The contribution of remote sensing data for the detection of natural selection signatures in North American Grey Wolves

The current thesis constitutes an interdisciplinary approach of detecting a selection pressure driven by the environment examining the contribution of Remote Sensing and Spatial Analysis in the field of Landscape Genetics. Even though several studies have been attempting to link genetic and environmental information so as to discover the genes that are being shaped by natural selection because of various interacted environmental factors, aspiring remote sensing derived parameters may have not been extensively exploited. This project aims to fill a part of this gap by analysing whether Remote Sensing data would provoke the emergence of significant gene-environment associations. A heterogeneous set of quantitative and qualitative data from a wide variety of sources with different data structures was collected and tested for potential associations between allelic frequencies at marker loci and environmental parameters in order to identify signatures of natural selection within genomes of North American grey wolves (Canis lupus). Emphasis was set to the inquiry of Normalized Difference Vegetation Index (NDVI) as novel candidate predictor in the evolutionary divergence of the sampled populations. The dataset that has been eventually analysed, consisted of genetic samples by microsatellites, and of two types environmental data, climatic and remote sensed (NDVI, altitude) that have been collected as monthly variables – when available – in order to scan for possible effect of seasonality on genetic data. The procession has been elaborated by Spatial Analysis Method (SAM) on 22 environmental and 523 genetic parameters. SAM requires georeferenced genetic data of the study population so as to retrieve information to characterize the sampling location and to correlate genetic parameters to one or more environmental parameters. The research is summarized in three phases. The first phase requires the desired information to be derived by the corresponding data using a Geographic Information System, so as to proceed to the second stage, which is the encoding of the acquired data and the compilation of a combination matrix with the values of the environmental parameters and the binomial information of the genetic ones. The third, and final, part included the implementation of multiple univariate logistic regressions and the computation of the association degrees between the parameters, in order to establish hypotheses about the possible force that each parameter in question could form. Comparing the two groups of environmental parameters, derived from remote sensing data and climatic data, it is concluded that climatic variables are exerting a selection pressure that could lead to genetic diversity, in contrast to vegetation index and altitude that ceased to be involved in significant associations from the first two lowest confidence levels. Vegetation index tends to shape a reduced selective power for the study area and population in question, although this is not an overall conclusion and the results denote that future researchers could arrive to an outcome that would potentially be more unambiguous by using a dataset of higher resolution and varied content. An explanation that this index is restrained from consisting a powerful candidate for natural selection lies within the computation of the NDVI values proved to be sensitive to a number of perturbing factors including clouds and cloud shadows that due to the prevailing climatic conditions of the study area are not scarce. Furthermore, the missing values of initial genetic dataset prevented the effectuation of G test, so potentially with a complete dataset and additional alleles, a greater amount and range of environmental parameters, NDVI included, would have been unveiled to be under natural selection. From the aspect of genetic data, spatial distribution of alleles should be further analysed for the acquisition of information concerning their local effects and potential emergence of spatial patterns that could unveil an environmental oriented link. Concluding, this thesis has been elaborated under a geographical information point of view, although a biologically-oriented interpretation-analysis will be realised in the context of a future publication together with specialized molecular biologist

PREDICT 2017 Country Factsheets: EU Member States – Purchasing Power Standard

The PREDICT 2017 Factsheets present essential statistical data regarding the performance of the EU ICT sector. They provide figures and tables on general economic and industry trends and R&D performance. These Factsheets are the subject of three reports. This report on ‘Purchasing Power Standard’ and the second report on ‘Data in Current Prices’ present sets of Factsheets with data on each EU Member State, in comparison to the EU average. The third report presents Factsheets on the EU and 12 non-EU countries: Australia, Brazil, Canada, China, India, Japan, Korea, Norway, Russia, Switzerland, Taiwan and the United States.JRC.B.6-Digital Econom

PREDICT 2017 Country Factsheets: EU Member States – Data in Current Prices

The PREDICT 2017 Factsheets present essential statistical data regarding the performance of the EU ICT sector. They provide figures and tables on general economic and industry trends and R&D performance. These Factsheets are the subject of three reports. This report on ‘Data in Current Prices’ and the second report on ‘Purchasing Power Standard’ present sets of Factsheets with data on each EU Member State, in comparison to the EU average. The third report presents Factsheets on the EU and 12 non-EU countries: Australia, Brazil, Canada, China, India, Japan, Korea, Norway, Russia, Switzerland, Taiwan and the United States.JRC.B.6-Digital Econom

PREDICT 2017 Country Factsheets: EU Member States – Benchmarking with Non-EU Countries

The PREDICT 2017 Factsheets present essential statistical data regarding the performance of the EU ICT sector. They provide figures and tables on general economic and industry trends and R&D performance. These Factsheets are the subject of three reports. This third report presents Factsheets on the EU in total and 12 non-EU countries: Norway, Switzerland, Australia, Brazil, Canada, China, India, Japan, Korea, Russia, Taiwan and the United States. The first report on ‘Data in Current Prices’ and the second report on ‘Purchasing Power Standard’ present sets of Factsheets with data on each EU Member State and compare it to the EU average.JRC.B.6-Digital Econom

The 2018 PREDICT Key Facts Report. An Analysis of ICT R&D in the EU and Beyond

The 2018 PREDICT Key Facts Report provides a detailed analysis of the state of ICT R&D activities in the European Union. This is the eleventh edition of a series that is published annually. Like the previous editions, an online version is available at: https://ec.europa.eu/jrc/en/predict. The report covers the period between 1995 and 2015, providing a long-term analysis of the European Union (EU) ICT sector and its R&D, covering a whole cycle from the initial expansion years, to the double recession that began in early 2008, and the most recent evolution up to 2015. Whenever possible, the report includes nowcasted data for 2016 and 2017. The statistical information provided by the figures allows comparing the ICT sector with the total economy; the ICT manufacturing sector with the ICT services sector; each of the four ICT manufacturing, two ICT services, MC and RS sectors’ behaviour; the pace followed by each EU country; and the pattern of the EU in an international context, including the most relevant countries from the perspective of the role they play in the world economy today, especially from the ICT R&D perspective.JRC.B.6-Digital Econom

Estimation of supply and demand of tertiary education places in advanced digital profiles in the EU

In order to investigate the extent to which the education offer of advanced digital skills in Europe matches labour market needs, this study estimates the supply and demand of university places for studies covering the technological domains of Artificial Intelligence (AI), High Performance Computing (HPC), Cybersecurity (CS) and Data Science (DS), in the EU27, United Kingdom and Norway. The difference between demand and supply of tertiary education places (Bachelor and Master or equivalent level) in the mentioned technological domains is referred in this report as unmet students’ demand of places, or unmet demand. Demanded places, available places and unmet demand are estimated for the following dimensions: (a) the tertiary education level in which this demand is observed: Bachelor and Master or equivalent programmes; (b) the programme’s scope, or depth with which education programmes address the technological domain: broad and specialised; and (c) the main fields of education where this tuition is offered: Business Administration and Law; Natural sciences and Mathematics; Information and Communication Technology (ICT); and Engineering, Manufacturing and Construction, with the remaining fields grouped together in a fifth category. From these estimations, it is concluded that the number of available places in the EU27, at Bachelor level, reaches 587,000 for studies with AI content, 106,000 places offered in HPC, 307,000 places in CS and 444,000 places offered in the domain of DS. At Master level this demand is comparatively lower, except for the DS domain, were it equals the offer at bachelor level. DS outnumbers AI in demand of places at Master level, with 602,000 and 535,000 demanded places, respectively. The unmet demand for AI, HPC, CS and DS in EU27 at MSc level is approximately 150,000, 33,000, 59,000 and 167,000 places, respectively. At BSc level, the unmet demand reaches 273,000, 53,000, 159,000 and 213,000 places, respectively. Another finding is that the unmet demand for broad academic programmes is higher than for specialised programmes of all technological domains and education levels (Bachelor and Master). Higher availability of places for AI, HPC, CS and DS domains is found for academic programmes taught in the ICT field of education, both at Bachelor and Master levels. For Bachelor studies, Germany and Finland are estimated as the countries with the highest unmet demand in AI, HPC, CS and DS, either with a broad or specialised scope. United Kingdom is the only studied country offering places for all fields of education and technological domains at Bachelor level and Master level. For Master studies, this is also found in Germany, Ireland, France and Portugal

The 2019 PREDICT Key Facts Report

The 2019 PREDICT Key Facts Report provides a detailed analysis of the state of ICT R&D activities in the EU. This is the twelfth edition of a series that is published annually. As the previous editions, an online version is available at: https://ec.europa.eu/jrc/en/predict. The report covers the period between 1995 and 2016, providing a long-term analysis of the European Union (EU) ICT sector and its R&D, covering a whole cycle from the initial expansion years, to the double recession that began in early 2008, and the most recent evolution up to 2016. Whenever possible, the report includes nowcasted data for 2017 and 2018. The statistical information provided by the figures allows the comparison between: the ICT sector and the total economy; the ICT manufacturing sector and the ICT services sector; the four ICT manufacturing sectors, two ICT services sectors, and MC and RS sectors; EU countries; the EU and the international context (including the most relevant countries in the world economy). The report is focused especially on the ICT R&D macroeconomic dynamics.JRC.B.6-Digital Econom

The AI Techno-Economic Segment Analysis

The Techno-Economics Segment (TES) analytical approach aims to offer a timely representation of an integrated and very dynamic technological domain not captured by official statistics or standard classifications. Domains of that type, such as photonics and artificial intelligence (AI), are rapidly evolving and expected to play a key role in the digital transformation, enabling further developments. They are therefore policy relevant and it is important to have available a methodology and tools suitable to map their geographic presence, technological development, economic impact, and overall evolution. The TES approach was developed by the JRC. It provides quantitative analyses in a micro-based perspective. AI has become an area of strategic importance with potential to be a key driver of economic development. The Commission announced in April 2018 a European strategy on AI in its communication "Artificial Intelligence for Europe", COM(2018)237, and in December a Coordinated Action Plan, COM(2018)795. In order to provide quantitative evidences for monitoring AI technologies in the worldwide economies, the TES approach is applied to AI in the present study. The general aim of this work is to provide an analysis of the AI techno-economic complex system, addressing the following three fundamental research questions: (i) Which are the economic players involved in the research and development as well as in the production and commercialisation of AI goods and services? And where are they located? (ii) Which specific technological areas (under the large umbrella of AI) have these players been working at? (iii) How is the network resulting from their collaboration shaped and what collaborations have they been developing? This report addresses these research questions throughout its different sections, providing both an overview of the AI landscape and a deep understanding of the structure of the socio-economic system, offering useful insights for possible policy initiatives. This is even more relevant and challenging as the considered technologies are consolidating and introducing deep changes in the economy and the society. From this perspective, the goal of this report is to draw a detailed map of the considered ecosystem, and to analyse it in a multidimensional way, while keeping the policy perspective in mind. The period considered in our analysis covers from 2009 to 2018. We detected close to 58,000 relevant documents and, identified 34,000 players worldwide involved in AI-related economic processes. We collected and processed information regarding these players to set up a basis from which the exploration of the ecosystem can take multiple directions depending on the targeted objective. In this report, we present indicators regarding three dimensions of analysis: (i) the worldwide landscape overview, (ii) the involvement of players in specific AI technological sub-domains, and (iii) the activities and the collaborations in AI R&D processes. These are just some of the dimensions that can be investigated with the TES approach. We are currently including and analysing additional ones.JRC.B.6-Digital Econom

THE 2020 PREDICT REPORT Key Facts Report: An Analysis of ICT R&D in the EU and Beyond

The 2020 PREDICT Key Facts Report provides a detailed analysis of the state of ICT R&D activities in the EU28 and 12 further economies worldwide. This is the 13th edition of a series that is published annually. Like the previous editions, an online version is available at: https://ec.europa.eu/jrc/en/predict. The report covers the period between 1995 and 2017, providing a long-term analysis of the European Union (EU) ICT sector and its R&D, covering a whole cycle from the initial expansion years, to the double recession that began in early 2008, and the most recent evolution up to 2017. Whenever possible, the report includes nowcasted data for 2018 and 2019. The statistical information provided by the figures allows the comparison between: the ICT sector and the total economy; the ICT manufacturing sector and the ICT services sector; the four ICT manufacturing sectors, two ICT services sectors, and MC and RS sectors; EU countries; the EU and the international context (including the most relevant countries in the world economy). The report focuses especially on the ICT R&D macroeconomic dynamics.JRC.B.6-Digital Econom

The techno-economic segment analysis of the Earth observation ecosystem

This report analyses the worldwide landscape of the Earth observation ecosystem to identify opportunities, synergies, and obstacles that need to be addressed to foster the development of a vibrant space data economy in Europe. The report uses the Techno-Economic Segment (TES) analytical approach to provide a holistic view of the EO and geospatial ecosystem in Europe and worldwide through the identification of players and key clusters of activities. It also takes into consideration the potential flows of knowledge resulting from shared activities, locations and technological fields. The approach adopts a micro-based perspective considering a wide range of both horizontal and segment specific data sources. The outcome is a compelling characterisation of the key features of this very dynamic ecosystem. The TES EO ecosystem shows a very diverse global landscape with three distinguished global hubs, namely EU28, China and the US, as possible incubators for EO-linked innovation. Those hubs have the largest number of players in case of R&D and well as in case of industry. Nevertheless, the distribution of EO activities and concentration of those activities look quite different in the three leading macro areas. As far as the R&D activities are considered, the EU28 has the highest overall number of players involved in the all types of R&D activities, but scores quite low if only the patents are taken into account. Out of the three big players, the US has the smallest number of players involved in the overall EO R&D and stable position in number of patenting. In case of China, the largest number of R&D activities is concentrated in hands of relatively few players. In conclusion, the findings of this report confirm a general expectation about the growth in the EO downstream segment. However, up to 2017 the growth has not been staggering. Since 2017, there have been continuous policy efforts to increase the uptake of EO data in order to enable market growth.JRC.B.6-Digital Econom