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In accordance with the principles of open science, the results of the research should remain available for further investigations and practical interaction with them in the research community. Such results include collected initial and obtained data, specifications and implementations of methods applicable to them including data integration rules, and other possible resources. At the same time, the results of research on data should be curated to maintain their relevance, compliance with domain development, and standards in order to make them as useful as possible in the long term. A research support system in the research domain of physics of stellar systems is being developed to provide relevant data and methods, classify them, make them findable, avoid multiple integration of heterogeneous data, and integrate them into ongoing research in the astrophysical community. New catalogues are being added on regular basis, and this process is accompanied by correct cross-identification of the entities. New observational types of binary stars with their features are added to binary star database (https://bdb.inasan.ru).

The principles known by FAIR abbreviation have been applied for different kinds of data management technologies to support data reuse. In particular, they are important for investigations and development in research infrastructures but applied in significantly different ways. These principles are recognized as prospective since, according to them, data in the context of reuse should be readable and actionable by both humans and machines. The review of solutions for data interoperability and reuse in research infrastructures is presented in the paper. It is shown that conceptual modeling based on formal domain specifications still has good potential for data reuse in research infrastructures. It allows to relate data, methods, and other resources semantically, classify and identify them in the domain, integrate and verify the correctness of data reuse. Infrastructures based on formal domain modeling can make heterogeneous data management and research significantly more effective and automated.

The article examines the influence of processes which in different measure determined the development of the Russian sociological education over the last decade. Tendencies of changing the scale and "distribution" of sociological education in the regions of the Russian Federation and in tiers of education, as well as the complexity of its interactions with the labor market, are revealed. The purpose of the article is to obtain quantitative data and logical arguments that support the thesis of optimizing sociological education in its volumes, structure, professional capabilities and graduate positions. The analysis was based on empirically measured realities of sociological education in Russia for the last 10 years. As a subject, the professional identity of graduates in sociology was determined. The authors proceeded from the fact that the tendency to reduce sociological education is due to objective demographic and economic processes, as well as state policy on the modernization of the education system in the Russian Federation. The research hypothesis consisted in the assumption that the system of interaction between sociological education and the labor market is not provided with the necessary tools, which actualizes their creation and implementation. In this regard, it was suggested that attention be drawn to the inadequacy of the professional standard of the sociologist to address the problems of harmonization of relations between sociological education and the labor market. As a more general instrument of such harmonization, the authors suggest using foreign experience in the creation and application of a sectoral framework of qualifications that involves the development of the necessary professional standards. As an empirical basis for the analysis, the official statistics of the contingent and the admission control figures for the period were used. The search for opportunities for linking labor functions in the workplace and the competencies formed during training is carried out by comparing the capabilities of the draft professional standard of the sociologist and the model of the sectoral framework of qualifications. The authors come to conclusions about the presence of a tendency to reduce sociological education in general and its concentration in the capital cities and central regions of the Russian Federation. In addition, a very positive role of the project of the professional standard of the sociologist which fixes the main types of professional activity of sociologists, to which sociologists in universities should be prepared and the opportunities that open to harmonize with the labor market of social and humanitarian education through the sectoral framework of qualifications are noted.

Russia is undergoing a socio-economic transition to a new innovative economy, which requires new systematic reform. Throughout this transformation process, Russian higher education institutions are expected to expand their research activities. Research has, however, seldom been a competitive tradition or priority in Russian universities, which has impacted upon the quality of education (Dezhina, 2011). Given the less impressive performance of Russian universities in global higher education, the Russian government has initiated a series of policies to integrate research and education, ultimately to develop world-class education and research in Russia. This paper provides an account of these national policies and approaches, and analyses issues and challenges facing Russia and its higher education sector.

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts....

Machine learning methods have become crucial to many aspects of astrophysics and cosmology. We focus on the evaluation of photometric redshifts as a template case of classification/regression problem in astronomical data mining. We discuss the general aspects of the problem and some recent work which tries to solve the issues posed by optimal feature selection, missing data and by the evaluation of probability distribution functions.

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness., Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figures