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

Murray, Benjamin ; Kerfoot, Eric ; Chen, Liyuan ; Deng, Jie ; Graham, Mark S. ; Sudre, Carole H. ; Molteni, Erika ; Canas, Liane S. ; Antonelli, Michela and Klaser, Kerstin , et al. (2021) In Scientific Data 8(1).
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... (More)

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 pipeline that enables reproducible research across an international research group for the Covid Symptom Study.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Data
volume
8
issue
1
article number
297
publisher
Nature Publishing Group
external identifiers
  • scopus:85119660232
  • pmid:34811392
ISSN
2052-4463
DOI
10.1038/s41597-021-01071-x
language
English
LU publication?
yes
id
d652a4fe-5544-4171-b8ca-1ac8db990ece
date added to LUP
2021-12-08 13:58:18
date last changed
2024-12-29 18:39:11
@article{d652a4fe-5544-4171-b8ca-1ac8db990ece,
  abstract     = {{<p>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 pipeline that enables reproducible research across an international research group for the Covid Symptom Study.</p>}},
  author       = {{Murray, Benjamin and Kerfoot, Eric and Chen, Liyuan and Deng, Jie and Graham, Mark S. and Sudre, Carole H. and Molteni, Erika and Canas, Liane S. and Antonelli, Michela and Klaser, Kerstin and Visconti, Alessia and Hammers, Alexander and Chan, Andrew T. and Franks, Paul W. and Davies, Richard and Wolf, Jonathan and Spector, Tim D. and Steves, Claire J. and Modat, Marc and Ourselin, Sebastien}},
  issn         = {{2052-4463}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Data}},
  title        = {{Accessible data curation and analytics for international-scale citizen science datasets}},
  url          = {{http://dx.doi.org/10.1038/s41597-021-01071-x}},
  doi          = {{10.1038/s41597-021-01071-x}},
  volume       = {{8}},
  year         = {{2021}},
}