Target Audience:
Track is designed for education researchers, scientometrics specialists, and data analysts working with big data and administrative sources.
Track Focus:
We welcome submissions based on bibliometric and administrative data related to educational and scientific activities.
Suggested Topics:
●      Analysis of individual educational and research trajectories.
●      Assessment of institutional impact.
●      Investigation of global structures and trends in science and education.
Range of Topics for Discussion:
●      The transition from purely data-driven approaches in Science of Science (SciSci) to theory integration.
●      The specificity of data sources in science and education: accessibility, standardization, and bias in the comparative context of local versus global databases.
●      Challenges in transitioning from small samples to large-scale data analysis in science and education (paradigm shift, scaling issues).
●      The impact of AI on the production and systematization of scientific knowledge.
Track Concept:
Can researchers in science and education scale their studies and adapt new data analysis methods from other disciplines? Does understanding of data quality issues and source specificity hinder the transition from analyzing local contexts to identifying global trends and patterns? What is the role of theory in contemporary research on science and education, and should data-driven approaches take priority? The conference track aims to facilitate the discussion on aforementioned questions within both Russian and international contexts, as researchers navigate balancing out methodological novelty, data limitations, and ambitious research questions.

Target Audience:
This track is open to sociologists, researchers in related fields of social sciences, as well as students and PhD candidates.
Track Focus:
This conference track is meant to explore the application of contemporary computational methods and new data sources to the study of social phenomena. We invite submissions employing machine learning, social network analysis (SNA), natural language processing (NLP), agent-based modeling, and other computational tools to address both classical and emerging research questions in sociology.
We are particularly interested in research utilizing digital footprints (social media, platforms), large text corpora, administrative registries, and other non-traditional sociological sources. We also welcome methodological papers discussing the potential and challenges of digital methods.
Suggested Topics:
●      Methodological innovations and computational approaches: Network analysis of social structures and interaction dynamics; text analysis and NLP for studying discourses, narratives, and public opinion; simulation modeling of social processes (agent-based modeling, system dynamics); Large Language Models (LLMs) in social research: new opportunities and limitations; causal inference using (non)traditional data formats and machine learning methods.
●      Researching social processes and new phenomena: Analysis of social change, collective behavior, and emergent phenomena in the digital environment; computational methods for studying cultural patterns, diffusion of innovations, and social dynamics; investigation of social challenges (inequality, polarization, mobility) using new digital data.
●      Data and platforms as sources of sociological knowledge: Analysis of social media data and digital footprints to study social practices and identities; working with non-standard data: administrative registries, digital archives, sensor data; data quality issues: analysis of bias, incompleteness, and representativeness of digital sources.
Range of Topics for Discussion:
●      Critical reflection and ethical dilemmas in digital behavior research.
●      Issues of inclusivity and diversity in Computational Social Science.
●      Problems of reproducibility, validity, and Open Science principles in computational sociology.

Target Audience:
This track is designed for legal scholars and legal practitioners.
Track Focus:
This track is dedicated to the study of legal institutions using computational methods, as well as the analysis of technological solutions in legal practice.
Suggested Topics:
●      Research on judicial decisions and the legal profession using computational methods.
●      Legal Tech practices in the Russian context.
●      Data acquisition and processing for computational legal research.
●      Interdisciplinary research on legal phenomena: from extra-legal factors influencing judicial decision-making to the potential impact of Legal Tech advancements on the legal profession.

Target Audience:
This track is designed for researchers applying computational methods to data within the field of Humanities and Social Sciences.
Track Focus:
The track brings together researchers who employ various computational methods (ranging from machine learning and social network analysis to corpus linguistics and computer vision) to analyze data traditionally associated with the humanities and social sciences such as texts, images, audio recordings, and historical documents.
Suggested Topics:
●      Text analysis beyond linguistics: cultural and historical narratives.
●      Digital collections and heritage: discovering patterns in visual and audio archives.
●      Modeling and analysis of sociocultural networks.
●      Computational methods for the study of material culture and artifacts.
●      Presentations where computational methods form the core of the research design, addressing questions in philology, art history, anthropology, history, linguistics, cultural studies, and related disciplines.
●      The track aims to foster a dialogue between diverse disciplines, united by a shared methodological language and a common interest in refining computational methods for nuanced application within specific domains of knowledge.

Target Audience:
This track is designed for researchers and analysts working with spatial data, including (but not limited to) geographers, economists, and marketing specialists.
Track Focus:
The organizational committee invites both theoretical and applied papers in the field of computational geography and interdisciplinary spatial research. We particularly encourage contributions integrating spatial analysis methods with machine learning and deep learning to address research questions in the social sciences.
Suggested Topics:
●      Mapping researchers and institutions in computational geography and spatial analysis in Russia.
●      Methodological integration: combining GIS analysis, spatial econometrics, and machine learning on geospatial data.
●      Intersections and integration with other disciplines (interdisciplinarity).
Range of Topics for Discussion:
●      Global Agenda: an overview of Computational Human Geography trends, key journals, and conferences.
●      Panel Discussion: is Human Geography undergoing a "Second Quantitative Revolution"?
●      Future Scenarios: the development of the discipline and its future in the age of Artificial Intelligence.
●      Global Challenges: the role of geography in addressing key issues such as resource scarcity, migration, climate change, and geopolitical tension.
●      Discipline Formation: the emergence of Computational Human Geography as a new discipline—fragmentation of the field, achievements of Chinese scholars, and growth points in Russia (HSE University, regional universities, corporations).

Target Audience:
This track is designed for academic researchers applying econometrics, machine learning, and computational methods for economic policy analysis.
Track Focus:
We welcome research that offers quantifiable assessments of the effects of government interventions and employs modern computational methods to forecast policy outcomes and simulate alternative scenarios.
Suggested Topics:
●      Impact assessment of economic policy measures (e.g., changes in the tax burden or the introduction of new regulations) on the economy.
●      Designing economic policies using machine learning and econometric approaches.
●      Predictive modeling and simulation of alternative policy scenarios.

Target Audience:
This track is intended for researchers developing and applying mathematical and computational models to study the long-term dynamics of socioeconomic systems.
Track Focus:
We invite submissions that utilize dynamic optimization, general equilibrium theory, agent-based modeling (ABM), system dynamics, or network analysis methods to understand the evolution of economic and social processes.
Suggested Topics:
●      Modeling innovation diffusion and inequality dynamics.
●      Analysis of migration flows and sustainable development scenarios.
●      Development of new tools for analyzing complexity and transition processes in the economy.

Additionally, if your research falls within the scope of Computational Social Sciences (CSS) but does not fit into any of the specific tracks described above, you are welcome to submit your paper to the Open Session. The organizers will subsequently structure additional thematic sessions based on the submissions received.