Download as PDF, PPTX
Uploaded bySIKM
Data Curation - Data probity in a time of COVID
The document discusses the importance of data probity, or ensuring the integrity and quality of data. It notes that data harvested today must be able to answer unknown future questions. It advocates for transparency in research through pre-publishing protocols and data, using open licenses, and supporting peer review. The key aspects of data probity discussed are having an identifiable source, transparent methods, publication before analysis, maintaining point data before aggregation, and having a repeatable, auditable trail.
More Related Content
Similar to Data Curation - Data probity in a time of COVID
![Get a Pinterest account in[ pvatopservice ]sell regular every day.docx](https://cdn.slidesharecdn.com/ss_thumbnails/getapinterestaccountinpvatopservicesellregulareveryday-251030182004-67b5ef9f-thumbnail.jpg?width=640&height=640&fit=bounds)
![Get a Pinterest account in[ pvatopservice ]sell regular every day.pdf](https://cdn.slidesharecdn.com/ss_thumbnails/getapinterestaccountinpvatopservicesellregulareveryday-251030182003-d27b98a4-thumbnail.jpg?width=640&height=640&fit=bounds)
![Amazon Account Audit by Rey Benaid [Amazon PPC Expert]](https://cdn.slidesharecdn.com/ss_thumbnails/amazonppcexpert-accountaudit-reybenaid-251028095011-f4d63eab-thumbnail.jpg?width=640&height=640&fit=bounds)
Data Curation - Data probity in a time of COVID
- 1. Whythawk Data Curation Data probityin a time of COVID SIKM, June 2021
- 2. www.vperemen.com, CC BY-SA4.0, via Wikimedia Commons The screaming need for data Who is effected? How are they effected? What can we do about it? What might happen in response? How do we recover afterwards? Will things ever be the same?
- 6. Badics, CC BY-SA3.0, via Wikimedia Commons The intersection of Policy & Politics Data, analysis & the evidence illusion Post-hoc support & plausible deniability Competing self-interest Changing circumstance, changing evidence
- 7. Harvesting longitudinal datais not joyful Instant answers don’t happen instantly Longitudinal source data are incoherent Data probity takes method, practice & time Esayas Ayele, CC BY-SA 4.0, via Wikimedia Commons
- 8. CDC Global, CCBY-SA 2.0, via flickr What we talk about when we talk about probity Identifiable source Transparent methods Publication before analysis Point data before aggregation Repeatable, auditable trail
- 9. Transparency in practice Pre-publicationof research protocol, methods & data Systematic review Open licences No trust without support for peer review & validation Yakuzakorat, CC BY 4.0, via Wikimedia Commons
- 10. Photo by ClayBanks on Unsplash Protocols & ambiguity Maintain your source Pick sensible defaults Make no destructive changes Document every action Expect to be audited
- 11. Photo by LuboMinar on Unsplash Uncertainty & the distant future Data harvested today must answer unknown questions to unknown problems in an unknown – but different – future environment
- 12. Poverty is expensive Alegacy of futility risks becoming self-perpetuating Olga Ernst, CC BY-SA 4.0, via Wikimedia Commons
- 13. A history in35 million rows
- 14. Where are businessescompared to where we think they are? Does a change in tax rates cause business closure? How should we measure energy consumption? Who wins & loses from COVID commute changes?
- 15. Who wants tobe a millionaire?
- 16. Photo by SylvieTittel on Unsplash Protocol with sensible defaults 1. All units are occupied & pay full rates. 2. When data are ambiguous, refer to 1. 3. Ask for data, even when you know they’ll say no. 4. Never delete anything. 5. Document everything. 6. When in doubt, ask the data source. 7. Accept the weird but keep looking for answers. 8. Ensure the process is public.
- 17. 1. Track everystep
- 18. 2. Disclose everyrequest
- 19. 3. Non-destructive auditabletransformation
- 20. 4. Always readyto explain
- 21. 5. Make thedata useful
- 22.
- 23. Photo by SylvieTittel on Unsplash Sqwyre data probity protocol 1. Instant answers don’t happen instantly 2. Data probity takes method, practice & patience 3. Maintain all source data 4. Pick sensible & transparent defaults 5. Transformations must be documented 6. Make no destructive changes 7. Point data before aggregation or analysis 8. Open licences to encourage use & reuse 9. Collaborate to make the data wanted & useful 10. Be ready to explain & be audited
- 24. Hansueli Krapf Thisfile was uploaded with Commonist., CC BY-SA 3.0, via Wikimedia Commons Know your business
- 25.