CoCoRaHS: Citizen Science Network Provides Useful Data and Improves Science Process Skills

Sunday, 15 February 2015
Exhibit Hall (San Jose Convention Center)
Noah D. Newman, CoCoRaHS/Colorado State University, Fort Collins, CO
Background: The Community Collaborative Rain, Hail and Snow network (CoCoRaHS) is a citizen science project with volunteers from all ages and backgrounds who measure and map precipitation from their home, business or school locations.  Starting in 1998 in Fort Collins, Colorado, CoCoRaHS has now grown to all 50 states including Washington, D.C. and Puerto Rico as well as Canada.  CoCoRaHS’ goals are to provide a dense network of high quality precipitation data for scientific use as well as to provide educational opportunities for the public. Methods: To achieve these goals, volunteers use affordable yet accurate instruments while following standard protocols to provide precipitation data to the CoCoRaHS website - which is immediately free and open to the public in both map and list formats.  Educational opportunities are provided through the website and through several outlets including daily messages at the point of data entry, monthly e-mail newsletters, topical webinars with experts in the field and social media outlets. Results: Proving the value and validity of the measurements, users of the data include decision makers from NOAA’s River Forecast Centers and National Weather Service, the National Drought Mitigation Center, Coastal Fisheries, local governments concerned with urban flooding, as well as many others.  Partially funded by NOAA and NSF, an external evaluator recently conducted an extensive evaluation on CoCoRaHS participants showing strong evidence that CoCoRaHS volunteers (both adults as well as K-12 students) show an increase in their science process skills, i.e. possessing the skills necessary to engage in the scientific process as well as skills for scientific and critical thinking. Conclusion: Since inception, CoCoRaHS has proven that ordinary citizens can not only provide high quality data that are useful but at the same time can learn and improve on their own science process skills.