Community-based Data Validation Practices in Citizen Science

Technology-supported citizen science has created huge volumes of data with increasing potential to facilitate scientific progress, however, verifying data quality is still a substantial hurdle due to the limitations of existing data quality mechanisms. In this study, we adopted a mixed methods approach to investigate community-based data validation practices and the characteristics of records of wildlife species observations that affected the outcomes of collaborative data quality management in an online community where people record what they see in the nature. The findings describe the processes that both relied upon and added to information provenance through information stewardship behaviors, which led to improved reliability and informativity. The likelihood of community-based validation interactions were predicted by several factors, including the types of organisms observed and whether the data were submitted from a mobile device. We conclude with implications for technology design, citizen science practices, and research.

Quantitative and Qualitative Trade-Off Analysis of Drowsy Driver Detection Methods: Single Electrode Wearable EEG Device, Multi-Electrode Wearable EEG Device, and Head-Mounted Gyroscope

Drowsy driving impairs motorists’ ability to operate vehicles safely, endangering both the drivers and other people on the road. The purpose of the project is to find the most effective wearable device to detect drowsiness. Existing research has demonstrated several options for drowsiness detection, such as electroencephalogram (EEG) brain wave measurement, eye tracking, head motions, and lane deviations. However, there are no detailed trade-off analyses for the cost, accuracy, detection time, and ergonomics of these methods. We chose to use two different EEG headsets: NeuroSky Mindwave Mobile (single-electrode) and Emotiv EPOC (14- electrode). We also tested a camera and gyroscope-accelerometer device. We can successfully determine drowsiness after five minutes of training using both single and multi-electrode EEGs. Devices were evaluated using the following criteria: time needed to achieve accurate reading, accuracy of prediction, rate of false positives vs. false negatives, and ergonomics and portability. This research will help improve detection devices, and reduce the number of future accidents due to drowsy driving.