B5079 - Digital Humanities and Data Management for Cultural Heritage

Learning outcomes

At the end of the course, the student is familiar with the main theories, methodologies, and techniques belonging to the fields of Digital Humanities and Linked Open Data. Furthermore, they are able to apply the acquired knowledge to represent and manage data in the humanities and to produce, manage, and publish digital resources related to cultural heritage following the principles of open science.

Course contents

The course is intended as a cross-disciplinary introduction to the field of Digital Humanities (DH), and in particular how the DH offer a common methodological outlook that is well suited to enhancing cultural heritage in its many forms.

Topics covered will include: research & data literacy (how to find, evaluate, and work with information), metadata & data management (how to describe and work with information), data manipulation & presentation (how to analyze and present information), and key scientific, legal, and ethical frameworks (how, when and why you should share your work) such as Open Science, and the FAIR and CARE principles.

The course will follow a project-based learning approach that blends theory and practice of the models, tools, and principles necessary to conceive, design, and implement a digital resource related to cultural heritage. Students can expect to be introduced to key theoretical groundings that have driven and continue to drive the field of DH as well as practical ways to work with cultural heritage in a digital context.

Readings/Bibliography

The lecture notes, bibliography, and any other additional materials will be made available to students via Virtuale as well as the course's GitHub repository. All efforts will be made to ensure texts in the bibliography are freely accessible via pdf or html pages with no access requirements, or available via the University's library system. Recommended texts are in English and Italian.

The current bibliography is focused on providing students preparatory tools for the course, useful for filling any gaps for those who are new to the Digital Humanities. A more in depth bibliography will be given closer to the course's starting time.

Preparatory Bibliography:

• Chapter 1 (Humanities to Digital Humanities), Chapter 4.1 and 4.2 (DH Fundamentals, The Project as Basic Unit) in Burdick, A., Drucker, J., Lunenfeld, P., Presner, T., & Schnapp, J. (2012). Digital_Humanities. MIT Press, open access edition (Wayback copy). link (pdf)
• Introduction and Chapter 1 in Schreibman, S., Siemens, R., & Unsworth, J. (Eds.). (2008). A companion to digital humanities. John Wiley & Sons. https://companions.digitalhumanities.org/DH/
• Spiro, L. (2012). "This Is Why We Fight: Defining the Values of the Digital Humanities". In M. K. Gold (Ed.), Debates in the Digital Humanities. University of Minnesota Press. link (html)
• Smith, A.J., and Whearty, B. "All the Work You Do Not See: Labor, Digitizers, and the Foundations of Digital Humanities". In M. K. Gold (Ed.), Debates in the Digital Humanities 2023. University of Minnesota Press. link (html)
• Tammaro, A. M. (2017). "Data literacy: formare docenti e studenti alla gestione dei dati di ricerca". Biblioteche oggi, 35, 19-25. link (pdf)
• Introduction and Chapter 1 in Tomasi, F (2022). Organizzare la conoscenza: Digital Humanities e Web semantico: Un percorso tra archivi, biblioteche e musei. Editrice Bibliografica. link (pdf)
• Università di Bologna. Linee guida di Ateneo per la gestione dei dati della ricerca. link (pdf)
  

Teaching methods

The course, with a total duration of 30 hours, will aim to enhance students' learning potential with various approaches and tools:

• Project-based learning to allow students to actively explore and confront the topics and themes of the course. In particular, for the final exam students will be expected to work in groups that include members of the different programmes to foster cross-disciplinary skills and understanding.
• Lessons will be recorded and made available for download as a support for reviewing content, whether the students are attending or non-attending. The recording of lessons by students for their own specific needs is possible but must be discussed and agreed to prior to the start of the course.
• Slides and all course materials will also be made available to consult at any time, again as a support for reviewing content. Whenever possible additional material will be made available to deepen topics covered during the lessons as well as present students with key critical thinking approaches to the enhancement and management of cultural heritage.
• Participatory exercises (such as interactive q&a sessions) will be used to allow the class as a whole to discuss what they are learning and present practical and theoretical implications.
• Collaborative practical learning activities will balance the theoretical elements of the course to give students a chance to work together to integrate theory and practice and to develop skills useful for the final exam. These activities will also be a primary vehicle for introducing potential tools that can be used for the final exam and which contextualize the topics of the course.
• The use of Generative AI and LLMs is permitted but subject to both the Unibo policy and course-specific guidelines (see Teaching Tools). The non-use of Generative AI and LLMs is also encouraged. The choice of either approach and its application will be part of the final evaluation and grade.

Assessment methods

The course will be assessed via a project-based final exam, intended to evaluate students' knowledge of and ability to implement both theoretical and practical elements introduced during the course.

The exact details of what this final project will require, how it will be evaluated, and the grade assignment will be confirmed before the course starts towards the end of October 2026. It will, broadly speaking, involve the design and prototyping of a digital resource related to the valorization of cultural heritage that must then be presented during an oral exam.

Students are expected to work on the project as a group, which should include students from the different programmes. Exact details of group sizes and possible exceptions will be given at the start of the course.

Specific guidelines for the project will be given at the start of the course.

Each group member's individual contribution and understanding of the course topics will be assessed during the oral exam.

The use or non-use of Generative AI and LLMs in the completion of the final project will be part of the final evaluation and grade, allowing students to gain additional points. Full details will be given at the start of the course.

Based on the score, the following grades will be assigned as follows:

• <50% (failing grade): large gaps in methodology, approach, tools used, and documentation or a lack of understanding of the theoretical and practical materials offered during the course.
• 51–70% (sufficient): some gaps in methodology, approach, and tools used, some engagement with critical thinking, along with acceptable preparation in at least one aspect of the project (score between 18-22/30);
• 71–90% (good): good understanding of methodology, approach, and tools used, engagement with critical thinking, along with adequate preparation in more than one aspect of the project and basic use of critical thinking skills (score between 23-27/30);
• 91–100% (excellent): great understanding of methodology, approach, and tools used, significant engagement with critical thinking, along with excellent preparation in more than one aspect of the project (score between 28-30/30).

Teaching tools

The lessons take place in a classroom equipped with personal computers connected to the intranet and the internet.

Students who have their own personal computers or similar devices (such as tablets) are encouraged to bring them to the classroom. It is not recommended to only bring a mobile phone as a device with which to attend the classes.

Theoretical lessons will always be accompanied by practical sessions that will require the use of computers. 

All course materials – including lecture notes, slides, and others – will be made available on Virtuale and online via GitHub.

Students who, due to disabilities or specific learning disorders (SLD), require compensatory tools are encouraged to inform the instructor of their needs so they can be directed to the appropriate contacts and arrangements can be made to adopt the most suitable measures. For more information: https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students

The use of Generative AI and LLMs, by which is intended agents, chatbots, and other interfaces where text prompts can be used to produce an output, during the course for educational support is permitted based on the existing Unibo policy (e.g. research support, information summarizing, etc). The use of GenAI and LLMs for the final project is permitted but will be subject to course-specific guidelines in addition to those from the University.
Details of the course's guidelines for the use of GenAI and LLMs will be made available at the start of the course.

Importantly, the non-use of Generative AI and LLMs is encouraged. Students must decide if they want to use these technologies or not and how. The potential implications of these choices will be discussed in the first class alongside the guidelines, final exam, and other essential contextual elements of the course.

Office hours

See the website of Laurent Antoine Fintoni