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        Towards a Data-driven Military

        A Multidisciplinary Perspective

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        Contributor(s)
        Pijpers, Peter (editor)
        Voskuijl, Mark (editor)
        Beeres, Robert (editor)
        Language
        English
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        Abstract
        Towards a Data-Driven Military: A Multidisciplinary Perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Part one, ‘Military Systems and Technology’, approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. Part two, ‘War Studies’, takes a multidisciplinary approach to the evolution of warfare, while the third part, ‘Military Management Studies’, takes a holistic organizational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book.
        URI
        https://library.oapen.org/handle/20.500.12657/108621
        Keywords
        Military intelligence, Defence Strategy, Artificial Intelligence, Big Data
        ISBN
        9789400604537, 9789400604537, 9789087284084
        Publisher
        Leiden University Press
        Publisher website
        https://www.lup.nl/
        Publication date and place
        Leiden, 2023
        Imprint
        Leiden University Press
        Series
        LUP NL ARMS,
        Classification
        Theory of warfare and military science
        Military and defence strategy
        Artificial intelligence
        Pages
        358
        Rights
        https://creativecommons.org/licenses/by-nc-nd/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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