This document summarizes a survey of data quality and validation mechanisms used in citizen science projects. The survey found that most projects use expert review and that projects use an average of 2.5 validation methods, often in combination. Larger projects and those with more funding tend to use fewer validation methods that require more human resources. Common validation methods included expert review, photo submissions, paper data sheets, and replication by multiple participants. Future work should focus on validation of data analysis methods and developing tools to help projects plan quality assurance procedures.

1. Mechanisms for
Data Quality and Validation
in Citizen Science
A. Wiggins, G. Newman, R. Stevenson & K. Crowston
Presented by Nathan Prestopnik

2. Motivation
 Data quality and validation are a primary concern
for most citizen science projects
 More contributors = more opportunities for error
 There has been no review of appropriate data
quality and validation mechanisms
 Diverse projects face similar challenges
 Contributors’ skills and scale of participation are
important considerations in ensuring quality

3. Methods
 Survey
 Questionnaire with 70 items, all optional
 63 completed questionnaires representing 62 projects
 Mostly small-to-medium sized projects in US, Canada,
UK; most focus on monitoring and observation
 Inductive development of framework
 Based on survey results and authors’ direct experience
with citizen science projects

4. Survey: Resources
 FTEs: 0 – 50+
 Average: 2.4; Median: 1
 Often small fractions of several individuals’ time
 Annual budgets: $125 - $1,000,000
 Average: $105,000; Median: $35,000; Mode: $20,000
 Up to 5 different funding sources, usually grants, in-
kind contributions (staff time), & private donations
 Age/duration: -1 to 100 years
 Average age: 13 years; Median: 9 years; Mode: 2 years

5. Survey: Methods Used
Method n Percentage
Expert review 46 77%
Photo submissions 24 40%
Paper data sheets submitted along with online entry 20 33%
Replication/rating by multiple participants 14 23%
QA/QC training program 13 22%
Automatic filtering of unusual reports 11 18%
Uniform equipment 9 15%
Validation planned but not yet implemented 5 8%
Replication/rating, by the same participant 2 3%
Rating of established control items 2 3%
None 2 3%
Not sure/don’t know 2 3%

6. Survey:
Combining Methods
Methods n Percentage
Single method 10 17%
Multiple methods, up to 5 (average 2.5) 45 75%
Expert review + Automatic filtering 11 18%
Expert review + Paper data sheets 10 17%
Expert review + Photos 14 23%
Expert review + Photos + Paper data sheets 6 10%
Expert review + Replication, multiple 10 17%

7. Survey:
Resources & Methods
 Number of validation methods and staff are
positively correlated (r2 = 0.11)
 More staffing = more supervisory capacity
 Number of validation methods and budget are
negatively correlated (r2 = -0.15)
 If larger budgets means more contributors, this
constrains scalability of multiple methods
 Larger projects may use fewer but more sophisticated
mechanisms
 Suggests that human-supervised methods don’t scale

8. Survey:
Other Validation Options
 “Please describe any additional validation methods
used in your project”
 Several projects rely on personal knowledge of
contributing individuals for data quality
 Not scientifically robust, but understandably relevant
 Most comments referred to details of expert review
 Reinforces the perceived value of expertise
 Reporting interface and associated error-checking is
often overlooked, but provides important initial data
verification

9. Choosing Mechanisms
 Data characteristics to consider when choosing
mechanisms to ensure quality
 Accuracy and precision: taxonomic, spatial, temporal,
etc.
 Error prevention: malfeasance (gaming the system),
inexperience, data entry errors, etc.
 Evaluate assumptions about error and accuracy
 Where does error originate? How do mechanisms
address this? At what step in the research process?
How transparent is data review and outcomes? How
much data will be reviewed? In how much detail?

10. Mechanisms: Protocols
Mechanism Process Type/Detail
QA project plans Before SOP in some areas
Repeated samples/tasks During By multiple participants, single
participant, or experts (calibration)
Tasks involving control During Contributions compared to known states
items
Uniform/calibrated During Used for measurements; cost/scale
equipment tradeoff; who pays?
Paper data sheets + During Extended details, verifying data entry
online entry* accuracy
Digital vouchers* During Photos, audio, specimens/archives
Data triangulation, After Corroboration from other data sources;
normalization, mining* statistical & computer science methods
Data documentation* After Provide metadata about processes

11. Mechanisms: Participants
Mechanism Process Types/Details
Participant training Before, Initial; Ongoing; Formal QA/QC
During
Participant testing Before, Following training; Pre/test-retest
During
Rating participant During, Unknown to participant; Known to
performance After participant
Filtering of unusual During, Automatically; Manually
reports After
Contacting participants After May alienate/educate contributors
about unusual reports
Automatic recognition After Techniques for image/text processing
Expert review After By professionals, experienced contributors,
or multiple parties

12. Discussion
 Need to pay more attention to way that data are
created, not just protocols but also qualities of data
like accuracy, precision
 Clear need for quality/validation mechanisms for
analysis, not only for data collection/processing
 Data mining techniques
 Spatio-temporal modeling
 Scalability of validation may be limited
 May need to plan different quality management
techniques based on expected/actual project growth

13. Future Work
 Most projects worry more about contributor
expertise than appropriate analysis methods
 Resources are needed to support suitable analysis
approaches and tools
 Comparative valuation of the efficacy of the data
quality and validation mechanisms identified
 Develop a QA/QC planning and evaluation tool
 Develop examples of appropriate data
documentation for citizen science projects
 Necessary for peer review, data re-use

14. Thanks!
 Nate Prestopnik
 DataONE working group on Public Participation in
Scientific Research
 US NSF grants 09-43049 & 11-11107