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Summary docs from the data-driven-discussions

Awesome papers on best practices

Ten Simple Rules for a Computational Biologists Laboratory Notebook - more about labbooks in general, but interesting. Stern warning warning - "If you do not systematically archive changes, it will be very difficult or impossible to track the codes that you used to generate certain results. "

Joel Dudley's 2009 remarks on useful skills for bioinformatics have held up well. See also a gist of the talk here.

Who says nerds have no sense of humour?

(The first of these has been cited considerably more than the second).

If you have 10 USD spare, consider getting Jeff Leek's book 'How to be a Modern Scientist' - excerpts available here

Newsletters

One PhD student’s curated list of 21 newsletters to help you keep up with AI news and research Here

PCA

Learning Principal Component Analysis by Using Data from Air Quality Networks

Comparison of models and observations

Alex's slides

Follow up from ATA - Root mean square error (RMSE) or mean absolute error (MAE): when to use them or not

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