Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
This work solves data management problems for researchers handling massive citizen science datasets, though it is incremental as it builds on existing data curation concepts.
The paper tackles the challenge of curating and analyzing large-scale citizen science datasets, such as the Covid Symptom Study with 4.7 million participants and 189 million assessments, by developing ExeTera, an open-source software that addresses scalability and reproducibility issues.
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.