Accessible data curation and analytics for international-scale citizen science datasets

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation.

The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers.

We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipline that enables reproducible research across an international research group for the Covid Symptom Study.
Original languageEnglish
Article number297
Pages (from-to)297
JournalScientific Data
Volume8
Issue number1
Early online date22 Nov 2021
DOIs
Publication statusPublished - 22 Nov 2021

Keywords

  • Big Data
  • COVID-19/epidemiology
  • Citizen Science
  • Data Curation
  • Data Science
  • Datasets as Topic
  • Epidemiological Monitoring
  • Humans
  • Mobile Applications
  • Smartphone
  • Software

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