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This pocket data book contains the main indicators describing the trends in the development of basic general, secondary vocational, higher education, vocational training, and additional education in the Russian Federation. The publication also contains key indicators for the development of education in OECD countries. The data book includes information of the Federal State Statistics Service, Russian Ministry of Education, Russian Ministry of Science and Higher Education, Federal Treasury, Organisation for Economic Co-Operation and Development (OECD), as well as results of own methodological and analytical studies of the HSE Institute for Statistical Studies and Economics of Knowledge.

This pocket data book contains the main indicators describing the development trends of general, secondary vocational, higher education, vocational training, and additional education in the Russian Federation. The publication also covers key education indicators for the OECD countries. The data book includes information of the Federal State Statistics Service, Ministry of Education of the Russian Federation, Ministry of Science and Higher Education of the Russian Federation, Federal Treasury, Organisation for Economic Co-operation and Development (OECD), as well as results of methodological and analytical studies of the HSE Institute for Statistical Studies and Economics of Knowledge.

The paper discloses a new approach to emerging technologies identification, which strongly relies on capacity of big data analysis, namely text mining augmented by syntactic analysis techniques. It discusses the wide context of the task of identifying emerging technologies in a systemic and timely manner, including its place in the methodology of foresight and future-oriented technology analysis, its use in horizon scanning exercises, as well as its relation to the field of technology landscape mapping and tech mining. The concepts of technology, emerging technology, disruptive technology and other related terms are assessed from the semantic point of view. Existing approaches to technology identification and technology landscape mapping (in wide sense, including entity linking and ontology-building for the purposes of effective STI policy) are discussed, and shortcomings of currently available studies on emerging technologies in agriculture and food sector (A&F) are analyzed. The opportunities of the new big-data-augmented methodology are shown in comparison to existing results, both globally and in Russia. As one of the practical results of the study, the integrated ontology of currently emerging technologies in A&F sector is introduced. The directions and possible criteria of further enhancement and refinement of proposed methodology are contemplated, with special attention to use of bigger volumes of data, machine learning and ontology-mining / entity linking techniques for the maximum possible automation of the analytical work in the discussed field. The practical implication of the new approach in terms of its effectiveness and efficiency for evidence-based STI policy and corporate strategic planning are shortly summed up as well.

"Monitoring innovation activities of innovation process participants" is a project which has been carried out by the Higher School of Economics (HSE) for several years to promote monitoring and analysis of innovation issues in general, and on specific activities of its particular actors from a scientific research perspective. The project is aimed at accumulating empirical knowledge about the nature and types of interaction between various actors of the national innovation system. In 2009-2010 the study was targeted at manufacturing and service sector companies while the 2010-2011 study targeted at RD survey programme and tools to monitor Russian RD results of RD the collected data also allows to identify and systematise various factors and conditions affecting innovation activities of these organisations; eventually areas for updating the survey’s concept and tools were identified.

Recent studies of innovation behavior characteristics focus on analysis of microdata (enterprise level) as the key instrument to reveal facts and hypotheses describing the innovation activities under diverse economic, political and infrastructural conditions. This paper applies the state-of-the-art innovation modes approach [OECD, 2008] to provide insights on the Russian innovation environment, highlighting the variation of innovation strategies across sectors of Russian industry. Cross-country comparison based on the OECD data and Russian firm-level findings is presented along with the discussion of possible development of systemic instruments and evidence-based methods for policymaking.