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Metadata in Long-Term Digital Preservation

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Digital Preservation: Putting It to Work

Part of the book series: Studies in Computational Intelligence ((SCI,volume 700))

Abstract

Metadata has been used to describe digital data for several years. First metadata standards were created in the nineties. Particularly important proved to be Dublin Core designed for the description of web-based resources (1995). At the same time Exif standard was created, determining mainly technical metadata used to describe parameters of digital images and information on equipment used. Since that time various other types of metadata have been created, e.g. rights metadata, provenance, structural, and many domain-oriented standards. Long-term digital archives usually preserve all metadata transferred to them with corresponding digital objects and often use a subset of obtained metadata to manage archive assets. They also create preservation metadata, which describe processes and preservation actions applied to digital objects in the archives. In this chapter basic information about metadata in long-term digital archives is given.

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland

    Grzegorz Płoszajski

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  1. Grzegorz Płoszajski
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Correspondence to Grzegorz Płoszajski .

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Editors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland

    Tomasz Traczyk

  2. Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland

    Włodzimierz Ogryczak

  3. Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland

    Piotr Pałka

  4. Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland

    Tomasz Śliwiński

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Płoszajski, G. (2017). Metadata in Long-Term Digital Preservation. In: Traczyk, T., Ogryczak, W., Pałka, P., Śliwiński, T. (eds) Digital Preservation: Putting It to Work. Studies in Computational Intelligence, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-51801-5_2

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