Data Annotation

Data Annotation is a foundational data curation process within the data preparation pipeline that involves assigning descriptive, informative, or semantic labels to raw data instances to render them interpretable and usable for supervised machine learning models.

736

Publications

65.3K

Citations

6K

Authors

1.1K

Institutions

Publications per year

2017–2026

445

Authors

6K

  1. AS
    Angie S. Hinrichs

    University of California, Santa Cruz, United States

    9 publications · 6.7K citations

    9

    h-index

  2. HC
    Hiram Clawson

    University of California, Santa Cruz, United States

    8 publications · 4.9K citations

    8

    h-index

  3. BJ
    Brian J. Raney

    University of California, Santa Cruz, United States

    8 publications · 4.9K citations

    8

    h-index

  4. AS
    Ann S. Zweig

    University of California, Santa Cruz, United States

    7 publications · 4.4K citations

    7

    h-index

  5. MD
    Mark Diekhans

    University of California, Santa Cruz, United States

    7 publications · 4.9K citations

    7

    h-index

1–5 of 6K

Venues

  1. Nucleic Acids Research

    51 publications · 21.7K citations

    39

    h-index

  2. arXiv (Cornell University)

    Repository · 37 publications · 1.3K citations

    21

    h-index

  3. BMC Bioinformatics

    30 publications · 3.4K citations

    20

    h-index

  4. Bioinformatics

    27 publications · 3.5K citations

    19

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 518 citations

    10

    h-index

1–5