Assessing data quality in citizen science
Harvard University · University of Maryland, College Park · +2 more institutions
Abstract
Ecological and environmental citizen‐science projects have enormous potential to advance scientific knowledge, influence policy, and guide resource management by producing datasets that would otherwise be infeasible to generate. However, this potential can only be realized if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen‐science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development,…
Citation impact
- FWCI
- 70.51
- Percentile
- 100%
- References
- 64
Authors
4Topics & keywords
- Citizen science
- Replication (statistics)
- Computer science
- Suite
- Quality (philosophy)
- Resource (disambiguation)
- Skepticism
- Data science