Scheduled workshops

No prior knowledge is required for this course.

Duration 1.5 hours.

Research Data Management (RDM) is a crucial part of ensuring the reproducibility of your research, but it can be difficult to know how to begin. This 1.5 hour workshop will introduce you to the basic principles of RDM and why it is so important to manage your research data well, and it will sign-post you to all of the other training and support available to help you with all aspects of RDM. Whether you are planning your first research project or your 1001’s this workshop is an ideal for ensuring you know how to manage your data and where to find support when you need it.

Upon completion of this course, attendees will be able to:

1. Recognize the importance of RDM and Data Management Planning
2. Apply basic RDM skills to their daily research practices
3. Understand what the FAIR (Findable, Accessible, Interoperable, and Reusable) principles mean and why they matter
4. Know where to find support and training within the university

Interactive workshop - Attendees should bring a laptop or tablet to write their DMP on either using DMPonline, or the basic template the tutor will provide. It would also be helpful if you could bring your project outline to base the draft DMP on.

Duration 2 hours

At the end of this practical workshop you will understand the basic components of good DMP, and have produced a 1st draft Data Management Plan (DMP) for your research project.

Upon completion of this course, attendees will:

1. understand the necessity/benefits of producing a DMP;
2. know how to register for and use DMPonline;
3. have drafted a basic DMP that they can complete after the course.

Presentation - Before attending you should complete the mandatory online Data Protection Training https://data-protection.ed.ac.uk/training-events

Duration 2 hours

Researchers today are pressured to share their research data and make it accessible to other researchers. But what if you have collected sensitive or confidential data?

For many researchers, the sensitivity of research data is one of the main barriers to data sharing. Fear of violating ethical or legal obligations, lack of knowledge about disclosure control and the time required to anonymise data to a suitable standard often prevent valuable datasets from seeing the light of day.

In this two-hour awareness raising course, we introduce how to collect, share, store, and protect the sensitive data you may encounter as part of your work.

Upon completion of this course, attendees will:

1. understand the principles of good research data management;
2. have knowledge of GDPR and data protection regulations, and what these mean for research and research data;
3. be aware of relevant services and resources available to researchers at the university.

Interactive workshop

Duration 2 hours

Learn how to keep your data findable, accessible and re-usable (FAIR) for the decades to come, and how to show your funder you’re complying with their data policy. The course includes a demo of DataVault’s new and improved interface.

Interactive Workshop with LEGO®

Duration 2 Hours

What information will you need to make sense of your research in 6 months, or 3 years, or 10 years? What would another researcher need to know to reproduce and verify your findings? FAIR data and reproducibility are central themes of research and data management. Metadata and documentation are essential elements of both and improving them is easy, once you know how. 

This workshop introduces researchers to planning for metadata, formats of documentation and metadata recording, and metadata standards. Using the LEGO® Metadata for Reproducibility game developed at the University of Glasgow, the workshop engages researchers in important discussions about how metadata is captured, recorded and disseminated. This, in turn, provides an opportunity to explore parallels between recording and communicating the research process and documenting and the creation of a Lego model.

Upon completion of this workshop attendees will: 

1. Understand the principles of FAIR data and the importance of good metadata and documentation 
2. Be able to document their data, creating basic metadata and a simple ReadMe file
3. Know where to find support