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COVID-19 Analytics in Jupyter: Intuitive Provenance Integration using ProvIt

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COVID-19 Analytics in Jupyter: Intuitive Provenance Integration using ProvIt

  1. 1. COVID-19 Analytics in Jupyter: Intuitive Provenance Integration using ProvIt Martin Chapman, Elliot Fairweather, Asfand Khan and Vasa Curcin King’s College London Provenance-enabled Jupyter environment: https://github.com/kclhi/jupyter
  2. 2. How do I properly specify the data I want to capture?
  3. 3. How do I properly specify the data I want to capture?
  4. 4. How do I properly specify the data I want to capture? Provenance Authoring Webapp
  5. 5. How do I properly specify the data I want to capture? Provenance Authoring Webapp
  6. 6. How do I extract this data from a running system?
  7. 7. How do I extract this data from a running system?
  8. 8. How do I extract this data from a running system?
  9. 9. How do I extract this data from a running system? Pattern: import [ ] Action: extract
  10. 10. How do I extract this data from a running system? Pattern: import [ ] Action: extract Pattern: import [ ] Action: use string ‘python’
  11. 11. How do I extract this data from a running system? Pattern: import [ ] Action: extract Pattern: import [ ] Action: use string ‘python’ Template Service Client
  12. 12. How do I get my data back, once it’s been stored?
  13. 13. How do I get my data back, once it’s been stored? Query Templates
  14. 14. How do I get my data back, once it’s been stored? Query Templates
  15. 15. How do I get my data back, once it’s been stored? Query Templates
  16. 16. COVID-19 Analytics in Jupyter Data relating to 1468 patients who tested positive at Guy's and St. Thomas' NHS Foundation Trust (GSTT), analysed by a group of researchers between April 2020 and February 2021 at King's College London.
  17. 17. COVID-19 Analytics in Jupyter
  18. 18. COVID-19 Analytics in Jupyter
  19. 19. COVID-19 Analytics in Jupyter
  20. 20. COVID-19 Analytics in Jupyter When this is expanded across multiple saves, we can see what was imported when, and by whom; we can also see more complex information, such as common libraries
  21. 21. COVID-19 Analytics in Jupyter: Intuitive Provenance Integration using ProvIt Martin Chapman, Elliot Fairweather, Asfand Khan and Vasa Curcin King’s College London Provenance-enabled Jupyter environment: https://github.com/kclhi/jupyter

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