Data-driven Art

Data-Driven Art is a form of computational art in which the generative process is fundamentally informed and structured by empirical or real-world data sets, distinguishing it from other generative approaches through its reliance on data as both aesthetic and procedural foundation.

69

Publications

2.7K

Citations

254

Authors

112

Institutions

Publications per year

2017–2026

54

Authors

254

  1. CM
    Chongyang Ma

    Kuaishou (China)

    3 publications · 178 citations

    3

    h-index

  2. AD
    Alex Dragulescu

    Dana-Farber/Harvard Cancer Center, United States

    2 publications · 40 citations

    2

    h-index

  3. RJ
    Rachel Jacobs

    University of Nottingham, United Kingdom

    2 publications · 66 citations

    2

    h-index

  4. CL
    Chunping Liu

    Soochow University, China

    2 publications · 84 citations

    2

    h-index

  5. JH
    Jing Huo

    Nanjing University, China

    2 publications · 51 citations

    2

    h-index

1–5 of 254

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 287 citations

    9

    h-index

  2. ACM Transactions on Graphics

    TOG · 5 publications · 317 citations

    5

    h-index

  3. Applied Sciences

    2 publications · 66 citations

    2

    h-index

  4. IEEE Transactions on Image Processing

    2 publications · 170 citations

    2

    h-index

  5. Leonardo

    2 publications · 44 citations

    2

    h-index

1–5