fields and sectors of action within the same field, they try to balance the content of their publications to bring together both those who seek information and those who provide opportunities. For this reason it is possible to find news about events, training, research projects, news about entrepreneurs or literature among many others. 

Next steps 

Aware of the weight they have as a community within the field of geographic data, from SIG they plan to strengthen four axes that directly affect the work of the project: organize lectures and webinars, contact organizations and institutions capable of providing funding for projects in the GIS area, seek entities that provide open geospatial information and, finally, get part of the private sector to participate financially in the education and training of professionals in the field of GIS.  

The role of open data 

This is a community that is closely linked to the universe of open data, because it shares content that can be used, supplemented and redistributed freely by users. In fact, according to its own members, there is an increasing acceptance and preference for this trend, with community collaborators and their own projects driving the debate and interest in using open data in all active phases of their tasks or activities. 

How to contact them? 

As in the previous cases, if you are interested in contacting Comunidad SIG you can do so through their Facebook page, Twitter or Instagram or by sending an email to the following email.  

Communities like Gis and Beers, SIG or Geovolunteers are just a small example of the work that the GIS collective is currently developing. If you are part of any data community in this or any other field or you know about the work of communities that may be of interest in datos.gob.es, do not hesitate to send us an email to dinamizacion@datos.gob.es. 

Geo Developers

What is it and what is its purpose?

Geodevelopers is a community whose objective is to bring together developers and surveyors in the field of geographic data. The main function of this community is to share different professional experiences related to geographic data and, for this purpose, they organize talks where everyone can share their experience and knowledge with the rest.

Through their YouTube channel it is possible to access the trainings and talks held to date, as well as to be aware of the next ones that may be held.

The role of open data

Although this is not a community focused on the reuse of open data as such, they use it to develop some projects and extract new learnings that they then incorporate into their workflows.

Next steps and contact

The main objective for the future of Geodevelopers is to grow the community in order to continue sharing experiences and knowledge with the rest of the GIS stakeholders. If you want to get in touch and follow the evolution of this project you can do it through its Twitter profile.

According to the latest analysis conducted by Gartner in September 2021, on Artificial Intelligence trends, Chatbots are one of the technologies that are closest to deliver effective productivity in less than 2 years. Figure 1, extracted from this report, shows that there are 4 technologies that are well past the peak of inflated expectations and are already starting to move out of the valley of disillusionment, towards states of greater maturity and stability, including chatbots, semantic search, machine vision and autonomous vehicles.

Figure 1-Trends in AI for the coming years.

In the specific case of chatbots, there are great expectations for productivity in the coming years thanks to the maturity of the different platforms available, both in Cloud Computing options and in open source projects, especially RASA or Xatkit. Currently it is relatively easy to develop a chatbot or virtual assistant without AI knowledge, using these platforms.

How does a chatbot work?

As an example, Figure 2 shows a diagram of the different components that a chatbot usually includes, in this case focused on the architecture of the RASA project.

Figure 2- RASA project architecture

One of the main components is the agent module, which acts as a controller of the data flow and is normally the system interface with the different input/output channels offered to users, such as chat applications, social networks, web or mobile applications, etc.

The NLU (Natural Languge Understanding) module is responsible for identifying the user's intention (what he/she wants to consult or do), entity extraction (what he/she is talking about) and response generation. It is considered a pipeline because several processes of different complexity are involved, in many cases even through the use of pre-trained Artificial Intelligence models.

Finally, the dialogue policies module defines the next step in a conversation, based on context and message history. This module is integrated with other subsystems such as the conversation store (tracker store) or the server that processes the actions necessary to respond to the user (action server).

Chatbots in open data portals as a mechanism to locate data and access information

There are more and more initiatives to empower citizens to consult open data through the use of chatbots, using natural language interfaces, thus increasing the net value offered by such data. The use of chatbots makes it possible to automate data collection based on interaction with the user and to respond in a simple, natural and fluid way, allowing the democratization of the value of open data.

At SOM Research Lab (Universitat Oberta de Catalunya) they were pioneers in the application of chatbots to improve citizens' access to open data through the Open Data for All and BODI (Bots to interact with open data - Conversational interfaces to facilitate access to public data) projects. You can find more information about the latter project in this article.

It is also worth mentioning the Aragón Open Data chatbot, from the open data portal of the Government of Aragón, which aims to bring the large amount of data available to citizens, so that they can take advantage of its information and value, avoiding any technical or knowledge barrier between the query made and the existing open data. The domains on which it offers information are: 

• General information about Aragon and its territory
• Tourism and travel in Aragon
• Transportation and agriculture
• Technical assistance or frequently asked questions about the information society.

Conclusions

These are just a few examples of the practical use of chatbots in the valorization of open data and their potential in the short term. In the coming years we will see more and more examples of virtual assistants in different scenarios, both in the field of public administrations and in private services, especially focused on improving user service in e-commerce applications and services arising from digital transformation initiatives.

Content prepared by José Barranquero, expert in Data Science and Quantum Computing.

The contents and points of view reflected in this publication are the sole responsibility of the author.

The pandemic situation we have experienced in recent years has led to a large number of events being held online. This was the case of the Iberian Conference on Spatial Data Infrastructures (JIIDE), whose 2020 and 2021 editions had a virtual format. However, the situation has changed and in 2022 we will be able to meet again to discuss the latest trends in geographic information.

Seville will host JIIDE 2022

Seville has been the city chosen to bring together all those professionals from the public administration, private sector and academia interested in geographic information and who use Spatial Data Infrastructures (SDI) in the exercise of their activities.

Specifically, the event will take place from 25 to 27 October at the University of Seville. You can find more information here.

Focus on user experience

This year's slogan is "Experience and technological evolution: bringing the SDI closer to citizens".  The aim is to emphasise new technological trends and their use to provide citizens with solutions that solve specific problems, through the publication and processing of geographic information in a standardised, interoperable and open way.

Over three days, attendees will be able to share experiences and use cases on how to use Big Data, Artificial Intelligence and Cloud Computing techniques to improve the analysis capacity, storage and web publication of large volumes of data from various sources, including real-time sensors.

New specifications and standards that have emerged will also be discussed, as well as the ongoing evaluation of the INSPIRE Directive.

Agenda now available

Although some participations are still to be confirmed, the programme is already available on the conference website. There will be around 80 communications where experiences related to real projects will be presented, 7 technical workshops where specific knowledge will be shared and a round table to promote debate.

Among the presentations there are some focused on open data. This is the case of Valencia City Council, which will talk about how they use open data to obtain environmental equity in the city's neighbourhoods, or the session dedicated to the "Digital aerial photo library of Andalusia: a project for the convergence of SDIs and Open-Data".

How can I attend?

The event is free of charge, but to attend you need to register using this form. You must indicate the day you wish to attend.

For the moment, registration is open to attend in person, but in September, the website of the conference will offer the possibility of participating in the JIIDE virtually.

Organisers

The Jornadas Ibéricas de Infraestructuras de Datos Espaciales (JIIDE) were born from the collaboration of the Directorate General of Territory of Portugal, the National Geographic Institute of Spain and the Government of Andorra. On this occasion, the Institute of Statistics and Cartography of Andalusia and the University of Seville join as organisers.

KSNET (Knowledge Sharing Network S.L) is a company dedicated to the transfer of knowledge that aims to improve programmes and policies with both a social and economic impact. That is why they accompany their clients throughout the process of creating these programmes, from the diagnosis, design and implementation phase to the evaluation of the results and impact achieved, also providing a vision of the future based on proposals for improvement.

Estudio Alfa is a technology company dedicated to offering services that promote the image of companies and brands on the Internet, including the development of apps. To carry out these services, they use techniques and strategies that comply with usability standards and favour positioning in search engines, thus helping their clients' websites to receive more visitors and thus potential clients. They also have special experience in the production and tourism sectors.

A few months ago, Facebook surprised us all with a name change: it became Meta. This change alludes to the concept of "metaverse" that the brand wants to develop, uniting the real and virtual worlds, connecting people and communities.

Among the initiatives within Meta is Data for Good, which focuses on sharing data while preserving people's privacy. Helene Verbrugghe, Public Policy Manager for Spain and Portugal at Meta spoke to datos.gob.es to tell us more about data sharing and its usefulness for the advancement of the economy and society.

Full interview:

1. What types of data are provided through the Data for Good Initiative?

Meta's Data For Good team offers a range of tools including maps, surveys and data to support our 600 or so partners around the world, ranging from large UN institutions such as UNICEF and the World Health Organization, to local universities in Spain such as the Universitat Poliècnica de Catalunya and the University of Valencia.

To support the international response to COVID-19, data such as those included in our Range of Motion Maps have been used extensively to measure the effectiveness of stay-at-home measures, and in our COVID-19 Trends and Impact Survey to understand issues such as reluctance to vaccinate and inform outreach campaigns. Other tools, such as our high-resolution population density maps, have been used to develop rural electrification plans and five-year water and sanitation investments in places such as Rwanda and Zambia. We also have AI-based poverty maps that have helped extend social protection in Togo and an international social connectivity index that has been useful for understanding cross-border trade and financial flows. Finally, we have recently worked to support groups such as the International Federation of the Red Cross and the International Organization for Migration in their response to the Ukraine crisis, providing aggregated information on the volumes of people leaving the country and arriving in places such as Poland, Germany and the Czech Republic.    

Privacy is built into all our products by default; we aggregate and de-identify information from Meta platforms, and we do not share anyone's personal information.

2. What is the value for citizens and businesses? Why is it important for private companies to share their data?

Decision-making, especially in public policy, requires information that is as accurate as possible. As more people connect and share content online, Meta provides a unique window into the world. The reach of Facebook's platform across billions of people worldwide allows us to help fill key data gaps. For example, Meta is uniquely positioned to understand what people need in the first hours of a disaster or in the public conversation around a health crisis - information that is crucial for decision-making but was previously unavailable or too expensive to collect in time.

For example, to support the response to the crisis in Ukraine, we can provide up-to-date information on population changes in neighbouring countries in near real-time, faster than other estimates. We can also collect data at scale by promoting Facebook surveys such as our COVID-19 Trends and Impact Survey, which has been used to better understand how mask-wearing behaviour will affect transmission in 200 countries and territories around the world.  

3. The information shared through Data for Good is anonymised, but what is the process like? How is the security and privacy of user data guaranteed?

Data For Good respects the choices of Facebook users. For example, all Data For Good surveys are completely voluntary. For location data used for Data For Good maps, users can choose whether they want to share that information from their location history settings. 

We also strive to share how we protect privacy by publishing blogs about our methods and approaches. For example, you can read about our differential privacy approach to protecting mobility data used in the response to COVID-19 here.

4. What other challenges have you encountered in setting up an initiative of this kind and how have you overcome them?

When we started Data For Good, the vast majority of our datasets were only available through a licensing agreement, which was a cumbersome process for some partners and unfeasible for many governments. However, at the onset of the COVID-19 pandemic, we realised that, in order to operate at scale, we would need to make more of our work publicly available, while incorporating stringent measures, such as differential privacy, to ensure security. In recent years, most of our datasets have been made public on platforms such as the Humanitarian Data Exchange, and through this tool and other APIs, our public tools have been queried more than 55 million times in the past year. We are proud of the move towards open source sharing, which has helped us overcome early difficulties in scaling up and meeting the demand for our data from partners around the world.

5. What are Meta's future plans for Data for Good?

Our goal is to continue to help our partners get the most out of our tools, while continuing to evolve and create new ways to help solve real-world problems. In the past year, we have focused on growing our toolkit to respond to issues such as climate change through initiatives such as our Climate Change Opinion Survey, which will be expanded this year; as well as evolving our knowledge of cross-border population flows, which is proving critical in supporting the response to the crisis in Ukraine.

It is important to publish open data following a series of guidelines that facilitate its reuse, including the use of common schemas, such as standard formats, ontologies and vocabularies. In this way, datasets published by different organizations will be more homogeneous and users will be able to extract value more easily.

One of the most recommended families of formats for publishing open data is RDF (Resource Description Framework). It is a standard web data interchange model recommended by the World Wide Web Consortium, and highlighted in the F.A.I.R. principles or the five-star schema for open data publishing.

RDFs are the foundation of the semantic web, as they allow representing relationships between entities, properties and values, forming graphs. In this way, data and metadata are automatically interconnected, generating a network of linked data that facilitates their exploitation by reusers. This also requires the use of agreed data schemas (vocabularies or ontologies), with common definitions to avoid misunderstandings or ambiguities.

In order to promote the use of this model, from datos.gob.es we provide users with the "Practical guide for the publication of linked data", prepared in collaboration with the Ontology Engineering Group team - Artificial Intelligence Department, ETSI Informáticos, Polytechnic University of Madrid-.

The guide highlights a series of best practices, tips and workflows for the creation of RDF datasets from tabular data, in an efficient and sustainable way over time.

Who is the guide aimed at?

The guide is aimed at those responsible for open data portals and those preparing data for publication on such portals. No prior knowledge of RDF, vocabularies or ontologies is required, although a technical background in XML, YAML, SQL and a scripting language such as Python is recommended.

What does the guide include?

After a short introduction, some necessary theoretical concepts (triples, URIs, controlled vocabularies by domain, etc.) are addressed, while explaining how information is organized in an RDF or how naming strategies work.

Next, the steps to be followed to transform a CSV data file, which is the most common in open data portals, into a normalized RDF dataset based on the use of controlled vocabularies and enriched with external data that enhance the context information of the starting data are described in detail. These steps are as follows:

The guide ends with a section oriented to more technical profiles that implements an example of the use of RDF data generated using some of the most common programming libraries and databases for storing triples to exploit RDF data.

Additional materials

The practical guide for publishing linked data is complemented by a cheatsheet that summarizes the most important information in the guide and a series of videos that help to understand the set of steps carried out for the transformation of CSV files into RDF. The videos are grouped in two series that relate to the steps explained in the practical guide:

1) Series of explanatory videos for the preparation of CSV data using OpenRefine. This series explains the steps to be taken to prepare a CSV file for its subsequent transformation into RDF:

• Video 1: Pre-loading tabular data and creating an OpenRefine project.
• Video 2: Modifying column values with transformation functions.
• Video 3: Generating values for controlled lists or SKOS.
• Video 4: Linking values with external sources (Wikidata) and downloading the file with the new modifications.

2) Series of explanatory videos for the construction of transformation rules or CSV to RDF mappings.  This series explains the steps to be taken to transform a CSV file into RDF by applying transformation rules.

• Video 1: Downloading the basic template for the creation of transformation rules and creating the skeleton of the transformation rules document.
• Video 2: Specifying the references for each property and how to add the Wikidata reconciled values obtained through OpenRefine.

Below you can download the complete guide, as well as the cheatsheet. To watch the videos you must visit our Youtube channel.

Google is a company with a strong commitment to open data. It has launched Google Dataset Search, to locate open data in existing repositories around the world, and also offers its own datasets in open format as part of its Google Research initiative. In addition, it is a reuser of open data in solutions such as Google Earth.

Among its areas of work is Google for Education, with solutions designed for teachers and students. In datos.gob.es we have interviewed Gonzalo Romero, director of Google for Education in Spain and member of the jury in charge of evaluating the proposals received in the III edition of Desafío Aporta. Gonzalo talked to us about his experience, the influence of open data in the education sector and the importance of open data.

Full interview:

1. What challenges does the education sector face in Spain and how can open data and data-driven technologies help to overcome them?

Last year, due to the pandemic, the education sector was forced to accelerate its digitalization process so that the activity could develop as normally as possible.

The main challenges facing the education sector in Spain are technology and digitization as this sector is less digitized than average. Secure, simple and sustainable digital tools are needed so that the education system, from teachers and students to administrators, can operate easily and without any problems.

Open data makes it possible to locate certain quality information from thousands of sources quickly and easily at any time. These repositories create a reliable data sharing ecosystem that encourages publishers to publish data to drive student learning and the development of technology solutions.

2. Which datasets are most in demand for implementing educational solutions?

Each region usually generates its own. The main challenge is how new datasets can be created in collaboration with the variables that allow them to create predictive models to anticipate the main challenges they face, such as school dropout, personalization of learning or academic and professional orientation, among others.

3. How can initiatives such as hackathons or challenges help drive data-driven innovation? How was your experience in the III Aporta Challenge?

It is essential to support projects and initiatives that develop innovative solutions to promote the use of data.

Technology offers tools that help to find synergies between public and private data to develop technological solutions and promote different skills among students.

4. In addition to being the basis for technological solutions, open data also plays an important role as an educational resource in its own right, as it can provide knowledge in multiple areas. To what extent does this type of resource foster critical thinking in students?

The use of open data in the classroom is a way to boost and foster students' educational skills. For a good use of these resources it is important to search and filter the information according to the needs, as well as to improve the ability to analyse data and argumentation in a reasoned way. In addition, it allows the student to manage technological programs and tools.

These skills are useful for the future not only academically but also in the labour market, since more and more professionals with skills related to analytical capacity and data management are in demand.

5. Through your Google Research initiative, multiple projects are being carried out, some of them linked to the opening and reuse of open data. Why is it important that private companies also open data?

We understand the difficulties that private companies may have if they share data since sharing their information can be an advantage for competitors. However, it is essential to combine public and private sector data to drive the growth of the open data market that can lead to new analyses and studies and the development of new products and services.

It is also important to approach data reuse in the light of new and emerging social challenges and to facilitate the development of solutions without having to start from scratch.

6.What are Google's future plans for open data?

Sensitive corporate data has high survivability requirements, in case a provider has to cancel cloud services due to policy changes in a country or region, and we believe it is not possible to secure data with a proprietary solution. However, we do have open source and open standards tools that address multiple customer concerns.

Data analysis tools such as BigQuery or BigQuery Omni allow customers to make their own data more open, both inside and outside their organization. The potential of that data can then be harnessed in a secure and cost-efficient way. We already have clear use cases of value created with our data and artificial intelligence technology, and endorsed by the CDTI, such as the Student Success data dropout prevention model. Leading educational institutions already use it on a daily basis and it is in pilot phase in some education departments.

The company's goal is to continue working to build an open cloud hand in hand with our local partners and public institutions in Spain and across Europe, creating a secure European digital data ecosystem with the best technology.