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The usage of computer-aided analysis of textual sources has been a natural accompaniment of computer technology proliferation since the early 1950s. As computer software and hardware became widely accessible to even non-expert users, Digital Humanities (along with other analogical monikers) experienced rapid growth during the last 30 years. If we consider the ever-growing hardware capacity, digital shifts in all of the social sciences and humanities fields, and the all-encompassing interconnectivity of the internet age, it is only logical, that formerly niche-expertize has slowly turned into standard skill or even requirements for the research practice. The rapid development and spread of AI-assisted user and research performance only hastened and deepened the ever-growing pressure on the digitalization of academia. This course ameliorates this situation by offering low-threshold, entry-level access to knowledge and skillsets important for further and deeper exploration of the matter. Last update: Kocián Jiří, PhDr., Ph.D. (05.02.2024)
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Students will acquire fundamental knowledge, skills, and orientation in Digital Humanities. They become familiar with the most important concepts, operations, and subfields of DH. This course serves as an introductory class for the certified program in AI in Digital Humanities and is therefore directly connected to the other parallel and following courses aimed at a detailed understanding of ML (NPFL 112, NPFL 142, NPFL 143) Last update: Kocián Jiří, PhDr., Ph.D. (02.02.2024)
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Compulsory attendance; minimum 50% points in part A), B) and C) each. A: 100-91 pts B: 90-81 pts C: 80-71 pts D: 70-61 pts E: 60-51 pts F(failed): 50 pts or less Last update: Vedlichová Klára, Mgr. (01.10.2024)
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Recommended reading Accelerating Social and Behavioral Science Through Ontology Development and Use | National Academies (n.d.). Available at: https://www.nationalacademies.org/our-work/accelerating-social-and-behavioral-science-through-ontology-development-and-use (accessed 9 October 2023). Arnold T and Tilton L (2015) Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text. Quantitative Methods in the Humanities and Social Sciences. Cham: Springer International Publishing. Available at: https://link.springer.com/10.1007/978-3-319-20702-5 (accessed 9 October 2023). Greenwell BB& B (n.d.) Hands-On Machine Learning with R. Available at: https://bradleyboehmke.github.io/HOML/ (accessed 9 October 2023). Krippendorff KH (2018) Content Analysis: An Introduction to Its Methodology. Fourth edition. Los Angeles: SAGE Publications, Inc. Piotrowski M (2012) Natural Language Processing for Historical Texts. Synthesis Lectures on Human Language Technologies. Cham: Springer International Publishing. Available at: https://link.springer.com/10.1007/978-3-031-02146-6 (accessed 9 October 2023). R for Data Science (2e) (n.d.). Available at: https://r4ds.hadley.nz/ (accessed 9 October 2023). Ramírez AG, Mejía JM, Martin PV, et al. (2023) Digital Humanities, Corpus and Language Technology / Humanidades Digitales, Corpus y Tecnología Del Lenguaje. University of Groningen Press. Available at: https://books.ugp.rug.nl/index.php/ugp/catalog/book/128 (accessed 1 February 2024). Silge EH and J (n.d.) Supervised Machine Learning for Text Analysis in R. Available at: https://smltar.com/ (accessed 9 October 2023). Last update: Vedlichová Klára, Mgr. (01.10.2024)
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This is a bloc course with six sessions every two weeks of the semester, physical presence is required. Students complete individual tasks after each session and collaborate on a group project to produce a salient research design proposal by the end of the semester. Last update: Kocián Jiří, PhDr., Ph.D. (02.02.2024)
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The final grade (100 points) comprises fulfilling three partial activities: A) midterm (15 pts) B) regular homework assignments (35 pts) C) groupwork research design (50pts) Last update: Vedlichová Klára, Mgr. (01.10.2024)
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The course program is organized into seven teaching sessions, each comprising two standard-length classes. Each session represents one consistent thematic/methodological bloc, split into conceptual parts, practical training, and an introduction to basic programming concepts. Each class is accompanied by a compulsory reading of (one or two journal articles) to represent best practices in research application and leads into an individual homework assignment for the off week. 1. Introduction to fundamentals of computational science and computer operation - 2. 10. selection of group topic 2. Text from computer perspective - 9. 10. offweek - home assigment 3. Qualitative coding - 23. 10. offweek - home assigment 4. Text analysis I. - 6. 11. offweek - home assigment A. Midterm - 13. 11. offweek - home assigment 5. Text analysis II. - 20. 11. offweek - home assigment 6. Data visualizations - 4. 12. offweek - home assigment 7. Network Analysis - 18. 12. offweek - home assigment 8. Mapping & GIS - 8. 1. B. Final meeting - January 2025 Last update: Vedlichová Klára, Mgr. (01.10.2024)
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The course is primarily for the students enrolled in the certified program in Digital Humanities. Enrollment for other students is possible per individual consultations with the course teachers. Last update: Vedlichová Klára, Mgr. (26.09.2024)
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