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Dataset . 2022
Data sources: Datacite; Sygma
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DataverseNO
Dataset . 2022
Data sources: B2FIND
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Dataset . 2022
License: CC 0
Data sources: DataverseNO
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A Dataset Documenting Representations of Machine Vision Technologies in Artworks, Games and Narratives

Authors: Rettberg, Jill Walker; Kronman, Linda; Solberg, Ragnhild; Gunderson, Marianne; Bjørklund, Stein Magne; Stokkedal, Linn Heidi; de Seta, Gabriele; +2 Authors

A Dataset Documenting Representations of Machine Vision Technologies in Artworks, Games and Narratives

Abstract

This dataset captures cultural attitudes towards machine vision technologies as they are expressed in art, games and narratives. The dataset includes records of 500 creative works (including 77 digital games, 191 digital artworks and 236 movies, novels and other narratives) that use or represent machine vision technologies like facial recognition, deepfakes, and augmented reality. The dataset is divided into three main tables, relating to the works, to specific situations in each work involving machine vision technologies, and to the characters that interact with the technologies. Data about each work includes title, author, year and country of publication; types of machine vision technologies featured; topics the work addresses, and sentiments associated with that machine vision usage in the work. In the various works we identified 884 specific situations where machine vision is central. The dataset includes detailed data about each of these situations that describes the actions of human and non-human agents, including machine vision technologies. The dataset is the product of a digital humanities project and can be also viewed as a database at http://machine-vision.no. Data was collected by a team of topic experts who followed an analytical model developed to explore relationships between humans and technologies, inspired by posthumanist and feminist new materialist theories. The project team identified relevant works by searching databases, visiting exhibitions and conferences, reading scholarship, and consulting other experts. The inclusion criteria were creative works( art, games, narratives (movies, novels, etc)) where one of the following machine vision technologies was used in or represented by the work: 3D scans, AI, Augmented reality, Biometrics, Body scans, Camera, Cameraphone, Deepfake, Drones, Emotion recognition, Facial recognition, Filtering, Holograms, Image generation, Interactive panoramas Machine learning, MicroscopeOrTelescope Motion tracking, Non-Visible Spectrum Object recognition, Ocular implant, Satellite images, Surveillance cameras, UGV, Virtual reality, and Webcams. The dataset as well as the more detailed database can be viewed, searched, extracted, or otherwise used or reused and is considered particularly useful for humanities and social science scholars interested in the relationship between technology and culture, and by designers, artists, and scientists developing machine vision technologies. Drupal, 9 R, 4.1.3 RStudio, 2022.02.0 The dataset includes data describing 77 games, 192 artworks and 237 narratives (in total 500 Creative Works) where machine vision technologies play an important role. This includes Creative Works produced between 1891 and 2021, but with a heavy emphasis on recent works: 80% of the Works are from 2011-2021, and just over half from 2016-2021. The Creative Works are from 59 different countries, with 78,6% from North America and Europe, and 21,4% from other parts of the world.

Related Organizations
Keywords

electronic literature, digital art (visual works), new media art, motion pictures visual works, television series, digital humanities discipline, fiction, machine vision, film, computer vision, video game, Humanities, science fiction, digital humanities (discipline), digital art, Arts and Humanities, digital art visual works, digital humanities, motion pictures (visual works), algorithmic bias, digital culture, game studies, art, novels

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Funded by
EC| Machine Vision
Project
Machine Vision
Machine Vision in Everyday Life: Playful Interactions with Visual Technologies in Digital Art, Games, Narratives and Social Media
  • Funder: European Commission (EC)
  • Project Code: 771800
  • Funding stream: H2020 | ERC | ERC-COG
Validated by funder
Related to Research communities
Digital Humanities and Cultural Heritage
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