Resources
Last updated on 2023-08-31 | Edit this page
This page has a collection of resources on good practices in scientific computing. Feel free to refer to them.
Some other lessons with different emphasis
The FAIR principles describe work that is Findable, Accessible, Interoperable and Reusable.
These lessons are in a similar format and discuss good practices within the FAIR principles:
- FAIR in Biological practice, a 2-day comprehensive introduction
- FAIR for Leaders, 1-day lesson aimed at established PIs and institutional leaders who set policy and drive changes. Note, this lesson is under construction as of December 2022
Further Carpentries lessons
Many other Carpentries lessons may help further your learning:
- Data management
- Data Carpentry Ecology lessons on spreadsheets and OpenRefine
- Data Carpentry Social Sciences lessons on spreadsheets and OpenRefine
- Software Carpentry lesson on SQL
- Software and code
- Check out the Software Carpentry on unix, R, or Python.
- Project organization
- Data Carpentry Genomics lesson on organization
- Keeping track of changes
Papers and sites on open and good practices
- Five selfish reasons to work reproducibly. Florian Markowetz, Genome Biology
- When will ‘open science’ become simply ‘science’? Mick Watson, Genome Biology
- FAIR principles in science
- Wellcome Trust blogpost on their open data policy
- UK Research and Innovation (government science funding) open data policy
- A view against open data and analysis in the New England Journal of Medicine, and a response arguing with it from Carl Bergstrom
- The research parasite awards, inspired by the above debate
- Ten Simple rules collection of articles from PLoS, covering Data, coding, careers, writing, and more
- PLoS Computational Biology Education section
Ten simple rules for reproducible computational research
- Keep Track of How Every Result Was Produced
- Avoid Manual Data Manipulation Steps
- Track Versions of All External Programs Used
- Version Control Your Protocols/Scripts
- Record All Intermediate Results
- Track Relevant Sources of Randomness
- Store Raw Data behind Plots
- Allow Layers of Detail to Be Inspected
- Connect Statements to Underlying Results
- Share Scripts, Runs, and Results
Research is changing
For example,“papers” aren’t paper any more
- Executable research articles in eLife: https://elifesciences.org/for-the-press/eb096af1/elife-launches-executable-research-articles-for-publishing-computationally-reproducible-results
- Interactive figures in f1000 articles: https://blog.f1000.com/2017/07/19/so-long-static-we-now-support-interactive-ploty-figures-in-our-articles/
- Preprints: arxiv, biorxiv,…
Resources: External Training
Many institutions and societies run training courses, for example:
- The Carpentries teaches foundational coding and data science skills to researchers worldwide
- ELIXIR training platform for bioinformatics
- EMBL
- Cold Spring Harbor
- Woods Hole
For more courses:
- look at the posters on the walls around your lab
- ask for advice!
- talk to the head of your PhD program, postdoctoral fellows office, etc.
- google “bioinformatics course”, etc.
An example of one institution: Resources at the University of Edinburgh
- Research Data Service (plans, archiving, skills)
- Digital Research Services (data management, electronic lab notebooks)
- EdCarp for Data/Software Carpentry workshops on computing skills
- Coding Club, based out of Geosciences
- Quantitative imaging network
- Edinburgh Genomics, including training on programming and data analysis
- Clinical Research Facility, including training courses
- Statistics Consulting Unit
- Bioinformatics users group covering Scotland