Double Data Extraction
Double Data Extraction is a methodological procedure in data curation and systematic review processes wherein two independent researchers separately extract data from primary studies to ensure accuracy, reliability, and reduction of bias in the synthesized findings.
15
Publications
1.1K
Citations
61
Authors
22
Institutions
Publications per year
2017–2026
7
Variants
Double Data Abstraction
Parents
15
A Comparison of Meta-analytic Results Using Literature vs Individual Patient Data
1–5 of 15
61
Oklahoma State University Center for Health Sciences, United States
2 publications · 135 citations
2
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1
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1
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1
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1
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1–5 of 61
22
Oklahoma State University Center for Health Sciences
Tulsa, United States
2 h-index
8 publications
505 citations
Jiangnan University
Wuxi, China
1 h-index
5 publications
60 citations
University of Lisbon
Lisbon, Portugal
1 h-index
2 publications
22 citations
Capital District Health Authority
Halifax, Canada
1 h-index
5 publications
2.3K citations
Chemnitz University of Technology
Chemnitz, Germany
1 h-index
2 publications
22 citations
De Hoogstraat Revalidatie
Utrecht, The Netherlands
1 h-index
2 publications
40 citations
Heliomare
Wijk aan Zee, The Netherlands
1 h-index
2 publications
40 citations
China Medical University
Shenyang, China
1 h-index
1 publications
13 citations
1–8 of 22
2 publications · 164 citations
2
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Repository · 2 publications · 69 citations
2
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1 publications · 42 citations
1
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1 publications · 70 citations
1
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1 publications · 463 citations
1
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1–5