data management for citizen science
DESCRIPTION
Presentation for the USGS Community Data Integration workshop on Ctizen ScienceTRANSCRIPT
Data Management for Citizen Science
Challenges & Opportunities for USGS Leadership
Andrea WigginsPostdoctoral 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
PlanPlan
CollectCollect
AssureAssure
DescribeDescribe
PreservePreserve
DiscoverDiscover
IntegrateIntegrate
AnalyzeAnalyze
Should I share raw data with known errors?
What data about volunteers should I
keep or share?
How can I assure quality of volunteers’
data?
Who can help me?
What is a data management
plan?
What tools do I use?
How long will it take to get
enough data?
What if the data are used for commercial
profit?
PlanPlan
CollectCollect
AssureAssure
DescribeDescribe
PreservePreserve
DiscoverDiscover
IntegrateIntegrate
AnalyzeAnalyze
Should I share raw data with known errors?
What data about volunteers
should I keep or share?
How can I assure quality of
volunteers’ data?
Who can help me?
What is a data management
plan?
What tools do I use?
How long will it take to get
enough data?
What if the data are used for commercial
profit?
5
Citizen science data challenges
Data policies
Cyberinfrastructure
Data quality
6
Policy? What policy?
Data policies = boring
http://www.flickr.com/photos/escapist/107455718/
7
Policy? What policy?
Data policies = boring
Data policies = hard•Ownership, sharing, use, access, challenge, etc.•Lots of decisions, vague consequences
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!
9
Cyberinfrastructure
Technology is a major pain point
10
Cyberinfrastructure
Technology is a major pain point
Platforms needed•Transcription, observation, processing•Ongoing support & development required
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/
12
Data quality perceptions
No more reinvention•The data are as good as your project design•Reuse protocols & technologies•Replicability -> reliability
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
14
Survey says...
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
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
17
Survey says...
Tools & resources strongly desired across categories, especially: •Analyzing & visualizing data•Documenting & describing data• Training
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 data2. Documenting & describing data3. Long-term storage4. Establishing & updating data policies
19
Leading the way
20
Leading the way
Be an exemplar in data sharing & community building
21
Leading the way
Be an exemplar in data sharing & community building
Make your data policies easy to find & emulate
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!
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
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
25
Thanks!
[email protected]@AndreaWiggins
dataone.orgbirds.cornell.educitizenscience.organdreawiggins.com