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Data Management for Citizen Science
1. Data Management for Citizen Science
Challenges & Opportunities for USGS Leadership
Andrea Wiggins
Postdoctoral Fellow
DataONE & Cornell Lab of Ornithology
12 September, 2012
USGS CDI Citizen Science workshop
2. DataONE PPSR Working Group
Purpose:
• Improve quality, quantity, and accessibility of PPSR data
• Advance integration of PPSR data in conventional science
Products:
• Data Management Guide for PPSR - coming soon!
• Articles in August FREE special issue
• Data quality & validation paper
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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 I
Who 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 should
Who 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?
7. Policy? What policy?
Data policies = boring
Data policies = hard
• Ownership, sharing, use, access, challenge, etc.
• Lots of decisions, vague consequences
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8. Policy? What policy?
Data policies = boring
Data policies = hard
• Ownership, sharing, use, access, challenge, etc.
• Lots of decisions, vague consequences
Need examples of carefully crafted policies
• Story of the data + policy that resulted
• USGS is way ahead of the game!
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10. Cyberinfrastructure
Technology is a major pain point
Platforms needed
• Transcription, observation, processing
• Ongoing support & development required
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11. Cyberinfrastructure
Technology is a major pain point
Platforms needed
• Transcription, observation, processing
• Ongoing support & development required
Who is going to pay?
• <insert sound of crickets here>
http://www.flickr.com/photos/gravitywave/1303504847/ 11
12. Data quality perceptions
No more reinvention
• The data are as good as your project design
• Reuse protocols & technologies
• Replicability -> reliability
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13. Data quality perceptions
No more reinvention
• The data are as good as your project design
• Reuse protocols & technologies
• Replicability -> reliability
No 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
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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
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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 participants
Better data management planning than average
• 1/3 had NO data management plan at all!
• Government-funded projects: yes, for some data
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17. Survey says...
Tools & resources strongly desired across categories,
especially:
• Analyzing & visualizing data
• Documenting & describing data
• Training
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18. Survey says...
Tools & resources strongly desired across categories,
especially:
• Analyzing & visualizing data
• Documenting & describing data
• Training
Top priorities for improvement (high agreement)
1. Analyzing & visualizing data
2. Documenting & describing data
3. Long-term storage
4. Establishing & updating data policies
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21. Leading the way
Be an exemplar in data sharing & community building
Make your data policies easy to find & emulate
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22. Leading the way
Be an exemplar in data sharing & community building
Make your data policies easy to find & emulate
Share your platforms with everyone, not just New Zealand!
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23. Leading the way
Be an exemplar in data sharing & community building
Make your data policies easy to find & emulate
Share your platforms with everyone, not just New Zealand!
Make data quality obvious
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24. Leading the way
Be an exemplar in data sharing & community building
Make your data policies easy to find & emulate
Share your platforms with everyone, not just New Zealand!
Make data quality obvious
USGS brings more credibility to citizen 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.