Digital instruments of research II 2900-HAMC-DIGIT2
The course aims to present the digital instruments used in humanities. During the course, the participants will get acquainted with data management and learn about essential tools and data sources that can be used in their work.
The class will consist of a theoretical introduction and practical parts. The practical part will contain short exercises and project-based training to demonstrate different aspects of digital instruments used during one research project.
1. The first part will consist of an overview of digital tools and practical competencies, which will help participants in the scientific work and be relevant in managing and completing the project.
Topics Covered:
- An overview of the course: digital tools, data sources, and methods,
- Data organisation, Open Data and F.A.I.R data principles.
- Exercise: Introduction to digital notes software using Obsidian and Zotero for managing collected information, such as text, references, images and code.
2. The second part will focus on an introduction to databases used in the humanities. The classes will be acquainted with the types of databases and principles of SQL. The participants will learn how to create an SQL database. SQL queries, data cleaning, and preparation for further research steps will also be introduced.
Topics Covered:
- Introduction to databases.
- Overview of database types.
- SQL database: data types, relations and structure.
- Practical exercise: data analysis in EXCEL, SQL database, queries.
3. The third part will include a theoretical introduction to landscape archaeology and analysis of the landscape using satellite imagery. The practical part will focus on the application of satellite imagery available in the cloud datasets using Google Earth Engine (GEE)
Topics Covered:
- Principles of landscape analysis with satellite imagery (earth observation).
- Examples of application of satellite imagery in landscape archaeology.
- Exercise: landscape analysis using Google Earth Engine.
4. The last part will include a theoretical introduction to deep learning, best practices and ethics in artificial intelligence. Students will be acquainted with the tools necessary for creating datasets and training and evaluating deep learning models.
Topics Covered:
- Introduction to deep learning, an overview of deep learning models and examples of the use of deep learning in archaeology.
- Best practices and ethics in artificial intelligence.
- Preparation of the dataset.
- Training and evaluating the model.
Rodzaj przedmiotu
Założenia (opisowo)
Koordynatorzy przedmiotu
Efekty kształcenia
The student has in-depth knowledge of the existing paradigms - world achievements, including theoretical foundations and general and selected specific issues of digital archaeology (P8S_WG.1). The student knows and understands the main developmental trends of digital archaeology (P8S_WG.2) and knows the methodology of scientific research (P8S_WG.3). Student understands the fundamental dilemmas of modern civilization in the aspect of application of digital archaeology (P8S_WK.1).
Student is able to use knowledge from different fields of science to creatively identify, formulate and innovatively solve complex problems or carrying out tasks of research character. In particular, he/she is able to independently define the aim and subject of scientific research, formulate a research hypothesis, develop research methods, techniques and tools and apply them creatively, as well as make conclusions on the basis of research results (P8S_UW.1). Can communicate on topics related to digital archaeology to a degree that enables active participation in the international scientific community (P8S_UK.1). Can participate in scientific discourse (P8S_UK.4).
The student is ready to recognize the importance of knowledge in solving cognitive and practical problems (P8S_KK.3), and is also ready to sustain and develop the ethos of research and creative circles, including, conducting scientific activity in an independent manner, and respecting the principle of public ownership of the results of scientific activity, taking into account the principles of protection of intellectual property (P8S_KR.1)
Kryteria oceniania
Participants will be required to prepare a final assessment presentation based on the tools and theory of digital tools learned during the course, and a short review of a paper focusing on digital humanities or digital archaeology.
The final assessment will consist of making and recording the presentation, making up for missed exercises, and preparing a short review of a paper focusing on digital humanities or digital archaeology.
Two excused absences are allowed. Subsequent absences can be credited by doing exercises related to the topic of the class on which the student was absent, upon the permission of the KJD (Deputy Dean for student affairs) of the Faculty of History.
Literatura
Athanassopoulos, Effie F., and LuAnn Wandsnider. ‘Mediterranean Landscape Archaeology Past and Present’. In Mediterranean Archaeological Landscapes: Current Issues, edited by Effie F. Athanassopoulos and Luann Wandsnider, 1–14. Philadelphia, PA: University of Pennsylvania Press, Inc., 2011.
Bickler, Simon H. ‘Machine Learning Arrives in Archaeology’. Advances in Archaeological Practice 9, no. 2 (May 2021): 186–91. https://doi.org/10.1017/aap.2021.6.
Bishop, Christopher M, and Hugh Bishop. Deep Learning: Foundations and Concepts. Springer Nature, 2023.
Buławka, Nazarij, Hector A. Orengo, and Iban Berganzo-Besga. ‘Deep Learning-Based Detection of Qanat Underground Water Distribution Systems Using HEXAGON Spy Satellite Imagery’. Journal of Archaeological Science 171 (2024): 106053. https://doi.org/10.1016/j.jas.2024.106053.
Campana, Stefano. ‘Drones in Archaeology. State-of-the-Art and Future Perspectives’. Archaeological Prospection 24, no. 4 (2017). https://doi.org/10.1002/arp.1569.
Chapman, Henry. Landscape Archaeology and GIS. Tempus, 2006.
Conesa, Francesc C., Hector A. Orengo, Agustín Lobo, and Cameron A. Petrie. ‘An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale’. Remote Sensing 15, no. 1 (2023): 53. https://doi.org/10.3390/rs15010053.
Evans, Thomas L, and Patrick T Daly. Digital Archaeology : Bridging Method and Theory. London; New York: Routledge, 2006. https://doi.org/10.4324/9780203005262.
Howe, D R. Data Analysis for Database Design. Engineering Village. Butterworth Heinemann, 2001.
Karamalis, Athanasios. ‘Databases for Multiple Archaeological Excavations and Internet Applications’. In E-Learning Methodologies and Computer Applications in Archaeology, edited by Dionysios Politis, 104–27. IGI Global, 2009.
Matsumoto, Mallory E. ‘Archaeology and Epigraphy in the Digital Era’. Journal of Archaeological Research 30, no. 2 (June 2022): 285–320. https://doi.org/10.1007/s10814-021-09162-4.
Nicholson, Christopher, Sarah Kansa, Neha Gupta, and Rachel Fernandez. ‘Will It Ever Be FAIR?: Making Archaeological Data Findable, Accessible, Interoperable, and Reusable’. Advances in Archaeological Practice 11, no. 1 (February 2023): 63–75. https://doi.org/10.1017/aap.2022.40.
Petersen, John V. Absolute Beginner’s Guide to Databases. 1 online resource (vii, 313 pages) : illustrations vols. Absolute Beginner’s Guide Ser. Indianapolis, Ind.: Que, 2002.
Schmidt, Sophie C., Florian Thiery, and Martina Trognitz. ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’. Digital 2, no. 3 (22 June 2022): 333–64. https://doi.org/10.3390/digital2030019.
Seales, W. Brent, and Christy Y. Chapman. ‘From Stone to Silicon: Technical Advances in Epigraphy’. International Journal on Digital Libraries 24, no. 2 (June 2023): 129–38. https://doi.org/10.1007/s00799-023-00362-5.
Sumathi, S, and S Esakkirajan. Fundamentals of Relational Database Management Systems. Studies in Computational Intelligence, v. 47. Berlin / London: Springer, 2007.
Wilkinson, Tony James. Archaeological Landscapes of the Near East. Tucson: University of Arizona Press, 2003.
Wilson, Andrew T, and Ben Edwards. Open Source Archaeology : Ethics and Practice. Warsaw; Boston: De Gruyter Open, 2015.
Wiseman, James, and Farouk El-Baz. Remote Sensing in Archaeology. New York: Springer, 2007. http://public.eblib.com/choice/publicfullrecord.aspx?p=372655.
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: