Description
How much data would you lose if your laptop was stolen? Have you ever emailed your colleague a file named ‘final_final_versionEDITED’? Have you ever struggled to import your spreadsheets into R? Would you be able to write a Data Management Plan as part of a grant proposal?
As a researcher, you will encounter research data in many forms, ranging from measurements, numbers and images to documents and publications. Whether you create, receive or collect data, you will certainly need to organise it at some stage of your project. This workshop will provide an overview of some basic principles on how we can work with data more effectively. We will discuss the best practices for research data management and organisation so that our research is auditable and reproducible by ourselves, and others, in the future.
Target audience
Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
Please be aware that these courses are only free for registered University of Cambridge students. All other participantse.g. Staff will be charged a registration fee in some form.
Prerequisites
No previous experience required
Sessions
1-day course
Topics covered
Data repair tools, Data management plans, Backups, Data repositories, reproducibility
Objectives
After this course you should be able to:
- Select an appropriate backup strategy for your data
- Organise your files in a more structured and consistent manner
- Avoid common pitfalls in spreadsheet manipulation
- Know what resources are available at The University of Cambridge for Research Data Management
Aims
During this course you will learn about:
- The basic concepts of creating a Data Management Plan
- Options for backing up your computer
- Ideas for naming and organising your files
- Strategies for exchanging files with collaborators
- Tips and tricks to make sure that your spreadsheets are readable by programming languages such as R
- Learn how to use the OpenRefine software for data cleaning
- Preparing high-throughput biological data for submission to a public repository such as Gene Expression Omnibus (GEO) or ArrayExpress
Format
Presentations and practicals