Exploring the depths of the global earth observation system of systems

Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data, especially for managing satellite time series. These infrastructures build on the concept of multidimensional data model (data hypercube) and are complex systems engaging different disciplines and expertise. For this reason, their interoperability capacity has become a challenge in the Global Change and Earth System science domains. To address this challenge, there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features. In this respect, a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain. This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas, with the final goal of enabling and facilitating interoperability. It introduces six modeling views, each of them is described according to: its main concerns, principal stakeholders, and possible patterns to be used. The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional "cubes" along with the more recent and analogous development in the Earth Observation domain, and puts forward a set of interoperability recommendations based on the modeling views

Modelling Inventory and Knowledge Management System of the European Commission (MIDAS)

The Modelling Inventory and Knowledge Management System of the European Commission (MIDAS) is a Commission-wide knowledge management tool for modelling, enabling enhanced transparency and traceability of models in use for EC policy making. It forms an integral part of the Competence Centre on Modelling (CC-MOD) of the Joint Research Centre of the European Commission (JRC). This document describes MIDAS, by providing a bird's-eye view of the MIDAS content, architecture, and functionality, and identifying the benefits of the system for the organisation and in the context of the Better Regulation Agenda.JRC.I.2-Foresight, Modelling, Behavioural Insights & Design for Polic

Semantic Text Analysis tool: SeTA

An ever-growing number and length of documents, number and depth of topics covered by legislation, and ever new phrases and their slowly changing meaning, these are all contributing factors that make policy analysis more and more complex. As implication, human policy analysts and policy developers face increasing entanglement of both content and semantical levels. To overcome several of these issues, JRC has developed a central pilot tool called AI-KEAPA to support policy analysis and development in any domain. Recent developments in big data, machine learning and especially in natural language processing allow converting unfathomable complexity of many hundreds of thousands of documents into a normalised high-dimensional vector space preserving the knowledge. Unstructured text in document corpora and big data sources, until recently considered just an archive, is quickly becoming core source of analytical information using text mining methods to extract qualitative and quantitative data. Semantic analysis allows us to extract better information for policy analysis from metadata titles and abstracts than from the structured human-entered descriptions. This digital assistant allows document search and extraction over many different sources, discovery of phrase meaning, context and temporal development. It can recommend most relevant documents including their semantic and temporal interdependencies. But most importantly, it helps bursting knowledge bubbles and fast-learning new domains. This way we hope to mainstream artificial intelligence into policy support. The tool is now fit for purpose. It was thoroughly tested in real-life conditions for about two years mainly in the area of legislative impact assessments for policy formulation, and other domains such as large data infrastructure analysis, agri-environmental measures or natural disasters, some of which are detailed in this document. This approach boosts the strategic JRC focus on application of scientific analysis and development. This service adds to the JRC competence and central position in semantic reasoning for policy analysis, active information recommendation, and inferred knowledge in policy design and development.JRC.I.3-Text and Data Minin

DigiTranScope: some key findings

Digitranscope originated from the JRC Strategy 20301. The strategy identified ten strategic topics on which the JRC should concentrate to anticipate future policy requests. One of these topics was ‘Data and Digital Transformation’, to which the JRC set up two initiatives: the first being a transversal project on ‘Artificial Intelligence and Digital Transformation’, the second being a CAS research project on digital transformation, which was to be more exploratory in nature. The CAS project originally proposed to address two key issues: i) how the information glut triggered by digital transformation reverses the cognitive balance between humans and machines, and ii) the impact of digital information technology on the rules and institutions that guide modern societies. This proposal therefore led to the establishment of two projects in 2017: ‘Human behaviour and machine intelligence’ (HUMAINT)2 and our project, ‘Digital transformation and the governance of human society’ (Digitranscope)

DigiTranScope: the governance of digitally-transformed society

This volume presents the key outcomes and research findings of the Digitranscope research project of the European Commission Joint Research Centre. The project set out to explore during the period 2017-2020 the challenges and opportunities that the digital transformation is posing to the governance of society. We focused our attention on the governance of data as a key aspect to understand and shape the governance of society. Data is a key resource in the digital economy, and control over the way it is generated, collected, aggregated, and value is extracted and distributed in society is crucial. We have explored the increasing awareness about the strategic importance of data and emerging governance models to distribute the value generated more equitably in society. These findings have contributed to the new policy orientation in Europe on technological and data sovereignty and the sharing of data for the public interest. The digital transformation, the rise of artificial intelligence and the Internet of Things offer also new opportunities for new forms of policy design, implementation, and assessment providing more personalised support to those who need it and being more participative throughout the policy cycle. The use of digital twins, gaming, simulation, and synthetic data are just at their beginning but promise to change radically the relationships among all the stakeholders in governance of our society

DigiTranScope: some key findings

Digitranscope originated from the JRC Strategy 20301. The strategy identified ten strategic topics on which the JRC should concentrate to anticipate future policy requests. One of these topics was ‘Data and Digital Transformation’, to which the JRC set up two initiatives: the first being a transversal project on ‘Artificial Intelligence and Digital Transformation’, the second being a CAS research project on digital transformation, which was to be more exploratory in nature. The CAS project originally proposed to address two key issues: i) how the information glut triggered by digital transformation reverses the cognitive balance between humans and machines, and ii) the impact of digital information technology on the rules and institutions that guide modern societies. This proposal therefore led to the establishment of two projects in 2017: ‘Human behaviour and machine intelligence’ (HUMAINT)2 and our project, ‘Digital transformation and the governance of human society’ (Digitranscope)

CAS: Centre for advanced studies

An introduction to the Centre for Advanced Studies.JRC.A.5-Scientific Developmen

Academic offer and demand for advanced profiles in the EU

This study aims at supporting the policy initiatives to ensure the availability in EC Member States of adequate advanced digital skills in a number of IT domains including Artificial Intelligence, High Performance Computing and Cybersecurity. By making use of the Techno-Economic Segments (TES) analytical approach developed under the PREDICT3 project, the study collects data and builds quantitative indicators to provide a mapping of digital skills in the mentioned technological domains from two complementary perspectives: the existing offer of academic programmes (bachelor, master and doctoral programs), and the demand of profiles by the industry, as reflected by industry activity in the referred fields.JRC.B.6-Digital Econom

AI Watch : AI Uptake in Health and Healthcare, 2020

This document presents a sectoral analysis of AI in health and healthcare for AI Watch, the knowledge service of the European Commission monitoring the development, uptake and impact of Artificial Intelligence for Europe. Its main aim is to act as a benchmark for future editions of the report to be able to assess the changes in uptake and impact of AI in healthcare over time, in line with the mission of AI Watch. The report recognises that we are still at an early stage in the adoption of AI and that AI offers many opportunities in the short term for improved efficiency in administrative and operational processes and in the medium-long term for clinical applications, patients’ care, and increased citizen empowerment. At the same time, AI applications in this sensitive sector raise many ethical and societal issues and shaping the direction of development so that we can maximise the benefits whilst reducing the risks is a key issue. In the global context, Europe is well positioned with a strong research base and excellent health data, which is the pre-requisite for the development of beneficial AI applications. Where Europe is less well placed is in translating research and innovation into industrial applications and in venture capital funding able to support innovative companies to set themselves up and scale up once successful. There are however noticeable exception as the case of the BioNTech that is leading the development of one of the COVID-19 vaccines. It should also be noted that in AI-enabled health start-ups, many of them are in the area of drug discovery, i.e. the domain of BioNTech. Investment in education and training of the healthcare workforce as well as creating environments for multidisciplinary exchange of knowledge between software developers and health practitioners are other key areas. The report recognizes that there are many important policy developments already in the making that will shape future directions, including the European Strategy for Data which is setting up a common dataspace for health, a riskbased regulatory framework for AI to be put in place by the end of 2020, and the forthcoming launch of the Horizon Europe programme as well the Digital Europe Programme with large investments in AI, computing infrastructure, cybersecurity and training. The COVID-19 crisis has also acted as a booster to the adoption of AI in health and the digital transition of business, research, education and public administration. Furthermore, the unprecedented investments of the Recovery Plan agreed in July 2020 may fuel development in digital technologies and health beyond expectation. We are therefore at the junction of a potentially extraordinary period of change which we will be able to measure in future years against the baseline set by this report.JRC.B.6-Digital Econom

Artificial Intelligence and Digital Transformation: early lessons from the COVID-19 crisis

The COVID-19 pandemic has created an extraordinary medical, economic and social emergency. To contain the spread of the virus, many countries adopted a lock down policy closing schools and business and keeping people at home for several months. This resulted in a massive surge of activity online for education, business, public administration, research, social interaction. This report considers these recent developments and identifies some early lessons with respect to the present and future development of AI and digital transformation in Europe, focusing in particular on data, as this is an area of significant shifts in attitudes and policy. The report analyses the increasing use of AI in medicine and healthcare, the tensions in data sharing between individual rights and collective wellbeing, the search for technological solutions like contact tracing apps to help monitor the spread of the virus, and the potential concerns they raise. The forced transition to online showed the resilience of the Internet but also the disproportionate impact on already vulnerable groups like the elderly and children. The report concludes that the COVID-19 crisis has acted as a boost for AI adoption and data sharing, and created new opportunities. It has also amplified concerns for democracy and social inequality and showed Europe’s vulnerability on data and platforms, calling for action to address these crucial aspects.JRC.B.6-Digital Econom