COMPUTER SCIENCE FOR CULTURAL HERITAGE

This course provides students with a basic understanding of computer science principles and digital technologies applied to cultural heritage. It introduces the main digital tools used for the documentation, inventorying, digitization, management, preservation, and valorization of artistic and cultural assets. Particular attention is given to data organization, metadata, digital documentation, images, databases, GIS, 3D models, open-source tools, and the responsible use of artificial intelligence.
The course supports the program’s objectives by helping students develop basic digital literacy, use digital tools correctly, and communicate digital outputs clearly. It also prepares students to collaborate with professionals in conservation, documentation, information technology, museums, archives, and heritage management.
The course strengthens students’ ability to participate in practical activities, internships, and further study by transforming cultural heritage information into organized, understandable, and reusable digital resources.

By the end of the course, students will be able to:
- Understand the basic role of computer science in the documentation, study, management, preservation, and communication of cultural heritage.
- Identify the main types of cultural-heritage data, including text records, images, tables, spatial data, metadata, and simple 3D data.
- Understand basic principles of data organization, metadata, digital archives, databases, image processing, GIS, and 3D documentation.
- Use basic digital tools to organize, describe, and present cultural-heritage information.
- Create simple metadata records for cultural-heritage objects, images, or documentation materials.
- Apply elementary image-processing, data-visualization, and mapping techniques to support documentation and interpretation.
- Understand the main opportunities and risks of artificial intelligence in cultural heritage.
- Choose and present appropriate digital outputs, such as tables, images, maps, metadata records or simple prototypes, in a clear and correct way.

No advanced programming knowledge is required.
Students are expected to have basic computer literacy and to be willing to work with digital tools, datasets, and online platforms.
Students with no previous experience in digital tools will be supported through guided exercises, templates, and introductory materials.

The course program is organized around introductory theoretical concepts and practical exercises. The contents emphasize digital literacy, basic methodological rigor, and practical applications in cultural heritage.
The main contents of the course are the following:
1. Fundamentals of computer science for cultural heritage.
2. Digital documentation and digitization of cultural-heritage objects, collections, and sites.
3. Basic data organization and file management.
4. Introduction to metadata and digital cataloging.
5. Basic databases and information modeling.
6. Data visualization for cultural-heritage information.
7. Introduction to digital images and basic image processing.
8. Introduction to Geographic Information Systems and cultural-heritage mapping.
9. Introduction to 3D documentation and digital models.
10. Introduction to artificial intelligence and its responsible use in cultural heritage.
11. Digital preservation, online repositories, and access to cultural-heritage resources.
12. Digital communication and public engagement for cultural heritage.

Mandatory texts
1. Course slides, practical instructions, datasets and selected readings provided by the professor through Moodle.
2. Gilliland, A. J., “Setting the Stage”, in Introduction to Metadata, Getty Research Institute, latest available edition, selected sections.
3. Ronchi, A. M., ECulture: Cultural Content in the Digital Age, Springer-Verlag Berlin Heidelberg, 2009, selected sections.
4. QGIS Project, QGIS Training Manual, selected sections available online.
5. OpenRefine Documentation, selected sections available online.

Supplementary texts
1. Li, Z., Drew, M. S. and Liu, J., Fundamentals of Multimedia, Springer International Publishing, 2014, selected sections.
2. Lake, P. and Crowther, P., Concise Guide to Databases, Springer London, 2013, selected sections.
3. International Image Interoperability Framework Consortium, IIIF Documentation, selected introductory sections.
4. UNESCO, Recommendation on the Ethics of Artificial Intelligence, selected introductory sections.
5. Selected case studies on digital documentation, museums, archives, GIS, 3D models and artificial intelligence in cultural heritage, provided by the instructor during the course.

Learning will be assessed through practical activities developed during the course, a final applied project, and a final written exam.
The assessment is designed to verify both the basic theoretical understanding of the course content and the ability to use digital methods in simple cultural heritage contexts.

1. Practical activities and exercises (30%)
During the course, students will complete practical exercises related to the main topics of the program. These may include data organization, metadata creation, basic database modeling, simple data visualization, image processing, GIS mapping, 3D model inspection, and critical reflection on artificial intelligence tools.
These activities assess the student’s ability to apply the basic methods introduced in class, use digital tools correctly, document simple procedures and interpret results in relation to cultural heritage.

2. Final applied project (30%)
Students will develop a simple applied project on one of the course topics. The project may focus on the digital documentation of a cultural heritage object, site, or collection, the creation of a small metadata set, a basic database, a simple map, an image-processing exercise, a 3D documentation activity, or a critical analysis of the use of artificial intelligence in cultural heritage.
The project assesses the student’s ability to organize cultural heritage information, choose appropriate digital tools, apply basic methods correctly, document the work carried out and present the results in a clear and coherent way.

3. Final written exam (40%)
The final written exam assesses the student’s knowledge and understanding of the theoretical and applied contents of the course.
The exam may include open-ended questions, short theoretical questions, interpretation of practical cases, discussion of simple digital workflows, and exercises related to cultural heritage data, metadata, images, databases, GIS, 3D documentation, digital preservation, and artificial intelligence.
The final written exam verifies the student’s ability to explain basic concepts, choose appropriate digital tools for simple cultural heritage tasks, and communicate digital methods using adequate terminology.
The final grade will be calculated as the weighted average of the practical activities and exercises, the final applied project, and the final written exam.

written

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

Grades will be assigned according to the following criteria. The final grade will take into account the practical activities and exercises developed during the course, the final applied project, and the final written exam.

- Scores in the 18–22 range will be awarded when the student demonstrates sufficient knowledge of the course’s main topics in the final written exam and sufficient ability to apply basic digital tools to simple cultural heritage tasks in the practical activities and final project. Practical activities and the project are understandable but may show limited autonomy, partial documentation, weak interpretation, or limited connection with the cultural-heritage context.

- Scores in the 23–26 range will be awarded when the student demonstrates good knowledge of the course topics in the final written exam and an adequate ability to use digital tools for cultural heritage documentation, organization and communication in the practical activities and final project. Practical activities and the project are generally well organized and documented, with adequate terminology, fair interpretation of results, and a clear connection with the cultural-heritage context.

- Scores in the 27–30 range will be awarded when the student demonstrates very good or excellent knowledge of the course topics in the final written exam and a strong ability to apply digital tools correctly and critically to cultural heritage tasks in the practical activities and final project. Practical activities and the project are well structured, clearly documented, accurately communicated, and show good awareness of the role and limits of digital technologies in cultural heritage.

- 30 cum laude will be awarded when the student demonstrates excellent mastery of the course contents in the final written exam, strong autonomy in the practical activities, rigorous documentation, clear and effective communication, and a particularly mature or original application of digital technologies to cultural heritage in the final project.

The course combines lectures, applied demonstrations, computer laboratories, guided exercises, and discussion of case studies.
Lectures introduce the basic theoretical and methodological foundations of computer science applied to cultural heritage. Laboratory sessions allow students to apply these concepts to simple datasets, images, maps, and digital materials related to cultural heritage.
The teaching approach is practical and introductory. Students will work with open-source or widely accessible tools whenever possible. Depending on the activities developed during the course, these may include spreadsheets, OpenRefine, QGIS, ImageJ/Fiji, simple database tools, online repositories, and selected 3D or image-analysis tools.
Course materials, datasets, instructions, examples, self-study resources, and exercise templates will be provided through the Moodle e-learning platform. Students are expected to use Moodle regularly.

Students are encouraged to connect the practical activities with topics, examples, or case studies from other courses, museum visits, archive activities, archaeological contexts, internships, or personal study interests, whenever possible and appropriate.
The course prioritizes open-source, sustainable, and transferable tools so that students can continue using them after the course in further study, internships, museums, archives, cultural institutions, or professional contexts.
When specific software is required, installation instructions will be provided in advance. Alternative arrangements will be discussed if technical constraints arise.
Responsible use of artificial intelligence tools is allowed only when explicitly declared and critically discussed. AI tools may support language revision, basic data exploration or prototyping, but students remain responsible for the accuracy, originality and interpretation of their work. Undeclared or misleading use of AI tools is not acceptable.

This subject deals with topics related to the macro-area "Cities, infrastructure and social capital" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development