Data Management for Humanities Research

Module overview

In this module you will develop strategies and skills to integrate data management into humanities data science practices and methods. Over the course of the semester you will learn about good practice guidelines used in humanities research data management and develop skills to interpret and communicate them to a diverse audience of practitioners and researchers. Practical exercises developing data management strategies will enhance your understanding of debates about humanities data science and data driven research in the humanities. By the end of the semester, you will be prepared to situate data science methods in (inter)disciplinary humanities thinking and practically apply them to professional contexts.

Aims and Objectives

Learning Outcomes

Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

Engage in discussions about the contributions research data management practice make to humanities data science work
Practice applying research data management practices to humanities data

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Collaboratively work with peers to complete long term project goals
Manage time and resources to complete long term project work
Communicate complex topics to a diverse audience

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Develop practical knowledge of the research data management landscape for social science and humanities research
Develop practical knowledge of research data management practices
Evaluate data management workflows and infrastructures for humanities research
Appraise data management plans for humanities data science research

Learning and Teaching

Teaching and learning methods

Through a combination of practical and reflexive assessments students will demonstrate their foundational knowledge of research data management for humanities data science Students will complete practical exercises and reflect on their learning. They will also develop a portfolio that showcases their ability to bring their disciplinary knowledge of humanities data science into research data management practices

Study time
Type	Hours
Seminar	36
Independent Study	114
Total study time	150

Resources & Reading list

Internet Resources

PARTHENOS Training Materials .

Go FAIR .

DCC Curation Lifecycle Model .

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Formative Assessment

Assessment Type: Formative
Feedback: Students will receive written feedback, with the option to discuss feedback during seminar and office hours.
Final Assessment:
Group Work: Yes

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Final Assessment	50%
Group Assessment	50%