Data Management for Citizen Science
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Presentation for the USGS Community Data Integration workshop on Ctizen Science

Presentation for the USGS Community Data Integration workshop on Ctizen Science

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  • When it comes to the data life cycle that Bill mentioned yesterday, many scientists are grappling with questions about data management. Questions like... [READ OFF] These are just a few questions out of many that PPSR project leaders have discussed with me, but as you might have noticed, most of them are questions that are equally applicable to conventional scientific research.
  • In fact, the only thing I can see that is truly unique about PPSR data is the involvement of volunteers. At the end of the day, data is data. So I hope it comes as some comfort for everyone here to know that there ’ s nothing unusual in these challenges, with the exception of needing to manage aspects of the data that are directly related to volunteers.

Data Management for Citizen Science Presentation Transcript

  • 1. Data Management for Citizen ScienceChallenges & Opportunities for USGS LeadershipAndrea WigginsPostdoctoral FellowDataONE & Cornell Lab of Ornithology12 September, 2012USGS CDI Citizen Science workshop
  • 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. 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. 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. Citizen science data challengesData policiesCyberinfrastructureData quality 5
  • 6. Policy? What policy?Data policies = boring http://www.flickr.com/photos/escapist/107455718/ 6
  • 7. Policy? What policy?Data policies = boringData policies = hard • Ownership, sharing, use, access, challenge, etc. • Lots of decisions, vague consequences 7
  • 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. CyberinfrastructureTechnology is a major pain point 9
  • 10. CyberinfrastructureTechnology is a major pain pointPlatforms needed • Transcription, observation, processing • Ongoing support & development required 10
  • 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. Data quality perceptionsNo more reinvention • The data are as good as your project design • Reuse protocols & technologies • Replicability -> reliability 12
  • 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. Survey says... 14
  • 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. 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. Survey says...Tools & resources strongly desired across categories,especially: • Analyzing & visualizing data • Documenting & describing data • Training 17
  • 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. Leading the way 19
  • 20. Leading the wayBe an exemplar in data sharing & community building 20
  • 21. Leading the wayBe an exemplar in data sharing & community buildingMake your data policies easy to find & emulate 21
  • 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. 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. 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. Thanks!andrea.wiggins@cornell.edu@AndreaWigginsdataone.orgbirds.cornell.educitizenscience.organdreawiggins.com 25