10.1371/journal.pone.0158329 Alexis Comber Alexis Comber Peter Mooney Peter Mooney Ross S. Purves Ross S. Purves Duccio Rocchini Duccio Rocchini Ariane Walz Ariane Walz Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover Public Library of Science 2016 landscape features citizen science Recording Land Cover Volunteered VGI analyses citizen science data crowdsourced data contributors crowdsourced data 2016-07-26 13:13:06 Dataset https://plos.figshare.com/articles/dataset/Crowdsourcing_It_Matters_Who_the_Crowd_Are_The_Impacts_of_between_Group_Variations_in_Recording_Land_Cover/3882774 <div><p>Volunteered geographical information (VGI) and citizen science have become important sources data for much scientific research. In the domain of land cover, crowdsourcing can provide a high temporal resolution data to support different analyses of landscape processes. However, the scientists may have little control over what gets recorded by the crowd, providing a potential source of error and uncertainty. This study compared analyses of crowdsourced land cover data that were contributed by different groups, based on nationality (labelled Gondor and Non-Gondor) and on domain experience (labelled Expert and Non-Expert). The analyses used a geographically weighted model to generate maps of land cover and compared the maps generated by the different groups. The results highlight the differences between the maps how specific land cover classes were under- and over-estimated. As crowdsourced data and citizen science are increasingly used to replace data collected under the designed experiment, this paper highlights the importance of considering between group variations and their impacts on the results of analyses. Critically, differences in the way that landscape features are conceptualised by different groups of contributors need to be considered when using crowdsourced data in formal scientific analyses. The discussion considers the potential for variation in crowdsourced data, the relativist nature of land cover and suggests a number of areas for future research. The key finding is that the <i>veracity</i> of citizen science data is not the critical issue per se. Rather, it is important to consider the impacts of differences in the semantics, affordances and functions associated with landscape features held by different groups of crowdsourced data contributors.</p></div>