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  1. What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets.Gavin McArdle & Rob Kitchin - 2016 - Big Data and Society 3 (1).
    Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...)
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  • Critical data studies: An introduction.Federica Russo & Andrew Iliadis - 2016 - Big Data and Society 3 (2).
    Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...)
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  • Mundane data: The routines, contingencies and accomplishments of digital living.Christine Heyes La Bond, Deborah Lupton, Shanti Sumartojo & Sarah Pink - 2017 - Big Data and Society 4 (1).
    This article develops and mobilises the concept of ‘mundane data’ as an analytical entry point for understanding Big Data. We call for in-depth investigation of the human experiences, routines, improvisations and accomplishments which implicate digital data in the flow of the everyday. We demonstrate the value of this approach through a discussion of our ethnographic research with self-tracking cycling commuters. We argue that such investigations are crucial in informing our understandings of how digital data become meaningful in mundane contexts of (...)
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