Abstract: The active growth of the use of information technologies has affected the methodology of historiographical research. This article uses the social network modeling technology for the analysis of the Russian historiography of the 2010s, devoted to various aspects of the Mari Territory and local Mari groups’ history. The information capabilities of the Scientific Electronic Library eLIBRARY.RU which contains the RSCI bibliographic database were used to achieve this aim. A selection of scientific papers on the studied subject matter was formed on the stated resource portal. It includes 627 articles from journals and conference proceedings authored by more than 270 experts in historical and related disciplines. From all the articles a pool of highly cited publications was identified, including 72 scientific papers, on the basis of which a network graph was created in the Gephi program, which allowed to visualize the connections between selected papers. The automated graph stacking helped to make 13 large clusters of publications as well as a number of "peripheral" publications. The article describes in detail the methods and technologies used in the research conduction, shows the general description of the identified "topography" of the stated subject area and its development trends, and describes the most popular research topics at the present stage. The study is novel in that it uses citation data to identify and analyze the structure of communication in this subject area. The conducted social network analysis of the scientific literature has shown that the considered subject area of modern Russian historiography demonstrates a pronounced progress in its development, as evidenced by a significant thematic expansion of research, a large and geographically wide of authors’ corpus, the involvement of a number of new historical sources, the use of new research approaches, as well as significant grant support for research.

Keywords: mari local studies, Great Patriotic World, forestry, archeology, cluster, oriented graph, Gephi program, network analysis, historiography, Mari region

Abstract: The article presents the computer reconstruction of the Albazinsky fortress in 1685. Within the framework of the study, the method of ontological coordination of the maximum available sample of source data was used, which was subsequently repeatedly used in the reconstruction of other architectural complexes. At the first step, a general topographic plan of the settlement was created, containing all the archaeological data available at the time of the study. On top of the resulting painting plan, structures directly related to the fortress fence were reproduced, while the interior of the fortress was reproduced according to the cartographic drawing "Luosha". It was decided to fill in information gaps with information about architectural analogues of the period under consideration, as well as the general norms of wooden architecture. As a result of the work, a detailed three-dimensional model of the fortress was developed and a physical model was created for the scientific museum of Amur State University. A special feature of this work is the use of modern information technology tools, as well as a systematic approach, which made it possible to accurately and reasonably reproduce the appearance of the first outpost of the Amur region. It should be noted that all sources reflect only fragmentary background information on the problem under study, but their integration allows us to obtain a qualitatively new result. It is also worth noting that the developed three-dimensional models form a library of elements that simplifies subsequent reconstructions, and the three-dimensional printing technology allows to replicate the layout. In turn, the relevance of the research topic is related not only to the large number of similar lost architectural complexes, but also to the growing interest in patriotic education and national history in general.

Keywords: 3D-model, lost architectural complex, computer design, archival documents, 3D-printing, topographic plan, archaeology, reconstruction, Amur region, Albazinsky fortress

Abstract: The object of this study is the social history of the Kingdom of Castile and Leon in the 13th–15th centuries, which covers the dynamics of interactions between religious communities and their legal, economic and social status. The subject of the study is the application of object-oriented programming based on the example of using the unified modeling language (UML) to analyze the position of Muslims in the social space of the Kingdom of Castile and Leon of the specified period. The purpose of the article is to explore the possibilities of using UML in historical science, demonstrating how this method contributes to the modeling of social and legal structures of the past, as well as the structuring of historiographical concepts explaining the problem of coexistence in medieval Spain. The research aims to use engineering methods in studying the social and economic role of the Muslim population in the context of interaction with Christians and Jews. The research methodology combines traditional historical methods with modern programming approaches, which allows for a deeper understanding and analysis of the social status of Muslims and theoretical concepts about their role in the Kingdom of Castile and Leon. The author conducted a comprehensive analysis of the academic literature justifying the use of UML in the humanities. The scientific novelty lies in the use of class diagrams to analyze the social status of Muslims in the Kingdom of Castile and Leon in the 13th–15th centuries. This modeling language allows to structure complex social relationships, reflect the hierarchy of social groups, their legal status and relationships. UML is also effective for systematizing historiographical material, helping to identify hidden relationships between concepts. At the same time, this language is not a familiar tool for historians, which may make it difficult for researchers to use it. Nevertheless, mastering the syntax of these diagrams can be promising when modeling social relations. In addition, the use of UML promotes the integration of interdisciplinary approaches, combining methods of historical science and computer science.

Keywords: UML, visualization, Digital Humanities, Muslims, religious minorities, Castile and León, coexistence, historiography, object-oriented programming, social history

Abstract: The subject of the study is the participation of Russian Muslims in the White Movement and their emigration to Far Eastern countries, as exemplified by one of the figures of the Muslim and Bashkir national movement, Muhamed-Gabdulkhay Kurbangaliev (Ishan Kurbangali). The aim of the study is to reconstruct Ishan’s biography using methods of historical informatics. The sources include materials from Kurbangaliev’s investigative cases held in the Central Archive of the FSB and the National Archive of the Republic of Bashkortostan. The digitized archival materials were transformed into a database. To visualize Kurbangaliev’s spatial mobility, geodata mapping was performed using Google Maps services. His biography is examined within the framework of the "transborder biographies" methodology, rejecting binary categorizations ("us/them") in favor of hybridity (S. Conrad). A multi-layered geodatabase was created, combining spatial and attributive characteristics, and including descriptions of events, 204 locations, and 507 individuals. A geochronicle of Kurbangaliev’s life was constructed. Analysis of the geodata identified the main centers of Kurbangaliev’s activity – Chita, Manchuria, and Japan. An examination of his contacts in Tokyo revealed a core network of 12 individuals: imams, General Staff officers, representatives of Japanese ruling circles, and members of the Ottoman dynasty. This study presents Kurbangaliev within the context of multiple intersecting identities – religious, national, and political. It is proven that his activities in Japan (establishing a mosque, publishing a journal, and maintaining ties with Japanese politicians) contributed to the institutionalization of Islam in the region and stimulated discussions among Japanese Muslims about the Religious Organizations Law (April 8, 1939). The analysis of archival materials using methods of historical informatics, geodata mapping, and the "transborder biographies" methodology not only reconstructed Kurbangaliev’s biography but also situated it within the broader context of 20th-century transnational processes.

Keywords: Manchuria, Russia, Islam, Japan, Emigration, White movement, Mukhamed-Gabdulkhay Kurbanagaliev, Mapping, Geodatabase, Siberia

Abstract: The subject of this study is the development and implementation of a digital information-analytical resource "Orthodox Landscape of the Taiga Siberia: Actors, Institutions, Networks," intended for studying the formation of Siberian society in the 19th–20th centuries. The core informational content of the resource is formed from data on settlement, economic, and religious statistics, materials from the "Tomsk Diocesan Bulletin," as well as texts of liturgical, didactic, and polemical works from parish and Old Believer libraries. The process of developing the information resource includes the creation of a system for long-term storage and recovery of data, the unification and standardization of uploaded data, as well as the development of tools for analytical processing and modification of the data. Within the framework of the project, several problems related to the heterogeneity of measurement systems and the historical variability of toponyms have been addressed. The research is based on an interdisciplinary approach to the Siberian region as a cross-border territory with network forms of ethnoconfessional groups and identities. Methodologically, the work relies on the development of solutions for integrating heterogeneous historical geodata and ensuring their long-term storage and analytical processing. The scientific novelty lies in the development of a comprehensive approach to the digitalization of humanitarian studies, providing the systematization of diverse sources, their long-term storage, and integration into modern research infrastructure. An architecture of the information system has been developed, focused on supporting decentralized data storage while maintaining control for the owners. Problems of heterogeneity of measurement systems in historical sources and variability of toponyms have been addressed through the creation of a unique identification system for geographic objects. Standardization and formatting protocols for uploaded data have been implemented, ensuring their compatibility with modern geoinformation services. Microservices for automated processing have been realized, guaranteeing the integrity of the informational array. The interactive interface of the system provides researchers access to analytical tools without the need for specialized competencies in information technology and GIS systems.

Keywords: historical geoinformatics, Orthodox landscape, historical informatics, digitalization of the humanities, long-term data storage, GIS, spatial analysis, geographic information systems, integration of historical geodata, taiga Siberia

Abstract: Over the last decade, artificial intelligence (AI) technologies have become one of the most sought-after areas of scientific and technological development. This process has also impacted historical science, where the first research in this area began in the 1980s (the so-called first wave) – both in our country and abroad. Then came the "AI winter," and at the beginning of the 2010s, the "second wave" of AI emerged. The subject of this article is the new opportunities for applying AI in history and the new problems arising in this process today, when the main focus of AI has shifted to artificial neural networks, machine learning (including deep learning), generative neural networks, large language models, etc. Based on the experience of historians applying AI, the article proposes the following seven directions for such research: recognition of handwritten and old printed texts, their transcription; attribution and dating of texts using AI; typological classification and clustering of data from statistical sources (particularly using fuzzy logic); source criticism tasks, data completion and enrichment, and reconstruction using AI; intelligent search for relevant information, utilizing generative neural networks for this purpose; using generative networks for text processing and analysis; and the use of AI in archives, museums, and other institutions that store cultural heritage. An analysis of the discussion of similar issues organized by the leading American historical journal AHR has been conducted. These are conceptual questions regarding the interaction between humans and machines ("historian in the world of artificial neural networks"), the possibilities for historians to use machine learning technologies (particularly deep learning), various AI tools in historical research, as well as the evolution of AI in the 21st century. Practical aspects were also touched upon, such as the experience of recognizing newspaper texts from past centuries using AI. In conclusion, the article addresses the problems related to the use of generative neural networks by historians.

Keywords: algorythms, text atribution, image recognition, generative neural networks, deep learning, machine learning, artificial neural networks, Artificial Intelligence, data, historical source

Abstract: The article is devoted to the controversial problem of the use of artificial intelligence in historical research. The introduction briefly examines the history of the emergence of "artificial intelligence" (AI) as a field in computer science, the evolution of this definition and views on the application of AI; analyzes the place of artificial intelligence methods at different stages of specific historical research. In the main part of the article, based on the analysis of historiographical sources and his own experience of participating in foreign projects, the author analyzes the practice of implementing handwritten text recognition projects using various information technologies and AI methods, in particular, describes and justifies the requirements for creating electronic copies of recognizable sources, the need to take into account the texture of information carriers, writing materials, techniques and technologies for creating the text; varieties and methods of creating paleographic, codicological, diplomatic datasets, historical and lexicological dictionaries, the possibility of using large language models, etc. As a methodological basis, the author used a systematic approach, historical-comparative, historical-chronological and descriptive methods, as well as the analysis of historiographical sources. In conclusion, it is concluded that the use of artificial intelligence technologies is promising not only as an auxiliary tool, but also as research methods that help in establishing the authorship of historical sources, clarifying their dating, detecting forgeries, etc., as well as in creating new types of scientific reference search systems for archives and libraries. At the same time, the use of artificial intelligence technologies is highly expensive and capital intensive, which is a serious obstacle to the widespread introduction of these technologies into the practice of historical research.

Keywords: large linguistic models, datasets, historical lexicology, diplomatics, codicology, paleography, automated text recognition, historical sources, artificial intelligence, information technologies

Abstract: In the presented work, the object of research is the "Notes of the Decembrist I.I. Gorbachevsky", which are one of the most valuable sources on the history of the Decembrist movement, created by its participants themselves. They highlight the formation and development of such a Decembrist organization as the Society of United Slavs, which later joined the Southern Society of Decembrists. Written in exile in Siberia, these notes represent not only a source of factual material, but also an original concept of the secret society's development, and a retrospective "inside look" at the mistakes made by the conspirators. However, Gorbachevsky's "Notes" are notable for another circumstance. Contrary to their well-established name in literature, we cannot unequivocally assert that their author was I.I. Gorbachevsky himself from among the Decembrists. The fact is that the first publication of the "Notes" – in the journal "Russian Archive" in 1882 – was presented under the heading "Notes of an Unknown Person from the Society of the United Slavs." The subject of the research in the presented work is the question of the authorship of the "Notes", which has no clear answer among historians today. In this paper, we propose a solution to the problem of determining the authorship of the "Notes of the Decembrist I.I. Gorbachevsky" using machine learning methods. I.I. Gorbachevsky himself, as well as the Decembrist P.I. Borisov, are considered as possible authors. The novelty of the research lies in the fact that machine learning methods were used to determine the authorship of the "Notes". The authors trained four types of models to predict the authorship of each of the sentences in the Notes. As a result, most of the proposals of the "Notes" were assessed as written by Gorbachev. The largest percentage of offers, 69.2%, was attributed to Gorbachev by the Count Vectorizer + SVC model. The accuracy of all models exceeded 80% on average, while those based on BERT coding averaged close to 90%. The main conclusion of the work, therefore, can be considered that the "Notes" were more likely to have been written by I.I. Gorbachevsky than by P.I. Borisov. The methods used in the framework of the presented study provide another argument in favor of this version. The code and dataset are available at the link: https://github.com/WLatonov/Gorbachevskiy_notes .

Keywords: Gorbachevskiy's notes, The Decembrists, BERT, Binary classification, Neural networks, Machine learning, Stylometry, Attribution, authorship definition, Gorbachevskiy's letters

Abstract: The subject of the research is the articles of the official printed organ of the Russian Ministry of Finance – the journal "Bulletin of Finance, Industry and Trade" – for the year 1917. Undoubtedly, this year was a turning point in domestic history. In this regard, it is important to use new approaches to uncover the informational potential of this largely unique source, which contains valuable information about the country's economy (including not only those areas highlighted in the journal's title but also, for example, about tax and customs policy, as well as preparations for a number of reforms, including agrarian reforms). Moreover, it is necessary to take into account that during this period the journal was published against the backdrop of the ongoing First World War, and the related issues were also reflected in its pages. Methodologically, the article is based on computerized content analysis. The main focus is on artificial intelligence tools within the specialized software MAXQDA. The novelty of the research lies in the fact that for the first time the capabilities of the AI Assist module and its latest component, MAXQDA Tailwind, which was in the beta version at the time of the article's publication, have been tested. The author received early access to all product features by invitation from the developers and provided feedback based on the work outcomes. The international virtual conference of MAXQDA users (MAXDAYS 2025), where the functionality of MAXQDA Tailwind will be presented, will take place on March 18-19 of this year. Thus, readers will be able to familiarize themselves with it before its official release. The article proves that artificial intelligence in no way replaces the historian but can assist them in deepening and making the analysis of historical sources more comprehensive.

Keywords: February Revolution, First World War, official press organ, AI Assist, MAXQDA Tailwind, artificial intelligence, MAXQDA, content analysis, Media, Bulletin of Finance

Abstract: The authors studied the prospects of using artificial intelligence (AI) technologies to create and develop a digital archival environment, as well as their impact on the optimization and automation of archived data management processes. The main purpose of the work is to analyze modern digital solutions aimed at improving the processes of storing, searching and processing archival documents (including handwritten, damaged, multilingual). The paper explores key technologies used in digital archives, including intelligent scanning, natural language processing (NLP), computer vision, machine learning, and intelligent search methods. Special attention is paid to the problems of loss of archival materials, the need to restore them, ensure data security and accessibility, which is especially important in an unstable political situation and limited resources for new territories. The research is based on a systematic analysis of modern information technologies and their application in the archival business. The work uses methods of comparative analysis, classification and forecasting, which allows us to identify key areas of AI implementation in the archival field. The novelty of the work lies in an integrated approach to analyzing the use of AI in the archival field, identifying problematic aspects of archive digitalization, and proposing automation of the processes of storing, processing, and searching archival data. It is concluded that artificial intelligence technologies can significantly improve the efficiency of archives, providing accelerated document processing, intelligent classification, data protection and convenient access to information. In addition, the need to develop new algorithms based on machine learning is emphasized, which will improve the recognition of handwritten texts, the processing of corrupted documents and multilingual archival materials. The introduction of such technologies is becoming an important part of the digital transformation strategy of archival affairs and plays a key role in preserving historical heritage.

Keywords: machine learning, computer vision, natural language processing, data security, intelligent scanning, predictive intelligence, digital transformation, artificial intelligence, digital archival environment, archives

Abstract: The subject of the study is the corpus of children's literature from the collection of the Minusinsk Public Library of the late XIX – early XX century, consisting of 121 works written between 1719 and 1905. These texts are a significant source for studying the formation of geographical perception among residents of a provincial Siberian city through fiction. Special attention is paid to the analysis of geographical names (toponyms) found in texts in order to identify their frequency and geographical distribution. This allows us to reconstruct the picture of the world presented in the books of that time and understand how it was perceived by the children's audience, forming their idea of countries, cities and cultural centers. The research is aimed at studying the role of children's literature as a cultural tool that reflects and forms geographical representations, as well as at identifying methodological challenges and limitations when working with historical buildings. The methodological basis includes bringing pre-reform texts to a machine-readable form using digitization tools and geoparsing to automatically identify geographical entities. The Spacy library was used for the analysis, followed by manual verification and correction of the data. The results of the study include the identification of 668 cities and 97 countries represented in the texts, as well as the construction of a cartographic visualization of the frequency distribution of mentions. The analysis revealed an uneven distribution of geographical names in various texts, where mentions of Russia, Poland and England prevail among countries, and Kiev, Moscow and St. Petersburg among cities. The scope of the results includes research in the field of digital humanities, library science and historical and cultural studies. The novelty of the work lies in the use of modern geoparsing methods for processing Russian-language texts of pre-reform spelling and in the analysis of the previously unexplored literature corpus of the Minusinsk Library. The conclusions emphasize the importance of text mapping for understanding the formation of geographical perception and the need for further development of NER tools for complex corpora. Despite the limitations, the research contributes to the development of NLP methods for historical texts.

Keywords: Pre-reform orthography, Minusinsk Public Library, Children's literature, World map, Minusinsk, Siberia, Mapping, Named-entity recognition, Historical Computer Science, Geoparsing

Abstract: The article presents the results of a study of the previously unexplored history of land ownership in the volosts of Belaya – an important region on the Russian-Lithuanian border in the 14th to 17th centuries. The subject of the research is the geography of land ownership, including its distribution, sizes, ratio of different types, and developed land. The sources of the study were the census books of the Belsky district from the second half of the 17th century and the act materials from the first half of the 17th century – the Polish period in the history of Belaya. These sources contain information not only about landowners of this period but also from the earlier Moscow period (the second half of the 16th – early 17th centuries). In the work on localizing the toponyms of the 17th-century sources, cartographic materials from the General Survey of the 18th century were used. Geoinformation technologies were applied in the study. The geoinformation project developed in the NEXTGIS environment allowed processing a large volume of historical and geographical information. The integration of data from various sources enabled a retrospective analysis of the dynamics of land ownership in the Belsky volosts in the 16th-17th centuries for the first time. The boundaries of land allotments allowed for the visualization of the limits of land holdings in the 16th-17th centuries. The geography of palace and noble land ownership was characterized. Formed from confiscated lands, mainly from the Vitebsk Jesuit college, palace land ownership in the Belsky district in the 1650s to 1670s was only slightly inferior in size to that of the nobility and the Cossacks. The continuity of a number of land holdings is traced in two aspects. Firstly, this is the continuity of the borders of land holdings of the Russian nobility in the second half of the 16th – early 17th centuries and the estates of the nobility in the 17th century. In particular, the holdings of the Tatev, Travin, and Temirev families from the 16th century remained within their borders for the nobility both during the Polish period and after Belaya was incorporated into the Russian state. Secondly, there is continuity in the holdings of the nobility in the first and second halves of the 17th century. The ownership of the same estates by representatives of noble families, such as the Poplonsky, Rachinsky, and others, is traced. The continuity of the geographical boundaries of land ownership likely accompanied the preservation of the parameters of land development. A significant factor in the preservation of archaic features was the presence of extensive forest and wetland areas. The data obtained significantly enrich our understanding of the historical geography of the Russian borderlands.

Keywords: Polish-Lithuanian Commonwealth, szlachta, palace land, map, geoinformation technologies, volost’, uezd, land ownership, Russian state, historical geography

Abstract: The aim of this article is to explore the role of the Russian capital’s credit market in lending to regional businesses, citing the Moscow branch of the St Petersburg International Commercial Bank as an example of a commercial bank that provides services such as loans and cash credit. The research is based on the branch’s promissory note book for 1900 from the bank's archive at the Central State Archive in Moscow. This document records customer lending transactions, specifically data on promissory notes purchased by the bank branch for its customers. The emphasis is on the geographical aspects of lending, such as the places where bills were issued and settled, considering the industrial composition of the clients and the relationships between the largest clients and among themselves. To achieve a comprehensive analysis we used relational database technology alongside statistical and geoinformation analysis methods throughout this study. As a result, we demonstrated that business transactions were conducted on credit in the regions in the form of promissory notes in 242 settlements. These bills subsequently came to Moscow, where the bank purchased them, enabling entrepreneurs, firms and companies to access funds ahead of the due date indicated on the bill. The priority was given to large companies associated with the bank, which are involved in the oil industry (production, refining, transportation, and trade), as well as metallurgy and mechanical engineering. However, the funds available from the Moscow money market allowed for a much broader range of clients across various sectors of trade, industry and services. This development led to Moscow becoming a central point for interaction between the bank and its clients who were spread across the country.

Keywords: Moscow, geoinformation analysis, metallurgy, statistical analysis, oil industry, interregional relationships, Moscow money market, Russian Empire, bank credit, commercial banks