Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accessible Data Curation and Analytics for International-Scale Citizen
  Science Datasets   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.

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The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
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The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn