Purpose – The purpose of this paper is to present the organizational and technological processes and strategic choices that led to the successful digitization project of the Albert Einstein Archives. Design/methodology/approach – This is a case study of the major challenges that were associated with the project. These include: the integration of the archives in the academic environment; the management of a project of such magnitude within the university organization and between different stakeholders and the technological aspects of the project and user experience. Findings – A digitization project requires not only the archival staff expertise but also information specialists, IT staff, analysts and usually the digitization staff for processing the archival material. Finding the common language between all the professionals involved as well as building a good strategic plan are the keys to a successful project. Research limitations/implications – The planning and implementation of such a project requires a significant budget, manpower project management, hardware, software and intra- and inter-organizational cooperation and coordination. Originality/value – The phenomenon of digitizing unique and exclusive archival data by universities is becoming an innovative contribution of hidden goods to the public at large. This paper offers strategic insights for the planning of similar digitizing projects, particularly in an academic environment.
Linked Data principles offer significant advantages over current practices when publishing data. Linked Data allows library interoperability by linking to data from other organizations with authoritative data, which enriches library catalog-user search results. This paper describes LODLI, a Linked Open Data Back-End system that we designed and developed to enhance library catalog searches. We integrated our system with the Hebrew University library catalog, HUfind. While our platform can be used as is, it can also be customized by Linked Open Data providers that desire to convert their MARC records into Linked Data information library systems, making their data far more accessible. This research project faced the following challenges: finding the most efficient way to translate binary MARC into MARC records; mapping the MARC records into a variety of information models, such as Dublin Core, FRBR, RDA, OWL and FOAF, while selecting the most appropriate MARC field combinations; and providing links to resources in external datasets using a distance algorithm to identify string similarity. LODLI is a generic system to which additional ontologies can easily be added. We have demonstrated the system with two types of clients: FRBR visualization client and VIAF-extension client.
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