Abstract
Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects.
Highlights
Data quality and quality control are frequently discussed for citizen science projects because these data are generally perceived to be less accurate than traditional data due to human errors
This paper focuses on the value of the online CrowdWater game to check and improve the accuracy of crowdsourced water level class data submitted via the CrowdWater app
The CrowdWater game allows checking and correcting crowdsourced water level class data based on the pictures that are submitted by citizen scientists through the CrowdWater app
Summary
Data quality and quality control are frequently discussed for citizen science projects because these data are generally perceived to be less accurate than traditional data due to human errors. Wiggins et al [8] summarised 18 approaches for data quality control, which can be grouped into approaches before, during and after data collection. These include training participants and providing tutorial materials [9,10], filtering of the incoming data based on the plausibility of the data and the likelihood for a particular geographic region [6,10,11,12,13,14], bias correction, for example for presence only data [10,11,15,16], and review of incoming data [4,8,11,17,18]
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