Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Communications of the ACM
A Framework for Analysis of Data Quality Research
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How and why Wikipedia works: an interview with Angela Beesley, Elisabeth Bauer, and Kizu Naoko
Proceedings of the 2006 international symposium on Wikis
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
The Wealth of Networks: How Social Production Transforms Markets and Freedom
The Wealth of Networks: How Social Production Transforms Markets and Freedom
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information quality work organization in wikipedia
Journal of the American Society for Information Science and Technology
Representing community: knowing users in the face of changing constituencies
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Pathfinder: an online collaboration environment for citizen scientists
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A jury of your peers: quality, experience and ownership in Wikipedia
Proceedings of the 5th International Symposium on Wikis and Open Collaboration
Creek watch: pairing usefulness and usability for successful citizen science
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 2013 conference on Computer supported cooperative work
Free as in puppies: compensating for ict constraints in citizen science
Proceedings of the 2013 conference on Computer supported cooperative work
wq: a modular framework for collecting, storing, and utilizing experiential VGI
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Capturing quality: retaining provenance for curated volunteer monitoring data
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
Hi-index | 0.00 |
Citizen science is becoming more valuable as a potential source of environmental data. Involving citizens in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. However, a common concern among domain experts is the presumed lower quality of data submitted by volunteers. In this paper, we explore data quality assurance practices in River Watch, a community-based monitoring program in the Red River basin. We investigate how the participants in River Watch understand and prioritize data quality concerns. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, but there remain areas where technological intervention may help. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program. We draw implications for the design of quality assurance mechanisms for River Watch and other citizen science projects.