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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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От чего зависят удовлетворенность аспирантурой и намерения ее завершения у современных аспирантов?

Психологическая наука и образование. 2024. Т. 29. № 4. С. 5–15.
Marchuk L., Gordeeva T. O.

The article focuses on the role of motivational factors in the successful completion and satisfaction with postgraduate programs. PhD students (N=155,
M=26,8, SD=4,2, 63% women) participated in the study. The theoretical framework is based on self-determination theory, which provides an understanding
of the sources of autonomous and controlled motivation for research activity.
A battery of questionnaires was used to assess autonomous and controlled
motivation for postgraduate programs (the Universal Perceived Locus of Causality scales, UPLOC, and the Doctorate-related Need Satisfaction Scale, DN2S), publication activity, intentions to complete the postgraduate program,
and satisfaction with it. The results of the study demonstrated that autonomy
need satisfaction was a predictor of autonomous motivation and satisfaction
with the postgraduate program, whereas competence and relatedness needs
satisfaction were predictors of intentions to complete the postgraduate program. Path models also showed that autonomy need satisfaction is a predictor of low levels of controlled motivation, which in turn is a negative predictor
of intentions to complete the postgraduate program and satisfaction with it.
It is concluded that it is important to study and monitor motivational factors,
considering their predictive power in relation to PhD students’ performance.
Recommendations are offered to supervisors and faculty to support PhD students’ basic psychological needs for autonomy, competence, and relatedness
to foster their voluntary engagement and success in the research process.
 

Research target: Psychology
Language: Russian
Full text
DOI
Text on another site
Keywords: мотивацияmotivationудовлетворенностьавтономная мотивацияаспирантыself-determination theoryбазовые потребностиautonomous motivationконтролируемая мотивацияbasic psychological needsPhD studentsамотивацияAmotivationsatisfaction with studies
Publication based on the results of:
Existential foundations of life well-being (2024)
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