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Lab notebooks

Anum Azam Glasgow edited this page Jan 10, 2024 · 12 revisions

Lab policies on record-keeping

  • We upload lab notebooks at least once per semester to the server at /ifs/scratch/lab_notebooks.
  • Group meeting and subgroup slides should also be uploaded to scratch in separate folders.
  • It's OK to keep separate notebooks for wet and dry work, but it doesn't matter as long as everything is recorded.
  • Physical lab notebooks never leave the lab.
  • Include raw data like gels, blots, MS, FPLC traces, enzyme activity data, etc. whenever possible directly in your notebook, or as links to the data or folders containing that data.
  • Include experimental details, instrument settings, calibrations, materials, and anything else that would help someone reproduce the data. If a photo is better, take a photo.
  • Include all the details on how you analyzed your data, just as you would record steps in a protein purification.
  • Version control your software. Frequent commits to Github can help with this! No one is judging you, just git commit all the time.
  • Date everything.

A good lab notebook accomplishes two things:

  1. it keeps track of your activities in extreme detail day to day as part of the broader goals of your project
  2. it provides a way for someone else to exactly reproduce your sample, experimental results, analysis, method, setup, or anything else.

Lab notebooks are also standard materials to establish intellectual property.

Any format is fine for a lab notebook, so long as it does the job (meaning that you update it every day and it complies with our policies). Some options include:

  • OneNote
  • Evernote - has a browser and desktop app option, and easy image incorporation + linking to other pages or folders
  • Jupyter notebooks
  • Microsoft Word or another text editor
  • Benchling
  • Physical paper notebook

Helpful resources

General advice

How to keep a lab notebook.
Ten tips for organizing your lab notebook.

Record keeping for computational work

Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks.
Ten simple rules for reproducible computational research.

Examples

Some pages from Anum's exp lab notebook.
Some pages from Anum's comp lab notebook.
Strains spreadsheet template.
Primers spreadsheet template.

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