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Data Management for Citizen Science

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Presentation for the USGS Community Data Integration workshop on Ctizen Science

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Data Management for Citizen Science

  1. 1. Data Management for Citizen ScienceChallenges & Opportunities for USGS LeadershipAndrea WigginsPostdoctoral FellowDataONE & Cornell Lab of Ornithology12 September, 2012USGS CDI Citizen Science workshop
  2. 2. DataONE PPSR Working GroupPurpose: • Improve quality, quantity, and accessibility of PPSR data • Advance integration of PPSR data in conventional scienceProducts: • Data Management Guide for PPSR - coming soon! • Articles in August FREE special issue • Data quality & validation paper 2
  3. 3. How long will it What is a data take to get management enough data? plan? Plan Analyze Collect How can I assure quality of volunteers’ What tools data? do I use? Integrate Assure What data about volunteers should IWho can help keep or share? me? Discover Describe Preserve Should I share What if the data are raw data with used for commercial known errors? profit?
  4. 4. How long will it What is a data take to get management enough data? plan? Plan Analyze Collect How can I assure quality of What tools volunteers’ data? do I use? Integrate Assure What data about volunteers shouldWho can help I keep or share? me? Discover Describe Preserve Should I share What if the data are raw data with used for commercial known errors? profit?
  5. 5. Citizen science data challengesData policiesCyberinfrastructureData quality 5
  6. 6. Policy? What policy?Data policies = boring http://www.flickr.com/photos/escapist/107455718/ 6
  7. 7. Policy? What policy?Data policies = boringData policies = hard • Ownership, sharing, use, access, challenge, etc. • Lots of decisions, vague consequences 7
  8. 8. Policy? What policy?Data policies = boringData policies = hard • Ownership, sharing, use, access, challenge, etc. • Lots of decisions, vague consequencesNeed examples of carefully crafted policies • Story of the data + policy that resulted • USGS is way ahead of the game! 8
  9. 9. CyberinfrastructureTechnology is a major pain point 9
  10. 10. CyberinfrastructureTechnology is a major pain pointPlatforms needed • Transcription, observation, processing • Ongoing support & development required 10
  11. 11. CyberinfrastructureTechnology is a major pain pointPlatforms needed • Transcription, observation, processing • Ongoing support & development requiredWho is going to pay? • <insert sound of crickets here> http://www.flickr.com/photos/gravitywave/1303504847/ 11
  12. 12. Data quality perceptionsNo more reinvention • The data are as good as your project design • Reuse protocols & technologies • Replicability -> reliability 12
  13. 13. Data quality perceptionsNo more reinvention • The data are as good as your project design • Reuse protocols & technologies • Replicability -> reliabilityNo more excuses • All scientific data have errors • Our data are just like yours...except we have more friends • Document data collection & QA/QC in excruciating detail 13
  14. 14. Survey says... 14
  15. 15. Survey says...Least satisfied with current: • Process for sharing project data with colleagues, researchers, and/or participants • Ways of presenting project data/results to participants 15
  16. 16. Survey says...Least satisfied with current: • Process for sharing project data with colleagues, researchers, and/or participants • Ways of presenting project data/results to participantsBetter data management planning than average • 1/3 had NO data management plan at all! • Government-funded projects: yes, for some data 16
  17. 17. Survey says...Tools & resources strongly desired across categories,especially: • Analyzing & visualizing data • Documenting & describing data • Training 17
  18. 18. Survey says...Tools & resources strongly desired across categories,especially: • Analyzing & visualizing data • Documenting & describing data • TrainingTop priorities for improvement (high agreement) 1. Analyzing & visualizing data 2. Documenting & describing data 3. Long-term storage 4. Establishing & updating data policies 18
  19. 19. Leading the way 19
  20. 20. Leading the wayBe an exemplar in data sharing & community building 20
  21. 21. Leading the wayBe an exemplar in data sharing & community buildingMake your data policies easy to find & emulate 21
  22. 22. Leading the wayBe an exemplar in data sharing & community buildingMake your data policies easy to find & emulateShare your platforms with everyone, not just New Zealand! 22
  23. 23. Leading the wayBe an exemplar in data sharing & community buildingMake your data policies easy to find & emulateShare your platforms with everyone, not just New Zealand!Make data quality obvious 23
  24. 24. Leading the wayBe an exemplar in data sharing & community buildingMake your data policies easy to find & emulateShare your platforms with everyone, not just New Zealand!Make data quality obviousUSGS brings more credibility to citizen science 24
  25. 25. Thanks!andrea.wiggins@cornell.edu@AndreaWigginsdataone.orgbirds.cornell.educitizenscience.organdreawiggins.com 25

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