Статья 'Историческая информатика в контексте науки о данных (по материалам круглого стола)' - журнал 'Историческая информатика' - NotaBene.ru



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Historical informatics

Historical Information Science in the Context of Data Science (Round Table Materials)

Borodkin Leonid

Doctor of History

Professor, Historical Information Science Department, Faculty of History, Lomonosov Moscow State University

aud. 454,  27-4, ul. Lomonosovskii Prospekt, g. Moscow, Russia, 119991

Другие публикации этого автора

Vladimirov Vladimir

Doctor of History

Professor, the head of document studies, archival studies and historical information science department at Altai State University 

656049, Russia, Altaiskii krai, g. Barnaul, prospekt Lenina, 61, aud. 312

Другие публикации этого автора




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The article focused on problems and prospects caused by the rapid development of data science and discusses the opinions and remarks made by the participants of the round table called "Methods and Technologies of Data Science: Its Prospects in Historical Research" held by the editorial board of the "Historical Information Science” journal " and the Association "History and Computer" on July 3, 2020. The round table was attended by over 60 teachers, researchers, as well as students from 5 countries. Online discussions and talks were assisted by Zoom video conferencing service. The participants addressed issues related to the term "data science" itself, artificial intelligence and big data issues. All these were discussed in the light of problems that arise and are solved in the framework of historical research. The speakers addressed the problem of historical sources digitization and text recognition, the opportunities of programming languages (R and Python) use as well as many other issues. The very fact of the round table and its results have demonstrated the undeniable usefulness of the dialogue and the need to develop this form of scientific contacts. It is emphasized that that the development of such a field as "digital historical source studies" is becoming more and more urgent.

Keywords: artificial neural networks, machine learning, artificial intelligence, big data, historical information science, data science, mathematical statistics, programming, digitization, image recognition
This article written in Russian. You can find full text of article in Russian here .

Borodkin L.I. Metody iskusstvennogo intellekta: novye gorizonty istoricheskogo poznaniya // Informatsionnyi Byulleten' Komissii po primeneniyu matematicheskikh metodov i EVM v istoricheskikh issledovaniyakh pri otdelenii istorii RAN. 1992. № 5. S. 4-16.
Borodkin L.I. Stanovlenie istoricheskoi informatiki v Rossii: pervye shagi istorikov na puti «mikrokomp'yuternoi revolyutsii» // Istoricheskaya informatika. – 2017. – №3. – S. 155-172. DOI: 10.7256/2585-7797.2017.3.24709 URL: https://nbpublish.com/library_read_article.php?id=24709
Carvalho J. Expert Systems and Community Reconstruction Studies // History and Computing II / P. Denley, S. Fodelvik, and Ch. Harvey (eds.). Manchester University Press, 1989.-290 p.
Borodkin L.I., Koval'chenko I.D. Dva puti burzhuaznoi agrarnoi evolyutsii v Evropeiskoi Rossii (Opyt mnogomernogo tipologicheskogo analiza) // Agrarnaya evolyutsiya Rossii i SShA v XIX-nachale XX v. M., 1991. S.18-47.
Tsai, Richard Tzong-Han; Lu, Yi-Hsuan; Wang, Yu-Chun; Fan, I-Chun. Event Extraction on Classical Chinese Historical Texts: A Case Study of Extracting Tributary Events from the Ming Shilu. [Elektronnyi resurs.] URL: https://dev.clariah.nl/files/dh2019/boa/0987.html. Data obrashcheniya – 20.07.2020.
Ares Oliveira, Sofia; di Lenardo, Isabella; Tourenc, Bastien; Kaplan, Frederic. A deep learning approach to Cadastral Computing. [Elektronnyi resurs.] URL: https://dev.clariah.nl/files/dh2019/boa/0691.html. Data obrashcheniya – 20.07.2020.
Smeenk, Kim; Bilgin, Aysenur; Klaver, Tom; Tjong Kim Sang, Erik; Hollink, Laura; van Ossenbruggen, Jacco; Harbers, Frank; Broersma, Marcel. Grounding Paradigmatic Shifts In Newspaper Reporting In Big Data. Analysing Journalism History By Using Transparent Automatic Genre Classification. [Elektronnyi resurs.] URL: https://dev.clariah.nl/files/dh2019/boa/0774.html. Data obrashcheniya – 20.07.2020.
Heße, Sascha. Clean Separation Of Overlapping Components In Line Segmentation Of Historic Handwritten Documents. [Elektronnyi resurs.] URL: http://staticweb.hum.uu.nl/dh2019/dh2019.adho.org/papers/index.html. Data obrashcheniya – 20.07.2020.
Computational intelligence in archaeology / Juan A. Barcelo, editor. Information Science Reference, London, 2009.-437 p.
Jorge Lazo. Can Deep Learning help us to rediscover the past? An application of Deep Learning to Archaeology. [Elektronnyi resurs.] URL: https://towardsdatascience.com/can-deep-learning-help-us-to-rediscover-the-past-5fa940c4e6c3. Data obrashcheniya – 20.07.2020.
H.A. Orgengo, F.C. Conesa, A. Garcia-Molsosa, A. Lobo, A.S. Green, M. Madella and C.A. Petrie. Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data // Proceedings of the National Academy of Sciences, July 2020, 202005583; DOI: https://doi.org/10.1073/pnas.2005583117.
Artificial Intelligence for Cultural Heritage. Edited by Luciana Bordoni, Francesco Mele and Antonio Sorgente. Cambridge, 2016.-148 p.
Knyaz, V.A., Vygolov, O.V., Kniaz, V.V., Vizilter, Y.V., Gorbatsevich, V.S., Luhmann, T. and Conen, N. Deep learning of convolutional auto-encoder for image matching and 3D object reconstruction in the infrared range. Proceedings – 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. P. 2155-2164.
Chambers S., Coudyzer E., Kestemont V. Gaining INSIGHT: exploring the application of Artificial Intelligence to the automatic classification of cultural heritage objects. DH Benelux 2019: Short Paper Abstract. [Elektronnyi resurs.] URL: http://2019.dhbenelux.org/wp-content/uploads/sites/13/2019/08/DH_Benelux_2019_paper_72.pdf. Data obrashcheniya – 20.07.2020.
The Proceedings of the AI*CH 2017. The 11th workshop on Artificial Intelligence for Cultural Heritage. Workshop co-located with AI*IA 2017 Bari, Italy, November 14, 2017. [Elektronnyi resurs.] URL: http://smcm.isasi.cnr.it/AIxCH2017. Data obrashcheniya – 20.07.2020.
C. Bassett, D.M. Berry, M.B. Fazi, J. Pay, B. Roberts. Critical Digital Humanities and Machine-Learning. Digital Humanities 2017. Montreal, Canada, August 8-11, 2017. [Elektronnyi resurs.] URL: https://dh2017.adho.org/abstracts/509/509.pdf. Data obrashcheniya – 20.07.2020.
Borodkin L.I. — Istorik i mir (bol'shikh) dannykh: vyzovy tsifrovogo povorota // Istoricheskaya informatika. – 2019. – № 3. – S. 14-30. DOI: 10.7256/2585-7797.2019.3.31383 URL: https://nbpublish.com/library_read_article.php?id=3138
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