Data Quality Cleaning
Data Quality Cleaning is a methodological process within data management that systematically identifies, corrects, and eliminates inaccuracies, inconsistencies, and redundancies in datasets to ensure compliance with predefined quality standards.
269
Publications
13.6K
Citations
1.3K
Authors
530
Institutions
Publications per year
2017–2026
143
Parents
269
An Integrated Pipeline for de Novo Assembly of Microbial Genomes
Andrew Tritt, Jonathan A. Eisen, Marc T. Facciotti et al. · PLoS ONE · 2012 · 446 citations · Full text
1–5 of 269
1.3K
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5
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4
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530
Tsinghua University
Beijing, China
6 h-index
16 publications
852 citations

University of Waterloo
Waterloo, Canada
5 h-index
10 publications
1.4K citations
University College London
London, United Kingdom
4 h-index
6 publications
1.8K citations
Harvard University
Cambridge, United States
4 h-index
6 publications
111 citations
Columbia University
New York, United States
4 h-index
17 publications
1.3K citations
Stanford University
Stanford, United States
4 h-index
7 publications
1.1K citations

Linköping University
Linköping, Sweden
3 h-index
4 publications
219 citations
University of Bari Aldo Moro
Bari, Italy
3 h-index
4 publications
212 citations
1–8 of 530
6 publications · 648 citations
7
h-index
PVLDB · 6 publications · 512 citations
6
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Repository · 5 publications · 149 citations
5
h-index
3 publications · 152 citations
3
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TKDE · 3 publications · 113 citations
3
h-index
1–5