This training curriculum offers a modular programme to support researchers in applying FAIR (Findable, Accessible, Interoperable, Reusable) principles and open research practices to their research software. The overview module provides an introduction to FAIR for research software and gives an overview of the topics that are offered in more detail in additional modules, which can be selected according to an individual’s learning goals.
This programme is aimed at researchers, including PhDs and postgraduate research students, who create code (whether a few scripts or something more substantial) as part of their research and who want to make their research more open by applying the FAIR principles to their software or simply want to become more confident in the research code they are writing.
The FAIR principles were developed as guidelines to enhance the reusability of research data1. The FAIR principles apply the concepts of Findability, Accessibility, Interoperability and Reusability to scholarly data holdings, and were also intended to be applied to other digital research objects such as algorithms and workflows.
Over the past few years there has been growing recognition that research software, defined as “source code files, algorithms, scripts, computational workflows and executables that were created during the research process or for a research purpose”2, should adhere to FAIR guidelines as well as meeting broader open research goals such as reproducibility and open access. A modified set of FAIR principles for research software (FAIR4RS), allowing for the inherent differences between data and software, has been developed to provide a framework for the development of FAIR research software3.
22nd October 2024, LunchBytes talk, 12pm, online
In this introductory session we will try to understand what the FAIR principles are and why they have emerged. We will then introduce some actions on how to apply them to software and present a global review of the training programme.
When you start writing software it is often very useful to think about the development process and how you will make your software sustainable in the long term. In this module we will introduce important aspects of software development in research: software lifecyle, management plan, licences and dissemination. This module should allow you to ask yourself the right questions when starting a research software project.
The version control module has two distinct training sessions: one for beginners and one for more advanced users.
Git, GitHub and GitKraken - From Zero to Hero: If you’ve never heard of or used version control and Git before this is the course for you. We start by introducing version control and exploring how it can be beneficial to researchers, then we introduce some useful tools and get started with some basic workflow using these tools. We build on those foundations with collaborative exercises that introduce key concepts such as forks, pull requests and branches and give you the chance to get some hands-on experience with using version control in a research setting.
Git collaboration: This course aims to help you develop a deeper understanding of how Git works to facilitate collaboration. It builds on the foundations laid by the Git beginners course. The core idea around the course is that by improving your understanding of working with branches and how to make your commits tidier and neater it makes it easier to understand pull requests and Git history which in turn makes it easier to collaborate and work on code with others (including your future self!).
The way you write your code will have a massive impact on how easy it is to maintain. During this course we will learn how to create maintainable, readable and reusable code. Using examples and exercises, we will see that creating high quality code is actually quite straightforward when you understand how to do it and what tools are available to make your life easier.
This course aims to equip researchers with the skills to write effective tests and ensure the quality and reliability of their research software. No prior testing experience is required! We’ll guide you through the fundamentals of software testing using Python’s Pytest framework, a powerful and beginner-friendly tool. You’ll also learn how to integrate automated testing into your development workflow using continuous integration (CI). CI streamlines your process by automatically running tests with every code change, catching bugs early and saving you time.
Well-documented software promotes reproducibility, maintainability, and increased research impact through wider adoption and citation. This course teaches researchers how to document their software effectively, making it accessible and understandable to others. It covers topics such as writing readable code and usage instructions.
Ensuring that others are able to take your code, run it, and are able to produce the same (or equivalent) results is one of the key tenets of FAIR and reproducible research software. This course will provide you with an overview of different ways to make your code reproducible and then focus on virtual environments as a specific tool for computational reproducibility.
Packaging your software is one of the important steps in a software project to make it both findable and accessible. This course will provide you with an understanding of why and when packaging is useful, what different standards exist to package Python projects and take you through each step of the packaging process.
Did you know that you can actually publish a paper about your software? This is an ideal way to get recognition (and citation) for the software you have spent countless hours creating. In this course we will walk you through an example of submission in the Journal of open Source software. We will make an example software submission to the journal, and thanks to the collaboration of the Editor in Chief of JOSS (Arfon Smith), we will look at how the review process is done.
Each session will have some individual prerequisites. Some experience with developing research software or scripts, for example in Python or R, might be needed. Please refer to the individual course details to know what they are.
After completing this modular programme, participants should be able to:
For enquiries, please contact Tamora James (t.d.james@sheffield.ac.uk, Programme manager), and/or Romain Thomas, (romain.thomas@sheffield.ac.uk, Head of the RSE group).
Wilkinson et al., ‘The FAIR Guiding Principles for Scientific Data Management and Stewardship’. doi:10.1038/sdata.2016.18 ↩
Barker et al., ‘Introducing the FAIR Principles for Research Software’. doi:10.1038/s41597-022-01710-x ↩
Chue Hong et al., ‘FAIR Principles for Research Software (FAIR4RS Principles)’. doi:10.15497/RDA00068 ↩
For queries relating to collaborating with the RSE team on projects: rse@sheffield.ac.uk
Information and access to JADE II and Bede.
Join our mailing list so as to be notified when we advertise talks and workshops by subscribing to this Google Group.
Queries regarding free research computing support/guidance should be raised via our Code clinic or directed to the University IT helpdesk.