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
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- research data management practices
- the research data management landscape for social science and humanities research
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Collaboratively work with peers to complete long term project goals
- Communicate complex topics to a diverse audience
- Manage time and resources to complete long term project work
Disciplinary Specific Learning Outcomes
Having successfully completed this module you will be able to:
- Practice applying research data management practices to humanities data
- Engage in discussions about the contributions research data management practice make to humanities data science work
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- appraise data management plans for humanities data science research
- Evaluate data management workflows and infrastructures for humanities research
Syllabus
Indicative topics covered include:
Situating research data management in humanities research
Research data management lifecycles
Managing FAIR and Open Access humanities data
Research data management for interdisciplinary practice
Data Feminism and research data management
Minimal computing approaches to data management
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
| Type | Hours |
|---|---|
| Independent Study | 114 |
| Seminar | 36 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
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.
| Method | Percentage contribution |
|---|---|
| Final Assessment | 50% |
| Group Assessment | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Assessment tasks | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Assessment tasks | 100% |
Repeat Information
Repeat type: Internal