MLIT scientific results presented at DAMDID-2025 On 29 October 2025 , Scientific Leader of the Meshcheryakov Laboratory of Information Technologies Vladimir Korenkov delivered a talk as an invited speaker at the international conference “Data Analytics and Management in Data-Intensive Domains” (DAMDID-2025)) in Saint Petersburg. The work of MLIT specialists on the automation of scientific publication management was also presented at the forum. In the report “Digital technologies and intelligent data analysis in large-scale scientific projects””, Vladimir Korenkov spoke about JINR’s extensive experience in participating in cutting-edge international experiments, its computing infrastructure, and capabilities. The JINR IT infrastructure was developed in close collaboration with CERN and other leading scientific institutions. JINR is an important part of the Worldwide LHC Computing Grid (WLCG) system for processing and storing experimental data from all experiments at the Large Hadron Collider. WLCG is capable of managing hundreds of petabytes of data, providing community-wide access to computing resources and data storage systems, integrating national and international computing structures. As the speaker highlighted, the JINR research program for the coming decades focuses on conducting ambitious and large-scale experiments at the Institute’s basic facilities and within international cooperation. The program is related to the NICA megascience project, the construction of new experimental facilities, the JINR neutrino program, the modernization of LHC experimental facilities, and programs in condensed matter physics and nuclear physics. “The success of these projects entails the further development of the IT infrastructure, including distributed grid computing, supercomputing, cloud computing, distributed data storage, as well as methods and technologies for processing and storing growing data volumes. The defining task here is training young IT specialists whose qualification will enable them not only to employ advanced information technologies, but also to independently elaborate new algorithmic and software solutions for megascience projects,” Vladimir Korenkov underlined. Another conference participant was MLIT software engineer Tatiana Zaikina. On behalf of the authors’ team, she presented a report describing a modular system developed at MLIT for automated scientific publication management, integrated into the JINR digital repository based on the DSpace software platform. The system allows one to automatically retrieve publication metadata and full texts from external sources, to verify authorship records, to eliminate duplicates, and normalize data, significantly enhancing the accuracy and completeness of the repository’s information. The functionality of the repository was modernized through data visualization: interactive histograms, one of the most common and intuitive visualization types for such systems, were implemented. This feature, developed using D3.js, expands the repository’s analytical capabilities. The proposed architecture is characterized by flexibility, scalability, and the ability to integrate into existing research infrastructures, which opens up prospects for its implementation at universities, research centers, and national libraries. Tatiana Zaikina’s talk was received with interest by the audience. Following it, many questions were asked, and a constructive discussion took place. The XXVII international conference “Data Analytics and Management in Data-Intensive Domains” is taking place on 29–31 October at Peter the Great St. Petersburg Polytechnic University. The conference was organized by the Institute of Computer Science and Cybersecurity of Peter the Great St. Petersburg Polytechnic University, the Federal Research Center “Computer Science and Control” of the RAS, and the Moscow ACM SIGMOD Chapter. MLIT researcher Irina Filozova joined the DAMDID-2025 Program Committee. The DAMDID (Data Analytics and Management in Data-Intensive Domains) conference is a multidisciplinary forum dedicated to the problems of Big Data analysis and management in various science-intensive and business domains (physics, astronomy, biology, medicine, finance, etc.).
