data management for citizen science

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!

andrea.wiggins@cornell.edu@AndreaWiggins

dataone.orgbirds.cornell.educitizenscience.organdreawiggins.com