Block Depth

Block Depth is a hierarchical structural metric in academic discourse that denotes the level of nesting of a paper-level contribution—such as a method, model, framework, theory, construct, dataset, task, or metric—within the overarching topic hierarchy, distinguishing it by its position relative to the root topic.

11

Publications

391

Citations

39

Authors

23

Institutions

Publications per year

2017–2026

4

Authors

39

  1. JB
    Jeremy B. Ford

    Vanderbilt University, United States

    2 publications · 31 citations

    2

    h-index

  2. MW
    Michael W. Jenkins

    Case Western Reserve University, United States

    2 publications · 31 citations

    2

    h-index

  3. EJ
    E. Jansen

    Brookhaven National Laboratory, United States

    2 publications · 31 citations

    2

    h-index

  4. MG
    Mohit Ganguly

    Vanderbilt University, United States

    2 publications · 31 citations

    2

    h-index

  5. HJ
    Hillel J. Chiel

    Case Western Reserve University, United States

    2 publications · 31 citations

    2

    h-index

1–5 of 39

Venues

  1. 2

    h-index

  2. 1

    h-index

  3. 1

    h-index

  4. Journal of Neural Engineering

    1 publications · 13 citations

    1

    h-index

  5. Computers & Operations Research

    1 publications · 154 citations

    1

    h-index

1–5