Mechanisms for Data Quality and Validation in Citizen Science
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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.
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Mechanisms for Data Quality and Validation in Citizen Science
- 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
Editor's Notes
- #6: Rating = classification or judgment tasks, admittedly not the clearest wording, but no one corrected this in text responsesPercentage = percentage of responding projects that use each method
- #7: Percentage = Percentage of responding projects that use this combination of methodsThere were a few other combinations that a handful of projects used; these were the dominant ones.Surprised to see so many with photos, as they are hard to use and store, and the frequency of using paper data sheets
- #8: Note that we did ask about numbers of contributions, but the units of contribution for each project (and even the way they count volunteers) were so different that they couldn’t be used for analysis
- #11: Split framework of mechanisms in two for ease of viewing; these are methods that address the protocol as the presumed source of errorStarred items address errors arising from both protocols and participants
- #12: These methods all address expected errors form participants, focusing primarily on skill evaluation and filtering or review for unusual reports