Digital technology and algorithms have revolutionised the way we live, work and communicate. While promising efficiency, accuracy and convenience, these technologies can exacerbate prejudice and social inequalities exacerbate prejudice and social inequalities and create new forms of exclusion and create new forms of exclusion. Thus, invisibilisation and discrimination, which have always existed, take on new forms in the age of algorithms.

Lack of interest and data leads to algorithmic invisibilisation, leading to two types of algorithmic neglect. The first of these is among the world's underserved, which includes the millions who do not have a smartphone or a bank account millions who do not have a smartphone or a bank account, and are thus on the margins of the platform economy and who are therefore on the margins of the platform economy and, for algorithms, do not exist. The second type of algorithmic abandonment includes individuals or groups who are victims of the failure of the algorithmic system, as was the case with SyRI(Systeem Risico Indicatie)SyRI(Systeem Risico Indicatie) in the Netherlands that unfairly singled out some 20,000 families from low socio-economic backgrounds for tax fraud, leading many to ruin by 2021. The algorithm, which the algorithm, which was declared illegal by a court in The Hague months later, was applied in the country's poorest neighbourhoodsthe algorithm, which was declared illegal by a court in The Hague months later, was applied in the country's poorest neighbourhoods the algorithm, which was declared illegal by a court in The Hague months later, was applied in the poorest neighbourhoods of the country and blocked many families with more than one nationality from receiving the social benefits to which they were entitled because of their socio-economic status.

Beyond the example in the Dutch public system, invisibilisation and discrimination can also originate in the private sector. One example is Amazon's amazon's job posting algorithm which showed a bias against women by learning from historical data - i.e. incomplete data because it did not include a large and representative universe - leading Amazon to abandon the project. which showed a bias against women by learning from historical data - i.e. incomplete data because it did not include a large and representative universe - leading Amazon to abandon the project. Another example is Apple Card, a credit card backed by Goldman Sachs, which was also singled out when its algorithm was found to offer more favourable credit limits to men than to women.

In general, invisibility and algorithmic discrimination, in any field, can lead to unequal access to resources and exacerbate social and economic exclusion.

Making decisions based on algorithms

Data and algorithms are interconnected components in computing and information processing. Data serve as a basis, but can be unstructured, with excessive variability and incompleteness. Algorithms are instructions or procedures designed to process and structure this data and extract meaningful information, patterns or results.

The quality and relevance of the data directly impacts the effectiveness of the algorithms, as they rely on the data inputs to generate results. Hence, the principle "rubbish in, rubbish out"which summarises the idea that if poor quality, biased or inaccurate data enters a system or process, the result will also be of poor quality or inaccurate. On the other hand, well-designed well-designed algorithms can enhance the value of data by revealing hidden relationships or making by revealing hidden relationships or making predictions.

This symbiotic relationship underscores the critical role that both data and algorithms play in driving technological advances, enabling informed decision making, and This symbiotic relationship underscores the critical role that both data and algorithms play in driving technological advances, enabling informed decision making, and fostering innovation.

Algorithmic decision making refers to the process of using predefined sets of instructions or rules to analyse data and make predictions to aid decision making. Increasingly, it is being applied to decisions that have to do with social welfare social welfare and the provision of commercial services and products through platforms. This is where invisibility or algorithmic discrimination can be found.

Increasingly, welfare systems are using data and algorithms to help make decisions on issues such as who should receive what kind of care and who is at risk. These algorithms consider different factors such as income, family or household size, expenditures, risk factors, age, sex or gender, which may include biases and omissions.

That is why the That is why the Special Rapporteur on extreme poverty and human rights, Philip Alston, warned in a report to the UN General Assembly that the uncautious adoption of these can lead to dystopian social welfare dystopian social welfare. In such a dystopian welfarestate , algorithms are used to reduce budgets, reduce the number of beneficiaries, eliminate services, introduce demanding and intrusive forms of conditionality, modify behaviour, impose sanctions and "reverse the notion that the state is accountable".

Algorithmic invisibility and discrimination: Two opposing concepts

Although data and algorithms have much in common, algorithmic invisibility and discrimination are two opposing concepts. Algorithmic invisibility refers to gaps in data sets or omissions in algorithms, which result in inattentions in the application of benefits or services. In contrast, algorithmic discrimination speaks to hotspots that highlight specific communities or biased characteristics in datasets, generating unfairness.

That is, algorithmic invisibilisation occurs when individuals or groups are absent from datasets, making it impossible to address their needs. For example, integrating data on women with disabilities into social decision-making can be vital for the inclusion of women with disabilities in society. Globally, women are more vulnerable to algorithmic invisibilisation than men, as they have less access to digital technology have less access to digital technology and leave fewer digital traces.

Opaque algorithmic systems that incorporate stereotypes can increase invisibilisation and discrimination by hiding or targeting vulnerable individuals or populations. An opaque algorithmic system is one that does not allow access to its operation.

On the other hand, aggregating or disaggregating data without careful consideration of the consequences can result in omissions or errors result in omissions or errors. This illustrates the double-edged nature of accounting; that is, the ambivalence of technology that quantifies and counts, and that can serve to improve people's lives, but also to harm them.

Discrimination can arise when algorithmic decisions are based on historical data, which usually incorporate asymmetries, stereotypes and injustices, because more inequalities existed in the past. The "rubbish in, rubbish out" effect occurs if the data is skewed, as is often the case with online content. Also, biased or incomplete databases can be incentives for algorithmic discrimination. Selection biases may arise when facial recognition data, for example, is based on the features of white men, while the users are dark-skinned women, or on online content generated by a minority of agentswhich makes generalisation difficult.

As can be seen, tackling invisibility and algorithmic discrimination is a major challenge that can only be solved through awareness-raising and collaboration between institutions, campaigning organisations, businesses and research.

Content prepared by Miren Gutiérrez, PhD and researcher at the University of Deusto, expert in data activism, data justice, data literacy and gender disinformation.

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

The UNESCO (United Nations Educational, Scientific and Cultural Organization) is a United Nations agency whose purpose is to contribute to peace and security in the world through education, science, culture and communication. In order to achieve its objective, this organisation usually establishes guidelines and recommendations such as the one published on 5 July 2023 entitled 'Open data for AI: what now?'

In the aftermath of the COVID-19 pandemic the UNESCO highlights a number of lessons learned:  

1.  Policy frameworks and data governance models must be developed, supported by sufficient infrastructure, human resources and institutional capacities to address open data challenges, in order to be better prepared for pandemics and other global challenges.
2. The relationship between open data and AI needs to be further specified, including what characteristics of open data are necessary to make it "AI-Ready".
3. A data management, collaboration and sharing policy should be established for research, as well as for government institutions that hold or process health-related data, while ensuring data privacy through anonymisation and anonymisation data privacy should be ensured through anonymisation and anonymisation.
4. Government officials who handle data that are or may become relevant to pandemics may need training to recognise the importance of such data, as well as the imperative to share them.
5. As much high quality data as possible should be collected and collated. The data needs to come from a variety of credible sources, which, however, must also be ethical, i.e. it must not include data sets with biases and harmful content, and it must be collected only with consent and not in a privacy-invasive manner. In addition, pandemics are often rapidly evolving processes, so continuous updating of data is essential.
6. These data characteristics are especially mandatory for improving inadequate AI diagnostic and predictive tools in the future. Efforts are needed to convert the relevant data into a machine-readable format, which implies the preservation of the collected data, i.e. cleaning and labelling.
7. A wide range of pandemic-related data should be opened up,  adhering to the FAIR principles.
8. The target audience for pandemic-related open data includes research and academia, decision-makers in governments, the private sector for the development of relevant products, but also the public, all of whom should be informed about the available data.​
9. Pandemic-related open data initiatives should be institutionalised rather than ad hoc, and should therefore be put in place for future pandemic preparedness. These initiatives should also be inclusive and bring together different types of data producers and users.
10. The beneficial use of pandemic-related data for AI machine learning techniques should also be regulated to prevent misuse for the development of artificial pandemics, i.e. biological weapons, with the help of AI systems.

The UNESCO builds on these lessons learned to establish Recommendations on Open Science by facilitating data sharing, improving reproducibility and transparency, promoting data interoperability and standards, supporting data preservation and long-term access.

As we increasingly recognise the role of Artificial Intelligence (AI), the availability and accessibility of data is more crucial than ever, which is why UNESCO is conducting research in the field of AI to provide knowledge and practical solutions to foster digital transformation and build inclusive knowledge societies.

Open data is the main focus of these recommendations, as it is seen as a prerequisite for planning, decision-making and informed interventions. The report therefore argues that Member States must share data and information, ensuring transparency and accountability, as well as opportunities for anyone to make use of the data.

UNESCO provides a guide that aims to raise awareness of the value of open data and specifies concrete steps that Member States can take to open their data. These are practical, but high-level steps on how to open data, based on existing guidelines. Three phases are distinguished: preparation, data opening and follow-up for re-use and sustainability, and four steps are presented for each phase. 

It is important to note that several of the steps can be carried out simultaneously, i.e. not necessarily consecutively.

1. Develop a data management and sharing policy: A data management and sharing policy is an important prerequisite for opening up data, as such a policy defines the governments' commitment to share data. The Open Data Institute suggests the following elements of an open data policy:

• A definition of open data, a general statement of principles, an outline of the types of data and references to any relevant legislation, policy or other guidance.
• Governments are encouraged to adhere to the principle "as open as possible, as closed as necessary". If data cannot be opened for legal, privacy or other reasons, e.g. personal or sensitive data, this should be clearly explained. 

In addition, governments should also encourage researchers and the private sector in their countries to develop data management and sharing policies that adhere to the same principles.

2. Collect and collate high quality data: Existing data should be collected and stored in the same repository, e.g. from various government departments where it may have been stored in silos. Data must be accurate and not out of date. Furthermore, data should be comprehensive and should not, for example, neglect minorities or the informal economy. Data on individuals should be disaggregated where relevant, including by income, sex, age, race, ethnicity, migration status, disability and geographic location.
3. Develop open data capabilities:  These capacities address two groups:

• For civil servants, it includes understanding the benefits of open data by empowering and enabling the work that comes with open data.
• For potential users, it includes demonstrating the opportunities of open data, such as its re-use, and how to make informed decisions.

4. Prepare data for AI: If data is not only to be used by humans, but can also feed AI systems, it must meet a few more criteria to be AI-ready.

• The first step in this regard is to prepare the data in a machine-readable format.
• Some formats are more conducive to readability by artificial intelligence systems than others.
• Data must also be cleaned and labelled, which is often time-consuming and therefore costly. 

The success of an AI system depends on the quality of the training data, including its consistency and relevance. The required amount of training data is difficult to know in advance and must be controlled by performance checks. The data should cover all scenarios for which the AI system has been created.

1. Select the datasets to be opened: The first step in opening the data is to decide which datasets are to be opened. The criteria in favour of openness are:

• If there have been previous requests to open these data
• Whether other governments have opened up this data and whether this has led to beneficial uses of the data. 

Openness of data must not violate national laws, such as data privacy laws. 

2. Open the datasets legally: Before opening the datasets, the relevant government has to specify exactly under which conditions, if any, the data can be used. In publishing the data, governments may choose the license that best suits their objectives, such as the creative Commons and Open. To support the licence selection the European Commission makes available JLA - Compatibility Checkera tool that supports this decision
3. Open the datasets technically: The most common way to open the data is to publish it in electronic format for download on a website, and APIs must be in place for the consumption of this data, either by the government itself or by a third party.

Data should be presented in a format that allows for localisation, accessibility, interoperability and re-use, thus complying with the FAIR principles.

In addition, the data could also be published in a data archive or repository, which should be, according to the UNESCO Recommendation, supported and maintained by a well-established academic institution, learned society, government agency or other non-profit organisation dedicated to the common good that allows for open access, unrestricted distribution, interoperability and long-term digital archiving and preservation. 

4. Create a culture driven by open data: Experience has shown that, in addition to legal and technical openness of data, at least two other things need to be achieved to achieve an open data culture:

• Government departments are often not used to sharing data and it has been necessary to create a mindset and educate them to this end.
• Furthermore, data should, if possible, become the exclusive basis for decision-making; in other words, decisions should be based on data.
• In addition, cultural changes are required on the part of all staff involved, encouraging proactive disclosure of data, which can ensure that data is available even before it is requested. 

1. Support citizen participation: Once the data is open, it must be discoverable by potential users. This requires the development of an advocacy strategy, which may include announcing the openness of the data in open data communities and relevant social media channels. 

Another important activity is early consultation and engagement with potential users, who, in addition to being informed about open data, should be encouraged to use and re-use it and to stay involved.  

2. Supporting international engagement: International partnerships would further enhance the benefits of open data, for example through south-south and north-south collaboration. Particularly important are partnerships that support and build capacity for data reuse, whether using AI or not.
3. Support beneficial AI participation: Open data offers many opportunities for AI systems. To realise the full potential of data, developers need to be empowered to make use of it and develop AI systems accordingly.  At the same time, the abuse of open data for irresponsible and harmful AI applications must be avoided. A best practice is to keep a public record of what data AI systems have used and how they have used it.
4. Maintain high quality data: A lot of data quickly becomes obsolete. Therefore, datasets need to be updated on a regular basis. The step "Maintain high quality data" turns this guideline into a loop, as it links to the step "Collect and collate high quality data".

These guidelines serve as a call to action by UNESCO on the ethics of artificial intelligence.  Open data is a necessary prerequisite for monitoring and achieving sustainable development monitoring and achieving sustainable development. 

Due to the magnitude of the tasks, governments must not only embrace open data, but also create favourable conditions for beneficial AI engagement that creates new insights from open data for evidence-based decision-making. 

If UNESCO Member States follow these guidelines and open their data in a sustainable way, build capacity, as well as a culture driven by open data, we can achieve a world where data is not only more ethical, but where applications on this data are more accurate and beneficial to humanity.

References

https://www.unesco.org/en/articles/open-data-ai-what-now

Author : Ziesche, Soenke , ISBN : 978-92-3-100600-5

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

In the era dominated by artificial intelligence that we are just beginning, open data has rightfully become an increasingly valuable asset, not only as a support for transparency but also for the progress of innovation and technological development in general.

The opening of data has brought enormous benefits by providing public access to datasets that promote government transparency initiatives, stimulate scientific research, and foster innovation in various sectors such as health, education, agriculture, and climate change mitigation.

However, as data availability increases, so does concern about privacy, as the exposure and mishandling of personal data can jeopardize individuals' privacy. What tools do we have to strike a balance between open access to information and the protection of personal data to ensure people's privacy in an already digital future?

Anonymization and Pseudonymization

To address these concerns, techniques like anonymization and pseudonymization have been developed, although they are often confused. Anonymization refers to the process of modifying a dataset to eliminate a reasonable probability of identifying an individual within it. It is important to note that, in this case, after processing, the anonymized dataset would no longer fall under the scope of the General Data Protection Regulation (GDPR). This data.gob.es report analyzes three general approaches to data anonymization: randomization, generalization, and pseudonymization.

On the other hand, pseudonymization is the process of replacing identifiable attributes with pseudonyms or fictitious identifiers in a way that data cannot be attributed to the individual without using additional information. Pseudonymization generates two new datasets: one containing pseudonymized information and the other containing additional information that allows the reversal of anonymization. Pseudonymized datasets and the linked additional information fall under the scope of the General Data Protection Regulation (GDPR). Furthermore, this additional information must be separated and subject to technical and organizational measures to ensure that personal data cannot be attributed to an individual.

Consent

Another key aspect of ensuring privacy is the increasingly prevalent "unambiguous" consent of individuals, where people express awareness and agreement on how their data will be treated before it is shared or used. Organizations and entities that collect data need to provide clear and understandable privacy policies, but there is also a growing need for more education on data handling to help people better understand their rights and make informed decisions.

In response to the growing need to properly manage these consents, technological solutions have emerged that seek to simplify and enhance the process for users. These solutions, known as Consent Management Platforms (CMPs), originally emerged in the healthcare sector and allow organizations to collect, store, and track user consents more efficiently and transparently. These tools offer user-friendly and visually appealing interfaces that facilitate understanding of what data is being collected and for what purpose. Importantly, these platforms provide users with the ability to modify or withdraw their consent at any time, giving them greater control over their personal data.

Artificial Intelligence Training

Training artificial intelligence (AI) models is becoming one of the most challenging areas in privacy management due to the numerous dimensions that need to be considered. As AI continues to evolve and integrate more deeply into our daily lives, the need to train models with large amounts of data increases, as evidenced by the rapid advances in generative AI over the past year. However, this practice often faces profound ethical and privacy dilemmas because the most valuable data in some scenarios is not open at all.

Advancements in technologies like federated learning, which allows AI algorithms to be trained through a decentralized architecture composed of multiple devices containing their own local and private data, are part of the solution to this challenge. This way, explicit data exchange is avoided, which is crucial in applications such as healthcare, defense, or pharmacy.

Additionally, techniques like differential privacy are gaining traction. Differential privacy ensures, through the addition of random noise and mathematical functions applied to the original data, that there is no loss of utility in the results obtained from the data analysis to which this technique is applied.

Web3

But if any advancement promises to revolutionize our interaction on the internet, providing greater control and ownership of user data, it would be web3. In this new paradigm, privacy management is inherent in its design. With the integration of technologies like blockchain, smart contracts, and decentralized autonomous organizations (DAOs), web3 seeks to give individuals full control over their identity and all their data, eliminating intermediaries and potentially reducing privacy vulnerabilities.

Unlike current centralized platforms where user data is often "owned" or controlled by private companies, web 3.0 aspires to make each person the owner and manager of their own information. However, this decentralization also presents challenges. Therefore, while this new era of the web unfolds, robust tools and protocols must be developed to ensure both freedom and privacy for users in the digital environment.

Privacy in the era of open data, artificial intelligence, and web3 undoubtedly requires working with delicate balances that are often unstable. Therefore, a new set of technological solutions, resulting from collaboration between governments, companies, and citizens, will be essential to maintain this balance and ensure that, while enjoying the benefits of an increasingly digital world, we can also protect people's fundamental rights.

Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The content and views reflected in this publication are the sole responsibility of the author.

The UNA Women application offers a personalized dashboard with training options for young women according to their socioeconomic circumstances.   

The main objective of the project is to contribute to reducing the gender employment gap. For this purpose, the company ITER IDEA has used more than 6 million lines of data processed from different sources, such as data.europa.eu, Eurostat, Censis, Istat (Italian National Institute of Statistics) or NUMBEO. 

In terms of user experience, the application first asks the user to fill in a form to find out key data about the person seeking information: age, education or professional sector, training budget, etc. Once the data has been collected, the app offers an interactive map with all the training options in Europe. Each city has a panel that shows interesting data about studies, cost of living in the city, etc.

The "Stories of Use Cases" series, organized by the European Open Data portal (data.europe.eu), is a collection of online events focused on the use of open data to contribute to common European Union objectives such as consolidating democracy, boosting the economy, combating climate change, and driving digital transformation. The series comprises four events, and all recordings are available on the European Open Data portal's YouTube channel. The presentations used to showcase each case are also published.

In a previous post on datos.gob.es, we explained the applications presented in two of the series' events, specifically those related to the economy and democracy. Now, we focus on use cases related to climate and technology, as well as the open datasets used for their development.

Open data has enabled the development of applications offering diverse information and services. In terms of climate, some examples can trace waste management processes or visualize relevant data about organic agriculture. Meanwhile, the application of open data in the technological sphere facilitates process management. Discover the highlighted examples by the European Open Data portal!

Open Data for Fulfilling the European Green Deal

The European Green Deal is a strategy by the European Commission aiming to achieve climate neutrality in Europe by 2050 and promote sustainable economic growth. To reach this objective, the European Commission is working on various actions, including reducing greenhouse gas emissions, transitioning to a circular economy, and improving energy efficiency. Under this common goal and utilizing open datasets, three applications have been developed and presented in one of the webinars of the series on data.europe.eu use cases: Eviron Mate, Geofluxus, and MyBioEuBuddy.

• Eviron Mate: It's an educational project aimed at raising awareness among young people about climate change and related data. To achieve this goal, Eviron Mate utilizes open data from Eurostat, the Copernicus Program and data.europa.eu.
• Geofluxus: This initiative tracks waste from its origin to its final destination to promote material reuse and reduce waste volume. Its main objective is to extend material lifespan and provide businesses with tools for better waste management decisions. Geofluxus uses open data from Eurostat and various national open data portals.
• MyBioEuBuddy is a project offering information and visualizations about sustainable agriculture in Europe, using open data from Eurostat and various regional open data portals.

The Role of Open Data in Digital Transformation

In addition to contributing to the fight against climate change by monitoring environment-related processes, open data can yield interesting outcomes in other digitally-operating domains. The combination of open data with innovative technologies provides valuable results, such as natural language processing, artificial intelligence, or augmented reality.

Another online seminar from the series, presented by the European Data Portal, delved into this theme: driving digital transformation in Europe through open data. During the event, three applications that combine cutting-edge technology and open data were presented: Big Data Test Infrastructure, Lobium, and 100 Europeans.

• "Big Data Test Infrastructure (BDTI)": This is a European Commission tool featuring a cloud platform to facilitate the analysis of open data for public sector administrations, offering a free and ready-to-use solution. BDTI provides open-source tools that promote the reuse of public sector data. Any public administration can request the free advisory service by filling out a form. BDTI has already aided some public sector entities in optimizing procurement processes, obtaining mobility information for service redesign, and assisting doctors in extracting knowledge from articles.
• Lobium: A website assisting public affairs managers in addressing the complexities of their tasks. Its aim is to provide tools for campaign management, internal reporting, KPI measurement, and government affairs dashboards. Ultimately, its solution leverages digital tools' advantages to enhance and optimize public management.
• 100 Europeans: An application that simplifies European statistics, dividing the European population into 100 individuals. Through scrolling navigation, it presents data visualizations with figures related to healthy habits and consumption in Europe.

These six applications are examples of how open data can be used to develop solutions of societal interest. Discover more use cases created with open data in this article we have published on datos.gob.es

Learn more about these applications in their seminars -> Recordings here

Open data is a valuable tool for making informed decisions that encourage the success of a process and enhance its effectiveness. From a sectorial perspective, open data provides relevant information about the legal, educational, or health sectors. All of these, along with many other areas, utilize open sources to measure improvement compliance or develop tools that streamline work for professionals.

The benefits of using open data are extensive, and their variety goes hand in hand with technological innovation: every day, more opportunities arise to employ open data in the development of innovative solutions. An example of this can be seen in urban development aligned with the sustainability values advocated by the United Nations (UN).

Cities cover only 3% of the Earth's surface; however, they emit 70% of carbon emissions and consume over 60% of the world's resources, according to the UN. In 2023, more than half of the global population lives in cities, and this figure is projected to keep growing. By 2030, it is estimated that over 5 billion people would live in cities, meaning more than 60% of the world's population.

Despite this trend, infrastructures and neighborhoods do not meet the appropriate conditions for sustainable development, and the goal is to "Make cities and human settlements inclusive, safe, resilient, and sustainable," as recognized in Sustainable Development Goal (SDG) number 11. Proper planning and management of urban resources are significant factors in creating and maintaining sustainability-based communities. In this context, open data plays a crucial role in measuring compliance with this SDG and thus achieving the goal of sustainable cities.

In conclusion, open data stands as a fundamental tool for the strengthening and progress of sustainable city development.

In this infographic, we have gathered use cases that utilize sets of open data to monitor and/or enhance energy efficiency, transportation and urban mobility, air quality, and noise levels. Issues that contribute to the proper functioning of urban centers.

Click on the infographic to view it in full size.

Versión accesible en Word

As more of our daily lives take place online, and as the importance and value of personal data increases in our society, standards protecting the universal and fundamental right to privacy, security and privacy - backed by frameworks such as the Universal Declaration of Human Rights or the European Declaration on Digital Rights - become increasingly important.

Today, we are also facing a number of new challenges in relation to our privacy and personal data. According to the latest Lloyd's Register Foundation report, at least three out of four internet users are concerned that their personal information could be stolen or otherwise used without their permission. It is therefore becoming increasingly urgent to ensure that people are in a position to know and control their personal data at all times.

Today, the balance is clearly tilted towards the large platforms that have the resources to collect, trade and make decisions based on our personal data - while individuals can only aspire to gain some control over what happens to their data, usually with a great deal of effort. 

This is why initiatives such as MyData Global, a non-profit organisation that has been promoting a human-centred approach to personal data management for several years now and advocating for securing the right of individuals to actively participate in the data economy, are emerging. The aim is to redress the balance and move towards a people-centred view of data to build a more just, sustainable and prosperous digital society, the pillars of which would be:

• Establish relationships of trust and security between individuals and organisations.
• Achieve data empowerment, not only through legal protection, but also through measures to share and distribute the power of data.
• Maximising the collective benefits of personal data, sharing it equitably between organisations, individuals and society.

And in order to bring about the changes necessary to bring about this new, more humane approach to personal data, the following principles have been developed:

1 - People-centred control of data.

It is individuals who must have the power of decision in the management of everything that concerns their personal lives. They must have the practical means to understand and effectively control who has access to their data and how it is used and shared.

Privacy, security and minimal use of data should be standard practice in the design of applications, and the conditions of use of personal data should be fairly negotiated between individuals and organisations.

2 - People as the focal point of integration

The value of personal data grows exponentially with its diversity, while the potential threat to privacy grows at the same time. This apparent contradiction could be resolved if we place people at the centre of any data exchange, always focusing on their own needs above all other motivations.

Any use of personal data must revolve around the individual through deep personalisation of tools and services.

3 - Individual autonomy

In a data-driven society, individuals should not be seen solely as customers or users of services and applications. They should be seen as free and autonomous agents, able to set and pursue their own goals. 

Individuals should be able to securely manage their personal data in the way they choose, with the necessary tools, skills and support.

4 - Portability, access and re-use

Enabling individuals to obtain and reuse their personal data for their own purposes and in different services is the key to moving from silos of isolated data to data as reusable resources. 

Data portability should not merely be a legal right, but should be combined with practical means for individuals to effectively move data to other services or on their personal devices in a secure and simple way.

5 - Transparency and accountability

Organisations using an individual's data must be transparent about how they use it and for what purpose. At the same time, they must be accountable for their handling of that data, including any security incidents.

User-friendly and secure channels must be created so that individuals can know and control what happens to their data at all times, and thus also be able to challenge decisions based solely on algorithms.

6 - Interoperability

There is a need to minimise friction in the flow of data from the originating sources to the services that use it. This requires incorporating the positive effects of open and interoperable ecosystems, including protocols, applications and infrastructure. This will be achieved through the implementation of common norms and practices and technical standards.

The MyData community has been applying these principles for years in its work to spread a more human-centred vision of data management, processing and use, as it is currently doing for example through its role in the Data Spaces Support Centre, a reference project that is set to define the future responsible use and governance of data in the European Union.

And for those who want to delve deeper into people-centric data use, we will soon have a new edition of the MyData Conference, which this year will focus on showcasing case studies where the collection, processing and analysis of personal data primarily serves the needs and experiences of human beings.

The humanitarian crisis following the earthquake in Haiti in 2010 was the starting point for a voluntary initiative to create maps to identify the level of damage and vulnerability by areas, and thus to coordinate emergency teams. Since then, the collaborative mapping project known as Hot OSM (OpenStreetMap) has played a key role in crisis situations and natural disasters.

Now, the organisation has evolved into a global network of volunteers who contribute their online mapping skills to help in crisis situations around the world. The initiative is an example of data-driven collaboration to solve societal problems, a theme we explore in this data.gob.es report.

Hot OSM works to accelerate data-driven collaboration with humanitarian and governmental organisations, as well as local communities and volunteers around the world, to provide accurate and detailed maps of areas affected by natural disasters or humanitarian crises. These maps are used to help coordinate emergency response, identify needs and plan for recovery.

In its work, Hot OSM prioritises collaboration and empowerment of local communities. The organisation works to ensure that people living in affected areas have a voice and power in the mapping process. This means that Hot OSM works closely with local communities to ensure that areas important to them are mapped. In this way, the needs of communities are considered when planning emergency response and recovery.

Hot OSM's educational work

In addition to its work in crisis situations, Hot OSM is dedicated to promoting access to free and open geospatial data, and works in collaboration with other organisations to build tools and technologies that enable communities around the world to harness the power of collaborative mapping.

Through its online platform, Hot OSM provides free access to a wide range of tools and resources to help volunteers learn and participate in collaborative mapping. The organisation also offers training for those interested in contributing to its work.

One example of a HOT project is the work the organisation carried out in the context of Ebola in West Africa. In 2014, an Ebola outbreak affected several West African countries, including Sierra Leone, Liberia and Guinea. The lack of accurate and detailed maps in these areas made it difficult to coordinate the emergency response.

In response to this need, HOT initiated a collaborative mapping project involving more than 3,000 volunteers worldwide. Volunteers used online tools to map Ebola-affected areas, including roads, villages and treatment centres.

This mapping allowed humanitarian workers to better coordinate the emergency response, identify high-risk areas and prioritize resource allocation. In addition, the project also helped local communities to better understand the situation and participate in the emergency response.

This case in West Africa is just one example of HOT's work around the world to assist in humanitarian crisis situations. The organisation has worked in a variety of contexts, including earthquakes, floods and armed conflict, and has helped provide accurate and detailed maps for emergency response in each of these contexts.

On the other hand, the platform is also involved in areas where there is no map coverage, such as in many African countries. In these areas, humanitarian aid projects are often very challenging in the early stages, as it is very difficult to quantify what population is living in an area and where they are located. Having the location of these people and showing access routes "puts them on the map" and allows them to gain access to resources.

In this article The evolution of humanitarian mapping within the OpenStreetMap community by Nature, we can see graphically some of the achievements of the platform.

How to collaborate

It is easy to start collaborating with Hot OSM, just go to https://tasks.hotosm.org/explore and see the open projects that need collaboration.

This screen allows us a lot of options when searching for projects, selected by level of difficulty, organisation, location or interests among others.

To participate, simply click on the Register button.

Give a name and an e-mail adress on the next screen:

It will ask us if we have already created an account in Open Street Maps or if we want to create one.

If we want to see the process in more detail, this website makes it very easy.

Once the user has been created, on the learning page we find help on how to participate in the project.

It is important to note that the contributions of the volunteers are reviewed and validated and there is a second level of volunteers, the validators, who validate the work of the beginners. During the development of the tool, the HOT team has taken great care to make it a user-friendly application so as not to limit its use to people with computer skills.

In addition, organisations such as the Red Cross and the United Nations regularly organise mapathons to bring together groups of people for specific projects or to teach new volunteers how to use the tool. These meetings serve, above all, to remove the new users' fear of "breaking something" and to allow them to see how their voluntary work serves concrete purposes and helps other people.

Another of the project's great strengths is that it is based on free software and allows for its reuse. In the MissingMaps project's Github repository we can find the code and if we want to create a community based on the software, the Missing Maps organisation facilitates the process and gives visibility to our group.

In short, Hot OSM is a citizen science and data altruism project that contributes to bringing benefits to society through the development of collaborative maps that are very useful in emergency situations. This type of initiative is aligned with the European concept of data governance that seeks to encourage altruism to voluntarily facilitate the use of data for the common good.

Content by Santiago Mota, senior data scientist.

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

The European Commission's 'European Data Strategy' states that the creation of a single market for shared data is key. In this strategy, the Commission has set as one of its main objectives the promotion of a data economy in line with European values of self-determination in data sharing (sovereignty), confidentiality, transparency, security and fair competition.

Common data spaces at European level are a fundamental resource in the data strategy because they act as enablers for driving the data economy. Indeed, pooling European data in key sectors, fostering data circulation and creating collective and interoperable data spaces are actions that contribute to the benefit of society.

Although data sharing environments have existed for a long time, the creation of data spaces that guarantee EU values and principles is an issue. Developing enabling legislative initiatives is not only a technological challenge, but also one of coordination among stakeholders, governance, adoption of standards and interoperability.

To address a challenge of this magnitude, the Commission plans to invest close to €8 billion by 2027 in the deployment of Europe's digital transformation. Part of the project includes the promotion of infrastructures, tools, architectures and data sharing mechanisms. For this strategy to succeed, a data space paradigm that is embedded in the industry needs to be developed, based on the fulfilment of European values. This data space paradigm will act as a de facto technology standard and will advance social awareness of the possibilities of data, which will enable the economic return on the investments required to create it.

In order to make the data space paradigm a reality, from the convergence of current initiatives, the European Commission has committed to the development of the Simpl project.

What exactly is Simpl?

Simpl is a €150 million project funded by the European Commission's Digital Europe programme with a three-year implementation period. Its objective is to provide society with middleware for building data ecosystems and cloud infrastructure services that support the European values of data sovereignty, privacy and fair markets.

The Simpl project consists of the delivery of 3 products:

• Simpl-Open: Middleware itself. This is a software solution to create ecosystems of data services (data and application sharing) and cloud infrastructure services (IaaS, PaaS, SaaS, etc). This software must include agents enabling connection to the data space, operational services and brokerage services (catalogue, vocabulary, activity log, etc.). The result should be delivered under an open source licence and an attempt will be made to build an open source community to ensure its evolution.
• Simpl-Labs: Infrastructure for creating test bed environments so that interested users can test the latest version of the software in self-service mode. This environment is primarily intended for data space developers who want to do the appropriate technical testing prior to a deployment.
• Simpl-Live: Deployments of Simpl-open in production environments that will correspond to sectorial spaces contemplated in the Digital Europe programme. In particular, the deployment of data spaces managed by the European Commission itself (Health, Procurement, Language) is envisaged.

The project is practically oriented and aims to deliver results as soon as possible. It is therefore intended that, in addition to supplying the software, the contractor will provide a laboratory service for user testing. The company developing Simpl will also have to adapt the software for the deployment of common European data spaces foreseen in the Digital Europe programme.

The Gaia-X partnership is considered to be the closest in its objectives to the Simpl project, so the outcome of the project should strive for the reuse of the components made available by Gaia-X. 

For its part, the Data Space Support Center, which involves the main European initiatives for the creation of technological frameworks and standards for the construction of data spaces, will have to define the middleware requirements by means of specifications, architectural models and the selection of standards.

Simpl's preparatory work was completed in May 2022, setting out the scope and technical requirements of the project which have been the subject of detail in the currently open contractual process. The tender was launched on 24 February 2023. All information is available on TED eTendering, including how to ask questions about the tendering process. The deadline for applications is 24 April 2023 at 17:00 (Brussels time).

Simpl expects to have a minimum viable platform published in early 2024. In parallel, and as soon as possible, the open test environment (Simpl-Labs) will be made available for interested parties to experiment. This will be followed by the progressive integration of different use cases, helping to tailor Simpl to specific needs, with priority being given to cases otherwise funded under the Europe DIGITAL work programme.

In conclusion, Simpl is the European Commission's commitment to the deployment and interoperability of the different sectoral data space initiatives, ensuring alignment with the specifications and requirements emanating from the Data Space Support Center and, therefore, with the convergence process of the different European initiatives for the construction of data spaces (Gaia-X, IDSA, Fiware, BDVA).

1. Introduction

Visualizations are graphical representations of data that allows comunication in a simple and effective way the information linked to it. The visualization possibilities are very wide, from basic representations, such as a graph of lines, bars or sectors, to visualizations configured on dashboards or interactive dashboards. Visualizations play a fundamental role in drawing conclusions using visual language, also allowing to detect patterns, trends, anomalous data or project predictions, among many other functions.

In this section of "Step-by-Step Visualizations" we are periodically presenting practical exercises of open data visualizations available in datos.gob.es or other similar catalogs. They address and describe in a simple way the necessary stages to obtain the data, perform the transformations and analysis that are relevant to it and finally, the creation of interactive visualizations. From these visualizations we can extract information to summarize in the final conclusions. In each of these practical exercises, simple and well-documented code developments are used, as well as free to use tools. All generated material is available for reuse in the Github data lab repository belonging to datos.gob.es.

In this practical exercise, we have carried out a simple code development that is conveniently documented based on free to use tool.

Access the data lab repository on Github.

Run the data pre-processing code on Google Colab.

2. Objetive

The main objective of this post is to show how to make an interactive visualization based on open data. For this practical exercise we have used a dataset provided by the Ministry of Justice that contains information about the toxicological results made after traffic accidents that we will cross with the data published by the Central Traffic Headquarters (DGT) that contain the detail on the fleet of vehicles registered in Spain.

From this data crossing we will analyze and be able to observe the ratios of positive toxicological results in relation to the fleet of registered vehicles.

It should be noted that the Ministry of Justice makes available to citizens various dashboards to view data on toxicological results in traffic accidents. The difference is that this practical exercise emphasizes the didactic part, we will show how to process the data and how to design and build the visualizations.

3. Resources

3.1. Datasets

For this case study, a dataset provided by the Ministry of Justice has been used, which contains information on the toxicological results carried out in traffic accidents. This dataset is in the following Github repository:

• Dataset toxicological results in accidents

The datasets of the fleet of vehicles registered in Spain have also been used. These data sets are published by the Central Traffic Headquarters (DGT), an agency under the Ministry of the Interior. They are available on the following page of the datos.gob.es Data Catalog:

• Registered vehicle fleet datasets

3.2. Tools

To carry out the data preprocessing tasks it has been used the Python programming language written on a Jupyter Notebook hosted in the Google Colab cloud service.

Google Colab (also called Google Colaboratory), is a free cloud service from Google Research that allows you to program, execute and share code written in Python or R from your browser, so it does not require the installation of any tool or configuration.

For the creation of the interactive visualization, the Google Data Studio tool has been used.

Google Data Studio is an online tool that allows you to make graphs, maps or tables that can be embedded in websites or exported as files. This tool is simple to use and allows multiple customization options.

If you want to know more about tools that can help you in the treatment and visualization of data, you can use the report "Data processing and visualization tools".

4. Data processing or preparation

Before launching to build an effective visualization, we must carry out a previous treatment of the data, paying special attention to obtaining it and validating its content, ensuring that it is in the appropriate and consistent format for processing and that it does not contain errors.

The processes that we describe below will be discussed in the Notebook that you can also run from Google Colab. Link to Google Colab notebook

As a first step of the process, it is necessary to perform an exploratory data analysis (EDA) in order to properly interpret the starting data, detect anomalies, missing data or errors that could affect the quality of subsequent processes and results. Pre-processing of data is essential to ensure that analyses or visualizations subsequently created from it are reliable and consistent. If you want to know more about this process, you can use the Practical Guide to Introduction to Exploratory Data Analysis.

The next step to take is the generation of the preprocessed data tables that we will use to generate the visualizations. To do this we will adjust the variables, cross data between both sets and filter or group as appropriate.

The steps followed in this data preprocessing are as follows:

1. Importing libraries
2. Loading data files to use
3. Detection and processing of missing data (NAs)
4. Modifying and adjusting variables
5. Generating tables with preprocessed data for visualizations
6. Storage of tables with preprocessed data

You will be able to reproduce this analysis since the source code is available in our GitHub account. The way to provide the code is through a document made on a Jupyter Notebook that once loaded into the development environment you can execute or modify easily. Due to the informative nature of this post and favor the understanding of non-specialized readers, the code does not intend to be the most efficient, but to facilitate its understanding, so you will possibly come up with many ways to optimize the proposed code to achieve similar purposes. We encourage you to do so! 

5. Generating visualizations

Once we have done the preprocessing of the data, we go with the visualizations. For the realization of these interactive visualizations, the Google Data Studio tool has been used. Being an online tool, it is not necessary to have software installed to interact or generate any visualization, but it is necessary that the data tables that we provide are properly structured, for this we have made the previous steps for the preparation of the data.  

The starting point is the approach of a series of questions that visualization will help us solve. We propose the following:  

• How is the fleet of vehicles in Spain distributed by Autonomous Communities?
• What type of vehicle is involved to a greater and lesser extent in traffic accidents with positive toxicological results?
• Where are there more toxicological findings in traffic fatalities?  

Let''s look for the answers by looking at the data!  

5.1. Fleet of vehicles registered by Autonomous Communities

This visual representation has been made considering the number of vehicles registered in the different Autonomous Communities, breaking down the total by type of vehicle. The data, corresponding to the average of the month-to-month records of the years 2020 and 2021, are stored in the "parque_vehiculos.csv" table generated in the preprocessing of the starting data.  

Through a choropleth map we can visualize which CCAAs are those that have a greater fleet of vehicles. The map is complemented by a ring graph that provides information on the percentages of the total for each Autonomous Community. 

As defined in the "Data visualization guide of the Generalitat Catalana" the choropletic (or choropleth) maps  show the values of a  variable on a map by painting the areas of each affected region of a certain color. They are used when you want to find geographical patterns in the data that are categorized by zones or regions. 

Ring charts, encompassed in pie charts, use a pie representation that shows how the data is distributed proportionally.  

Once the visualization is obtained, through the drop-down tab, the option to filter by type of vehicle appears. 

View full screen visualization

5.2. Ratio of positive toxicological results for different types of vehicles

This visual representation has been made considering the ratios of positive toxicological results by number of vehicles nationwide. We count as a positive result each time a subject tests positive in the analysis of each of the substances, that is, the same subject can count several times in the event that their results are positive for several substances. For this purpose, the table "resultados_vehiculos.csv” has been generated during data preprocessing. 

Using a stacked bar chart, we can evaluate the ratios of positive toxicological results by number of vehicles for different substances and different types of vehicles. 

As defined in the "Data visualization guide of the Generalitat Catalana" bar graphs are used when you want to compare the total value of the sum of the segments that make up each of the bars. At the same time, they offer insight into how large these segments are.  

When stacked bars add up to 100%, meaning that each segmented bar occupies the height of the representation, the graph can be considered a graph that allows you to represent parts of a total. 

The table provides the same information in a complementary way.  

Once the visualization is obtained, through the drop-down tab, the option to filter by type of substance appears. 

View full screen visualization

5.3. Ratio of positive toxicological results for the Autonomous Communities

This visual representation has been made taking into account the ratios of the positive toxicological results by the fleet of vehicles of each Autonomous Community. We count as a positive result each time a subject tests positive in the analysis of each of the substances, that is, the same subject can count several times in the event that their results are positive for several substances. For this purpose, the "resultados_ccaa.csv" table has been generated during data preprocessing. 

It should be noted that the Autonomous Community of registration of the vehicle does not have to coincide with the Autonomous Community where the accident has been registered, however, since this is a didactic exercise and it is assumed that in most cases they coincide, it has been decided to start from the basis that both coincide.  

Through a choropleth map we can visualize which CCAAs are the ones with the highest ratios. To the information provided in the first visualization on this type of graph, we must add the following. 

As defined in the "Data Visualization Guide for Local Entities" one of the requirements for choropleth maps is to use a numerical measure or datum, a categorical datum for the territory, and a polygon geographic datum.

The table and bar chart provides the same information in a complementary way.   

Once the visualization is obtained, through the peeling tab, the option to filter by type of substance appears. 

View full screen visualization

6. Conclusions of the study

Data visualization is one of the most powerful mechanisms for exploiting and analyzing the implicit meaning of data, regardless of the type of data and the degree of technological knowledge of the user. Visualizations allow us to build meaning on top of data and create narratives based on graphical representation. In the set of graphical representations of data that we have just implemented, the following can be observed:  

• The fleet of vehicles of the Autonomous Communities of Andalusia, Catalonia and Madrid corresponds to about 50% of the country''s total.
• The highest positive toxicological results ratios occur in motorcycles, being of the order of three times higher than the next ratio, passenger cars, for most substances.
• The lowest positive toxicology result ratios occur in trucks.
• Two-wheeled vehicles (motorcycles and mopeds) have higher "cannabis" ratios than those obtained in "cocaine", while four-wheeled vehicles (cars, vans and trucks) have higher "cocaine" ratios than those obtained in "cannabis"
• The Autonomous Community where the ratio for the total of substances is highest is La Rioja.  

It should be noted that in the visualizations you have the option to filter by type of vehicle and type of substance. We encourage you to do so to draw more specific conclusions about the specific information you''re most interested in.  

We hope that this step-by-step visualization has been useful for learning some very common techniques in the treatment and representation of open data. We will return to show you new reuses. See you soon!