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        Data Management for Social Scientists

        From Files to Databases

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        Author(s)
        Weidmann, Nils B.
        Collection
        DFG - German Research Foundation
        Language
        English
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        Abstract
        The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.
        URI
        https://library.oapen.org/handle/20.500.12657/94809
        Keywords
        Data management
        ISBN
        9781108990424
        Publisher
        Cambridge University Press
        Publication date and place
        Cambridge, 2023
        Grantor
        • Deutsche Forschungsgemeinschaft
        Classification
        Data science and analysis: general
        Pages
        242
        Rights
        https://creativecommons.org/licenses/by-nc/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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