Course on good principles & practices in Data Management (

Managing your Research Data: Best practices in Research Data Management for Biological Sciences

When and where?


It has been said that 80% of data analysis is spent on the process of cleaning and preparing the data. Not only does this represent a significant time investment for the data analyst, but is often a hurdle for the non-specialist trying to get to grips with analysing their own data after attending an R or Python course. Despite the best intentions, a spreadsheet that is intuitive and easily-understandable by human eyes can lead to disaster when trying to process computationally.

This workshop will go through the basic principles that we can all adopt in order to work with data more effectively and “think like a computer”. Moreover, we will discuss the best practices for data management and organisation so that our research is auditable and reproducible by ourselves, and others, in the future. Part of the journey will be via critical evaluation of example Data Management Plans (Often a condition of Grant).


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  • - Do you know what a Data Management Plan is and what it covers?
  • - 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?
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.

Aims: During this course you will learn about:

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  • - What Research Funders expect
  • - 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

Objectives: After this course you should be able to:

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  • - Select an appropriate backup strategy for your data
  • - Organise your files in a more structured and consistent manner
  • - Avoid common pitfalls in spreadsheet manipulation
  • - Known what resources are available at The University of Cambridge for Research Data Management


Mark Fernandes (CRUK Cambridge Institute).
Qi Wang (Bioinformatician, Department of Plant Sciences).

  Timetable for 19th March 2021 10:00-16:00
10:00 - 10:20 Introduction, Data Management Plans (Mark)
10:20 - 11:00 Data formatting (Mark)
11:00 - 11:10 Break
11:10 - 12:00 OpenRefine practical (Live coding) (Mark&Qi)
12:00 - 12:45 File management (Qi)
12:45 - 13:45 Lunch break
13:45 - 14:15 File management in DMP practical (Qi&Mark)
14:15 - 15:00 Data Sharing & Backup (Mark)
15:00 - 15:10 Break
15:10 - 15:45 Data Sharing & Backup in DMP practical (Mark&Qi)
15:45 - 16:00 Wrap-up & close

»»Please fill in the feedback survey at end of course««

Data for OpenRefine practical - right click for download

Example Data Management Plans for practicals

Drosophila BBSRC project.
Signalling pathways MRC project.
Bioinformatics software BBSRC project.
Pathways to violence & crime ESRC project.

A very different style of DMP for EU projects (Do NOT use for practicals).
Horizon 2020 EU Project.

Useful checklist
A Data management plan checklist.

Further reading

Further viewing