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Koster, L.: Persistent identifiers for heritage objects (2020) 0.01
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Abstract

Persistent identifiers (PID's) are essential for getting access and referring to library, archive and museum (LAM) collection objects in a sustainable and unambiguous way, both internally and externally. Heritage institutions need a universal policy for the use of PID's in order to have an efficient digital infrastructure at their disposal and to achieve optimal interoperability, leading to open data, open collections and efficient resource management. Here the discussion is limited to PID's that institutions can assign to objects they own or administer themselves. PID's for people, subjects etc. can be used by heritage institutions, but are generally managed by other parties. The first part of this article consists of a general theoretical description of persistent identifiers. First of all, I discuss the questions of what persistent identifiers are and what they are not, and what is needed to administer and use them. The most commonly used existing PID systems are briefly characterized. Then I discuss the types of objects PID's can be assigned to. This section concludes with an overview of the requirements that apply if PIDs should also be used for linked data. The second part examines current infrastructural practices, and existing PID systems and their advantages and shortcomings. Based on these practical issues and the pros and cons of existing PID systems a list of requirements for PID systems is presented which is used to address a number of practical considerations. This section concludes with a number of recommendations.

Preminger, M.; Rype, I.; Ådland, M.K.; Massey, D.; Tallerås, K.: ¬The public library metadata landscape : the case of Norway 2017-2018 (2020) 0.01

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Samples, J.; Bigelow, I.: MARC to BIBFRAME : converting the PCC to Linked Data (2020) 0.01

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Dagher, I.; Soufi, D.: Authority control of Arabic psonal names : RDA and beyond (2021) 0.01

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Morris, V.: Automated language identification of bibliographic resources (2020) 0.01

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Date: 2. 3.2020 19:04:22

Das, S.; Paik, J.H.: Gender tagging of named entities using retrieval-assisted multi-context aggregation : an unsupervised approach (2023) 0.01

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Date: 22. 3.2023 12:00:14

Kim, J.(im); Kim, J.(enna): Effect of forename string on author name disambiguation (2020) 0.01

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Date: 11. 7.2020 13:22:58

Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023) 0.01

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Date: 22. 1.2023 18:40:36

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