The principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) have been put forward to guide optimal sharing of data. The potential for industrial and social innovation is vast. Domain-specific metadata standards are crucial in this context, but are widely missing in the energy sector. This report provides a collaborative response from the low carbon energy research community for addressing the necessity of advancing FAIR metadata standards. We review and test existing metadata practices in the domain based on a series of community workshops. We reflect the perspectives of energy data stakeholders. The outcome is reported in terms of challenges and elicits recommendations for advancing FAIR metadata standards in the energy domain across a broad spectrum of stakeholders.

This working paper reports the current stage of development of an agent-based model (ABM) of regional knowledge creation in Europe. Building on the scientific and conceptual foundations laid out before (see Duenser et al. 2017) the paper focuses on the specifications regarding model architecture, knowledge creation processes and collaboration procedures. The novelty of the model is in the extensive use of empirical data for the initialization and the calibration of the ABM, both at the level of firms, at the level of regions and inter-regional networks, which will enable us to apply the model to real world contexts: In policy experiments the effect of external framework conditions (especially policy interventions) on the regional knowledge creation can be simulated, in order to support the ex-ante evaluation of different policy measures in the field of regional innovation. Both the theoretical foundation and the empirical foothold of the model will raise the level of its practical relevance for policymakers at regional, national and European levels.

We describe the development of the European aerospace R&D collaboration network from 1987 to 2013 with the help of the publicly available raw data of the European Framework Programmes and the German Forderkatalog. In line with the sectoral innovation system approach, we describe the evolution of the aerospace R&D network on three levels. First, based on their thematic categories, all projects are inspected and the development of technology used over time is described. Second, the composition of the aerospace R&D network concerning organization type, project composition and the special role of SMEs is analyzed. Third, the geographical distribution is shown on the technological side as well as on the actor level. A more complete view of the European funding structure is achieved by replicating the procedure on the European level to the national level, in our case Germany.

Agglomerative clustering is a well established strategy for identifying communities in networks. Communities are successively merged into larger communities, coarsening a network of actors into a more manageable network of communities. The order in which merges should occur is not in general clear, necessitating heuristics for selecting pairs of communities to merge. We describe a hierarchical clustering algorithm based on a local optimality property. For each edge in the network, we associate the modularity change for merging the communities it links. For each community vertex, we call the preferred edge that edge for which the modularity change is maximal. When an edge is preferred by both vertices that it links, it appears to be the optimal choice from the local viewpoint. We use the locally optimal edges to define the algorithm: simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity, redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased. We apply the algorithm to model and empirical networks, demonstrating that it can efficiently produce high-quality community solutions. We relate the performance and implementation details to the structure of the resulting community hierarchies. We additionally consider a complementary local clustering algorithm, describing how to identify overlapping communities based on the local optimality condition.

Agglomerative clustering is a well established strategy for identifying communities in networks. Communities are successively merged into larger communities, coarsening a network of actors into a more manageable network of communities. The order in which merges should occur is not in general clear, necessitating heuristics for selecting pairs of communities to merge. We describe a hierarchical clustering algorithm based on a local optimality property. For each edge in the network, we associate the modularity change for merging the communities it links. For each community vertex, we call the preferred edge that edge for which the modularity change is maximal. When an edge is preferred by both vertices that it links, it appears to be the optimal choice from the local viewpoint. We use the locally optimal edges to define the algorithm: simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity, redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased. We apply the algorithm to model and empirical networks, demonstrating that it can efficiently produce high-quality community solutions. We relate the performance and implementation details to the structure of the resulting community hierarchies. We additionally consider a complementary local clustering algorithm, describing how to identify overlapping communities based on the local optimality condition.

Барбер Майкл (Michael Barber) — профессор, главный советник министра образования Великобритании по школьным стандартам (1997–2001), руководитель глобальной исследовательской программы Pearson в области образовательной политики и влияния продуктов и услуг компании на результаты обучения (Лондон). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.Доннелли Кейтлин (KatelynDonnelly) — исполнительный директор компании Pearson, глава фонда «Образование по средствам» (Лондон). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.Ризви Саад (Saad Rizvi) — Ph.D. по экономике и международным отношениям, исполнительный директор по вопросам эффективности компании Pearson (Лондон). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.В контексте бурного развития технологий и процесса глобализации, уже затронувших многие секторы мировой экономики, рассматриваются перспективы системы высшего образования. Продемонстрированы возможности, открывающиеся университетам в случае проведения ими радикальных преобразований в своих ключевых институциях, проанализированы риски, которые могут возникнуть при неосуществлении таких изменений в условиях встающих перед ними вызовов XXI в.Характеризуются модель классического университета XX в. и его функции. Рассматриваются факторы, способные коренным образом изменить парадигму классического высшего учебного заведения. Подчеркивается необходимость пересмотра университетами существующей бизнес-модели и принципа подготовки студентов. В связи с процессом маркетизации, выраженной превращением студента в потребителя, диктующего условия, и возникновением множества альтернативных университетам возможностей для талантливых студентов, отмечается возникновение потребности четкого определения университетами своего предложения, отличающего их от конкурентов, и целевой аудитории среди потенциальных студенческих групп.Описываются модели университета будущего, основанные на разделении функций существующих учебных заведений и объединении их в новых вариантах; выявляются их преимущества относительно существующей модели классического университета.Предполагается, что широкие перспективы, которые открывает высшему образованию XXI в., достижимы только в том случае, если все участники системы, от учащихся до правительства, подхватят инициативу коренных преобразований в соответствии с встающими перед ними вызовами. Ставятся наиболее важные для реализации эффективной перестройки высшего образования вопросы для каждого участника образовательной системы.

We describe the development of the European aerospace R&D collaboration network from 1987 to 2013 with the help of the publicly available raw data of the European Framework Programmes and the German Forderkatalog. In line with the sectoral innovation system approach, we describe the evolution of the aerospace R&D network on three levels. First, based on their thematic categories, all projects are inspected and the development of technology used over time is described. Second, the composition of the aerospace R&D network concerning organization type, project composition and the special role of SMEs is analyzed. Third, the geographical distribution is shown on the technological side as well as on the actor level. A more complete view of the European funding structure is achieved by replicating the procedure on the European level to the national level, in our case Germany.

We describe the development of the European aerospace R&D collaboration network from 1987 to 2013 with the help of the publicly available raw data of the European Framework Programmes and the German Förderkatalog. In line with the sectoral innovation system approach, we describe the evolution of the aerospace R&D network on three levels. First, based on their thematic categories, all projects are inspected and the development of technology used over time is described. Second, the composition of the aerospace R&D network concerning organization type, project composition and the special role of SMEs is analyzed. Third, the geographical distribution is shown on the technological side as well as on the actor level. A more complete view of the European funding structure is achieved by replicating the procedure on the European level to the national level, in our case Germany.

Барбер Майкл (Michael Barber) - профессор, главный советник по вопросам образования компании Pearson (Великобритания). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.Доннелли Кейтлин (Katelyn Donnelly) - исполнительный директор в аппарате главного советника по вопросам образования компании Pearson (Лондон). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.Ризви Саад (Saad Rizvi) - исполнительный директор в аппарате главного советника по вопросам образования компании Pearson(Лондон). Эл. адрес: krdonnelly @pearson.com Адрес: Institute for Public Policy Research, 4th Floor, 14 Buckingham Street, London WC2N 6DF, UK.Полагая, что в ближайшем будущем на позиции лидера в мировой экономике выйдет Тихоокеанский регион, авторы анализируют значение этой перспективы для системы образования региона. Для обоснования данного прогноза приводятся аналогии из истории экономического успеха стран Атлантики и анализируются изменения в экономике, в результате которых за последние полвека лидерство перешло от Атлантики к Азиатско-Тихоокеанскому региону.С учетом специфики мирового лидерства в XXI в. предлагается новая модель стимулирования инноваций на разных уровнях: каждого отдельного человека, команд, организаций и общества в целом. Отмечается, что знания о механизмах и закономерностях творчества и инноваций особенно значимы для систем образования. Подчеркивается, что благополучие человечества в течение следующих 50 лет во многом зависит от уровня образования.Выделены ключевые факторы, обусловившие успешность образовательных систем стран Азиатско-Тихоокеанского региона. Показано, что достигнутый ими прогресс не должен служить основанием для остановки в развитии образовательных систем. Рассматриваются характеристики, которыми должны обладать системы образования, чтобы обеспечить успешное мировое лидерство и инновации в предстоящие десятилетия. Чтобы произвести революцию в образовательной системе в целом, вдохновить новое поколение и взрастить глобальных лидеров, способных ответить на вызовы XXI в., авторы рекомендуют объединить оправдавшие себя методы последовательного реформирования образования в каждой стране с передовыми идеями развития системных инноваций.

An overarching concern in regional science is the characterization of interactions—such as commuter flows, transport, migration, or knowledge flows—within and between subnational spatial units. In this work, we use techniques from social network analysis to address the quality, rather than the quantity, of such interactions. Given the great current interest in European RD the fraction of the shortest paths on which an edge occurs is defined as the edge betweenness centrality. Edges with high betweenness centrality have the greatest load, are strategically positioned, and potentially can act as bottlenecks for the flows. We use this idea to evaluate knowledge flows between organizations in the European R&D network, considering several ways to relate the betweenness centrality at the level of FP project participants to knowledge flows at the NUTS2 regional level. We do so by aggregating betweenness centrality values calculated using bipartite graphs linking organizations to the FP projects in which they participate, considering annual FP data between the years 1999 and 2006. We determine the most central inter-regional knowledge flows, describe how this changes over time, and consider the implications for knowledge flows in European R&D networks. We model the centrality of the flows by means of spatial interaction models, estimating how geographical, technological, and social factors influence which region pairs become bottlenecks in the flow of knowledge. The results have meaningful implications to European R&D policy, in particular concerning which region pairs constitute the core in European R&D networks and which mechanisms drive the formation of this regional core. Keywords: European R&D networks, social network analysis, betweenness centrality, Framework Programmes JEL codes: L14, O31, R12

In line with the Europe 2020 vision, there is an increasing need for adequate analytical tools to monitor progress towards the European Research Area (ERA). The Framework Programme (FP) is the main instrument of EU research policy. With 17.5 billion euros devoted to FP6 (rising to 51billion euros in FP7), it funds a substantial proportion of collaborative research activity in the EU and is, by far, the most prominent funding mechanism for transnational research globally. Therefore, the analysis of the structure of European networks of collaboration in the FPs, from FP1 to FP6, is a valuable tool in understanding the contribution of European policies in transforming the fabric of research within the ERA, as well as in identifying a possible backbone of research actors in the ERA. Within this context, the projects "Network analysis study on participations in Framework Programmes" conducted by ARC sys (now Austrian Institute of Technology)and "Centrality Analysis in Research Networks" carried out by the Knowledge for Growth Unit of the Institute for Prospective and Technological Studies (IPTS), Joint Research Centre (JRC), European Commission, respond to the on-going need for data and analysis on the characteristics and evolution of the ERA. This report presents findings of the above-mentioned studies and discusses them in a policy context. In addition, it applies novel methodological tools for analysing data on FP participations and improving our understanding of transnational networks of collaborative research.

This study compares the spatial characteristics of industrial R&D networks to those of public research R&D networks (i.e. universities and research organisations). The objective is to measure the impact of geographical separation effects on the constitution of cross-region R&D collaborations for both types of collaboration. We use data on joint research projects funded by the fifth European Framework Programme (FP) to proxy cross-region collaborative activities. The study area is composed of 255 NUTS-2 regions that cover the EU-25 member states (excluding Malta and Cyprus) as well as Norway and Switzerland. We adopt spatial interaction models to analyse how the variation of cross-region industry and public research networks is affected by geography. The results of the spatial analysis provide evidence that geographical factors significantly affect patterns of industrial R&D collaboration, while in the public research sector effects of geography are much smaller. However, the results show that technological distance is the most important factor for both industry and public research cooperative activities.

The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 2002-2006, within the 5th Framework Program. The cooperations in this Framework Program give rise to a collaborative network, with network nodes representing actors (i.e. organizations including firms, universities, research organizations and public agencies) and network edges representing R&D projects. With this construction, participating actors are linked only through joint projects. We formally describe and analyze the network from a social networks perspective that shifts attention to the detection and analysis of the community structure within the network. Distinct communities within networks may be loosely defined as groups of actors such that there is a higher density of relations within groups than between them. In this study, we attempt to detect communities of actors solely on the basis of the relational structure within the network, and to characterize and differentiate the identified network communities by means of information-theoretic methods, community-specific profiles and the location of their major actors. We expect the results to enrich our picture of the European research area by providing new insights into the global and local structures of R&D cooperation across Europe.

We investigate research and development collaborations under the EU Framework Programs (FPs) for Research and Technological Development. The collaborations in the FPs give rise to bipartite networks, with edges existing between projects and the organizations taking part in them. A version of the modularity measure, adapted to bipartite networks, is presented. Communities are found so as to maximize the bipartite modularity. Projects in the resulting communities are shown to be topically differentiated.

We investigate the recently proposed label-propagation algorithm (LPA) for identifying network communities. We reformulate the LPA as an equivalent optimization problem, giving an objective function whose maxima correspond to community solutions. By considering properties of the objective function, we identify conceptual and practical drawbacks of the label-propagation approach, most importantly the disparity between increasing the value of the objective function and improving the quality of communities found. To address the drawbacks, we modify the objective function in the optimization problem, producing a variety of algorithms that propagate labels subject to constraints; of particular interest is a variant that maximizes the modularity measure of community quality. Performance properties and implementation details of the proposed algorithms are discussed. Bipartite as well as unipartite networks are considered.