- 1 Introduction to Software for Open and Reproducible Research
- 1.1 Content overview
- 1.2 Learning objectives
- 1.3 Completion criteria
- 1.4 Audience
- 1.5 Prerequisite skills, expertise and experience
- 1.6 Overall likely time commitment
- 1.7 Event date and time
- 1.8 Registration deadline
- 1.9 Training partner
- 1.10 Location
- 1.11 Cost to institution per participant
- 1.12 Number of attendees
- 1.13 Application
Introduction to Software for Open and Reproducible Research
This short (2-day) course is teaching tools and practice for developing FAIR (Findable, Accessible, Interoperable and Reusable) research software to support open and reproducible research.
After attending this training, you will be able to:
- list challenges typically faced by researchers developing software and managing data for modern computational research, including requirements commensurate with the FAIR (Findable, Accessible, Interoperable, Reusable) principles;
- list some tools and practices that can help make your research, data and software open and FAIR;
- automate your research and enable replication of your research results by writing software to implement the research methodology;
- share and version control your software using Git and GitHub;
- list best practices for developing and sharing open and sustainable software (including writing readable code, code documentation, licencing and citation);
- assess if the code does what it intends to do;
- list tools and techniques for collaborative and sustainable software development and maintenance.
To be confirmed.
- Post-graduate students or early career researchers who are starting their research projects and want to develop software to support their research using established best practices
- Researchers who had foundational software training before but wish to refresh, reinforce or improve their skills and practices in the wider context of FAIR research and sharing and writing software for open and reproducible research
Prerequisite skills, expertise and experience
Before joining this training, participants should have foundational knowledge of Python. For example, attending a Software Carpentry/Data Carpentry/Library Carpentry or a similar introductory Python course could help with this requirement and gaining necessary prerequisite skills.
Some foundational knowledge of shell and version control tool git is desirable.
Overall likely time commitment
- 2×2 training hours
- 1 office hour
- 6 to 8 hours creating and presenting deliverables
Event date and time
1st to 4th July 2024 (four half-days)
8th March 2024
Cost to institution per participant
Number of attendees