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

Authors

61

  1. MV
    Matt Vassar

    Oklahoma State University Center for Health Sciences, United States

    2 publications · 135 citations

    2

    h-index

  2. UG
    Uwe Götze

    Chemnitz University of Technology, Germany

    1 publications · 11 citations

    1

    h-index

  3. EK
    Eva Kaltenthaler

    University of Sheffield, United Kingdom

    1 publications · 10 citations

    1

    h-index

  4. NB
    Nina Buscemi

    University of Alberta, Canada

    1 publications · 463 citations

    1

    h-index

  5. YY
    Yingmin Yi

    Xi'an University of Technology, China

    1 publications · 70 citations

    1

    h-index

1–5 of 61

Venues

  1. JAMA

    2 publications · 164 citations

    2

    h-index

  2. PubMed

    Repository · 2 publications · 69 citations

    2

    h-index

  3. BMC Medical Informatics and Decision Making

    1 publications · 42 citations

    1

    h-index

  4. Entropy

    1 publications · 70 citations

    1

    h-index

  5. Journal of Clinical Epidemiology

    1 publications · 463 citations

    1

    h-index

1–5