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        Ein regulatives Vertragsrecht für die Datenwirtschaft

        Vertragsrechtliche Optionen zur Vermeidung von Effizienzverlusten bei der (Weiter-)Verwertung maschinengenerierter Daten in der Industrie 4.0

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        Author(s)
        Katharina Wunner
        Collection
        DFG - German Research Foundation
        Language
        German
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        Abstract
        The Faculty of Law and Economics at the University of Bayreuth accepted the present work as a dissertation in March 2022. Case law and literature are up to date as of October 2021. For the printed version, the recently published proposal of the European Commission of the European Commission for a Data Act.
         
        Die Rechts- und Wirtschaftswissenschaftliche Fakultät der Universität Bayreuth nahm die vorliegende Arbeit im März 2022 als Dissertation an. Rechtsprechung und Literatur befinden sich auf dem Stand von Oktober 2021. Für die Druckfassung fand der jüngst veröffentlichte Vorschlag der Europäischen Kommission für einen Data Act Berücksichtigung.
         
        URI
        https://library.oapen.org/handle/20.500.12657/75338
        Keywords
        Regulatory contract law; data economy; machine-generated data; contract law
        DOI
        10.5771/9783748934134
        ISBN
        9783748934134, 9783848788552
        Publisher
        Nomos Verlagsgesellschaft mbH & Co. KG
        Publisher website
        https://www.nomos.de/
        Publication date and place
        2022
        Grantor
        • Deutsche Forschungsgemeinschaft (DFG)
        • Universität Bayreuth
        Series
        Recht und Digitalisierung | Digitization and the Law, 6
        Classification
        Law
        Databases
        Pages
        476
        Rights
        https://creativecommons.org/licenses/by/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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