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News
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Improving knowledge transfer through enterprise social media: the mediating role of transactive memory

Industrial Management and Data Systems. 2022. Vol. 122. No. 1. P. 272–291.
Yuan S., Rong-An S., Haiyang C., Boehnke K., Hongyu J., Jindi F.

Purpose – Enterprise socialmedia can be the organizational transactive memory in which the knowledge dialogue provides users with the metaknowledge to support knowledge transfer. The purpose of this study is to examine a mediation model to show how perceived critical mass, openness and affiliation climate affect organizational knowledge transfer through the mediation of improving the metaknowledge of who knows what and whom. Design/methodology/approach – To test the mediation model and corresponding hypotheses, this study employs structural equation modeling analysis using 264 valid questionnaires. Findings – The study found the two mediators fully explained the effects of the three preconditions on knowledge transfer. Originality/value – These results help us to better understand the benefits of enterprise social media and the functions of transactive memory in organizations.

Research target: Psychology Sociology (including Demography and Anthropology Economics and Management
Language: English
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Keywords: организационный климатпередача знанийknowledge transfer Critical massEnterprise social mediaMetaknowledgeOrganizational climateTransactive memory systemкорпоративные социальные медиаметазнаниясистема трансактивной памяти
Publication based on the results of:
Values, intercultural and socio-economic interaction in the context of ethnic diversity and the COVID-19 pandemic (2022)
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