Unifying Fragmented Catalogues: A Prototype Framework for Library Discovery Using SLiMS, VuFind, and Python via OAI-PMH

The paper describes a model designed to overcome the fragmentation of bibliographic metadata among the different information systems kept by libraries. The bibliographic records were first obtained from the open-source integrated library system SLIMS (Senayan Library Management System) via the Z39.50 and Search/Retrieve via URL (SRU) interfaces. SLIMS was registered as a data provider with the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), and its metadata was transferred to VuFind in order to create a single discovery interface consisting of 18,395 records. The collection was also expanded by including 38,113 bibliographic records in MARC 21 (Machine-Readable Cataloging) format that were obtained from a number of web-based repositories, thus increasing the amount of material available for discovery of the collection. The metadata was harvested from the OAI-PMH server using the Sickle OAI-PMH client within the Python environment of the Jupyter Notebook. The model was built using only open source components on a Linux platform. The findings indicate that it is possible to develop a single search interface based on a traditional library catalog which will allow for federated search and faceted browsing. The innovation of the system lies in the combination of protocol-driven harvesting and programmatic metadata processing, this enabling bibliographic records and item-level metadata to be stored in a single, structured, machine-readable format. Therefore, the model supports the objectives of open catalogs and open science, while also providing a metadata foundation for possible intelligent indexing and retrieval.

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Publication Details

Journal
Journal of Library Metadata
Published
2026-08-27
DOI
https://doi.org/10.1080/19386389.2026.2724693
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
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article

Unifying Fragmented Catalogues: A Prototype Framework for Library Discovery Using SLiMS, VuFind, and Python via OAI-PMH

Sukumar Mandal
Journal of Library Metadata
Research Data Management Practices
article

Unifying Fragmented Catalogues: A Prototype Framework for Library Discovery Using SLiMS, VuFind, and Python via OAI-PMH

Sukumar Mandal
article en

Abstract

The paper describes a model designed to overcome the fragmentation of bibliographic metadata among the different information systems kept by libraries. The bibliographic records were first obtained from the open-source integrated library system SLIMS (Senayan Library Management System) via the Z39.50 and Search/Retrieve via URL (SRU) interfaces. SLIMS was registered as a data provider with the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), and its metadata was transferred to VuFind in order to create a single discovery interface consisting of 18,395 records. The collection was also expanded by including 38,113 bibliographic records in MARC 21 (Machine-Readable Cataloging) format that were obtained from a number of web-based repositories, thus increasing the amount of material available for discovery of the collection. The metadata was harvested from the OAI-PMH server using the Sickle OAI-PMH client within the Python environment of the Jupyter Notebook. The model was built using only open source components on a Linux platform. The findings indicate that it is possible to develop a single search interface based on a traditional library catalog which will allow for federated search and faceted browsing. The innovation of the system lies in the combination of protocol-driven harvesting and programmatic metadata processing, this enabling bibliographic records and item-level metadata to be stored in a single, structured, machine-readable format. Therefore, the model supports the objectives of open catalogs and open science, while also providing a metadata foundation for possible intelligent indexing and retrieval.

Journal of Library Metadata
University of Burdwan (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Research Data Management Practices
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