Challenges In-person delivery ✓Enhanced trainer and trainee interaction ✓Reduced overhead for instructors of hybrid delivery Less inclusive of participants who wanted to join online Python code examples Time constraints Reaching target audiences ✓Familiar to the scientific research community Not all participants were familiar with Python ✓Registration statistics showed good uptake across the University No single channel of communication to reach all sections of target audience ✓Minimise participants’ time commitment Challenge of covering material in sufficient depth in the time available FAIR2 for research software Developing a FAIR (Findable, Accessible, Interoperable and Reusable) and Reproducible research software training programme Tamora James1,*, Romain Thomas1 1 University of Sheffield, UK. * Corresponding author:
[email protected] The FAIR Principles for Research Software (FAIR4RS) [1] provide a framework for the development of FAIR (Findable, Accessible, Interoperable and Reusable) [2] research software. Recently there has been growing recognition that research software should adhere to the FAIR4RS principles, as well as meeting broader open research goals such as reproducibility. “The FAIR4RS Principles are relevant to any stakeholder in the research community seeking to increase transparency, reproducibility, and reusability of research.” [1] Many researchers require additional support to meet these guidelines due to lack of training in software development. Hence, FAIR4RS skills training is a key component of successful adoption of FAIR research software practices [3]. The University of Sheffield Research Software Engineering team and collaborators developed a training programme to support researchers in applying FAIR principles and open research practices to their research software. We aim to motivate and equip participants with the skills and knowledge needed to create and publish FAIR research software, to meet our goal to support research excellence at the university by enhancing capacity for producing high quality, efficient and sustainable research software. Modular programme: Topics can be selected according to an individual’s learning goals. Consistent look and feel: Use of the Carpentries Workbench with custom theme. Varied delivery formats: Online webinar, hands-on skills training, walkthrough/live demo. Scheduled prerequisites: Training for prerequisites fed into subsequent modules. Open educational resource: Course materials published under Creative Commons licenses. FINDABLE Software, and its associated metadata, is easy for both humans and machines to find ACCESSIBLE Software, and its metadata, is retrievable via standardised protocols INTEROPERABLE Software interoperates with other software by exchanging data and/or metadata, and/or through interaction via standardised application programming interfaces REUSABLE Software is both usable (can be executed) and reusable (can be understood, modified, built upon, or incorporated into other software) REPRODUCIBLE* Reproducibility of research software supports transparency and robustness of the research process *not part of the FAIR4RS principles Programme outline Publishing & dissemination Software papers Packaging Tools for reproducibility Reproducible computational environments Coding best practice Code design Version control Documentation Testing and CI Software lifecycle planning Software management plans Licensing DOI/metadata Impact 45 hours of training 250registrations 190attendees I appreciated that time was set aside to put the learning into practice. The willingness of the course leaders to help me and to make sure I understood everything as we progressed through the training. Learning “ ” “ ” ✓Collaborating across different teams strengthened the quality of the offering ✓Feedback highlighted knowledgeability of instructors and clarity of course materials and delivery ✓Coordination and communication amongst the team members helped to ensure smooth running of the programme ✓Facilitated by a dedicated programme manager and regular meetings during design phase ✓Not everything went according to plan and instructors had to be flexible ✓Room booking challenges, changes to software packages, participants requiring extra support ✓Helpful to reflect on feedback from participants and instructors to refine the programme for future delivery ✓Need to adjust timings and content covered for some components References [1] Barker et al. (2022) Introducing the FAIR Principles for research software [2] Wilkinson et al. (2016) The FAIR Guiding Principles for Scientific Data Management and Stewardship [3] Barker et al. (2024) The FAIR for Research Software Principles after two years: An adoption update Acknowledgements The training programme was created by the Research Software Engineering team, in collaboration with the Data Analytics Service and the Library team at the University of Sheffield, supported by the School of Computer Science, the School of Mathematical and Physical Sciences and IT Services—Research & Innovation IT. Course materials were developed by Jenni Adams, Farhad Allain, Dan Brady, Ric Campbell, Joe Heffer, Kate O’Neill, Neil Shephard, Romain Thomas, Sylvia Whittle, and Christopher Wild. FAIR4RS icons adapted from Chue Hong, Neil (2023) Is Research Software Engineering coming of age? and used under a CC-BY 4.0 licence. DOI: https://doi.org/10.6084/m9.figshare.24078054.v5. Other icons are from Font Awesome and used under a CC-BY 4.0 licence. This work is licensed under a Creative Commons Attribution 4.0 International License
