Please include these references where applicable

[malvikasharan]

Summary

• Here I have collated all referenced initially compiled in https://github.com/alan-turing-institute/data-training-for-bioscience/issues.
• Please do comment below if we have not included some resources that should be added.

References:

Turing resources

• The Turing Way materials: https://the-turing-way.netlify.app/welcome
• RSE4DataScience: https://github.com/alan-turing-institute/RSE4DataScience18
  • https://alan-turing-institute.github.io/rds-course/modules/m1/1.1-WhatIsDataScience.html
• RDS course - under development: https://github.com/alan-turing-institute/rds-course
• Data Science Skills: https://github.com/alan-turing-institute/DataScienceSkills
• RSE Summer School: https://github.com/alan-turing-institute/DH-RSE-Summer-School

Other resources

• RDA-CODATA Summer School: https://github.com/CODATA-RDA-DataScienceSchools (Check with Patricia H. and DCC)
• EMBL-EBI resources: https://competency.ebi.ac.uk/ -> The UX aspect of this resource is also quite great.
  • https://twitter.com/EBItraining/status/1427197636730306563?s=20
• Open, Data-driven Science for Decision Makers
• https://training.csx.cam.ac.uk/bioinformatics/course/bioinfo-python/
• Data science in Python: https://training.csx.cam.ac.uk/bioinformatics/event/3748813
• API and biocuration: https://github.com/malvikasharan/2019-Biocuration-Module-2
  • API: EBI version of databases - proteins
  • Features: localization, pathways, intact (do two proteins interact?)
• Also check resources from: https://softwaresaved.github.io/python-intermediate-development/
• Foundations of Open Science Skills 2021
• Markowetz, F. (2017). All biology is computational biology. PLoS Biol., 15(3), e2002050. doi: 10.1371/journal.pbio.2002050).
• EOSC-Pillar RDM training catalogue, potentially anything here is relevant (The Turing Way is listed there)

Carpentries Resources

• Genomics Lessons: https://datacarpentry.org/lessons/#genomics-workshop

• Ecology Lessons: https://datacarpentry.org/lessons/#ecology-workshop

  Going through the list of current incubators, here a a few projects that deal with ML already that we should connect with and reuse

• https://carpentries-incubator.github.io/ml4bio-workshop/

• https://carpentries-incubator.github.io/machine-learning-novice-python/

• https://carpentries-incubator.github.io/machine-learning-novice-sklearn/

• http://datascibc.org/Data-Science-for-Docs/

• https://carpentries-incubator.github.io/machine-learning-librarians-archivists/

• https://carpentries-incubator.github.io/bioc-intro/

Packaging and publishing

• https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/
• https://carpentries-incubator.github.io/python-packaging-publishing/

different levels of cloud computing and HPC training materials

• http://www.hpc-carpentry.org/
• https://carpentries-incubator.github.io/hpc-intro/

AI and Machine Learning

• https://www.nature.com/articles/nrg3920
• https://turing-commons.netlify.app/
• https://medium.com/coders-mojo/quick-recap-60-days-of-data-science-and-machine-learning-series-457ca0e98279?sk=c681bf663d6a0311454a43f755e3ed87
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/
• https://pubmed.ncbi.nlm.nih.gov/34398394/
• https://www.astrazeneca.com/r-d/data-science-and-ai.html
• https://www.ddw-online.com/ground-breaking-techniques-in-the-use-of-ai-to-predict-cell-growth-8646-202012/
• https://www.nature.com/articles/s41524-019-0221-0