What To Do Next
Last updated on 2023-08-31 | Edit this page
Overview
Questions
- How can you improve practices after this workshop?
Objectives
- Learn about other resources to improve good practices
- Reflect on good enough practices appropriate for your career stage
Learning good practices is a long-term process
We have outlined a series of good enough practices for scientific computing. These practices are pragmatic, broadly accessible, and can be applied by both individuals and groups. Most importantly, these practices make researchers more productive individually by enabling them to get more done in less time and with less pain. They also accelerate research as a whole by making work more reproducible.
We have covered good practices in several areas:
- Data management
- Software
- Collaboration
- Project organization
- Keeping track of changes
- Manuscripts
We have explained the shared set of principles behind good practices:
- Planning
- Modular organization
- Names
- Documentation
Collectively, these practices make your work easier to use for other people, including for your future self. Thinking through your work from a collaborator’s point of view is helpful, and ultimately kinder to your future self.
What we left out, and where to learn more
Learning good practices is a long-term process that never stops. We left out many good practices that, although useful, have more niche applications.
We recommend the paper Best Practices in Scientific Computing, especially for those gaining more experience with coding. There are many other useful papers and resources that we have selected.
One of your best resources is your colleagues - talk to them! In our experience, people who have learned good practices themselves are eager to teach them to others.
Different people make different contributions to good practice
Progress in computational good practices comes from different places in the scientific community:
- Funders can require data management plans and provide support for data management.
- Institutions can value the time that goes into data sharing and training.
- Research librarians, research software engineers, and data managers support others in learning and applying good practices.
- PIs and lab heads can require that lab members share code and data, and make it easy for them to do so.
- Lab members can organise “data curation days” and training sessions to share good practices.
- Self-organised groups led by students and postdocs can share ideas and train each other.
- Global organisations like The Carpentries can co-ordinate training and support training materials.
- Professional societies can help to organise training.
It takes time to learn good practices, and time to train others. This time has to be valued by individuals, funders, institutions, and the scientific community.
Developing your own good practices is ultimately a personal journey that will impact your career and impact the people around you. Maybe today you will learn to write README files, or next year you might start using version control, or in the future you could find yourself teaching students to write data management plans for their projects.
Attribution
Content of this episode was adopted after Wilson et al. Good Enough Practices for Scientific Computing.