Online Course - Research Data Management 'Data Tree'

Module 1 - Context
In an age of data driven discovery one of the things that makes a good researcher is ensuring that your research data are effectively managed, and where appropriate, made open to others to re-use and re-purpose.


Module 2 – The Practicalities of Data Management.
The material covered in this module is what I refer to as the “nitty-gritty” aspects of data management; i.e. things researchers need to do and be aware of when it comes to data management. This includes the very important topic of Data Quality Assurance (DQA): what you should do to ensure your data are of good quality. The aim of the Module is to enable users to recognise potential problems with their data and to demonstrate practical methods for dealing with such problems.

Module 3 - NERC Specifics
NERC places a real importance on the long-term value of environmental research data, and of NERC’s long-term commitment to managing and making these data openly available.

Module 4 - Data Application: Analysis
The module aims to give you some practical tools to help you start to understand the stories that your data contain. Usually, this means understanding the shape of your data and the sources of variability within your data.
In the module, we will demonstrate the use of descriptive statistics to explore different datasets. We will also review hypothesis testing, and discuss the meaning of 'statistical' significance - and why it's not always the same as 'practical' importance.
If this sounds too much like statistics for you - don't worry! The point of the module is to provide some core statistical tools to help you with your research, without getting bogged down in formulae or heavy mathematics.

Module 5 - Visualisation
The visualisation module will give students an overview of some of the things they should be thinking about when visualising their data. We’ll tease out some of the different purposes for visualisation, and why these lead to different approaches. We’ll see some examples of good practice that should help students make sensible decisions about common issues such as colour, map projections and visualising uncertain data. To help people get started, we’ll recommend some particular tools and websites where they can find out more.

Module 6 - Working with policy
This section of the Data Tree explores the role of data in the policymaking process, the mechanisms to engage with policy-makers and ways to communicate uncertainty in data. The module takes the student on a journey to discover the value of data in the political context, what do policy-makers want from data and how they use data as evidence. Students will also learn about the policy cycle and how this concept is not as linear and straight forward as it sounds. As an example, there is also a review of how the Intergovernmental Panel for Climate Change influences policy and how they deal with uncertainty.

Module 7 - Working with business

Module 8 - Working with the media and public

Module 8 aims to give researchers and scientists the confidence to develop and deliver their own effective communications strategy and to develop what journalists call 'a nose for a story'. For researchers who are lucky enough to have a communications team at their disposal, Module 8 is a guide to providing engaging content and materials to create the maximum impact. From being able to write your own press release to taking 'story-telling' photos for social media to effective networking, Module 8 shows how accurate and meaningful communication can support the aims of your work, demonstrate reach and impact and impress your funders.