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The last few years have seen the emergence of a large number of worldwide web portals where volunteers report and collect observations of plants and animals, share these reports with other users, and provide data for scientific research purposes along the way. Activities engaging citizens in the collection of scientific data or in solving scientific problems are collectively called citizen science. Data quality is a vital issue in this field. Currently, reports of species observations from citizen scientists are often validated manually by experts as a means of quality control. Experts evaluate the plausibility of a report based on their own expertise and experience. However, a rapid growth in the quantity of reports to be processed makes this approach increasingly less feasible, creating a need for methods supporting (semi)automatic validation of observation data. This aim is achieved primarily by analysing the spatial and temporal context of the data. Relevant context information can be provided by existing observation data, as well as by spatial data of environmental factors, or other spatio-temporal factors impacting the distribution of species, or the process of observation and contribution itself. It is very important that the specific properties of data emerging from citizen science origins are taken into account. These data are often not produced in a systematic way, resulting in (for instance) spatial and temporal incompleteness. Also, the data structure is not only determined by the natural spatio-temporal patterns of species distribution, but by other factors such as the behaviour of contributors or the design of the citizen science project that produced the data as well.

Data quality in crowdsourcing for biodiversity research: issues and examples