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2020 LD4 Conference on Linked Data in Libraries has ended
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Back To Schedule
Thursday, July 23 • 10:00am - 11:30am
Discovery of multilingual resources • Linked open data for disaster response • Faculty curation and linked open data

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Session recording

Will Linked Data Enhance the Discovery of Multilingual Resources? Charlene Chou and Alexandra Provo (New York University)
Slides
Multilingual resources have been challenging for bibliographic description and discovery systems for years because of complex issues concerning data models and interface functions in various systems and platforms. For example, the IFLA Library Reference Model indicates that "in the implicit conceptualization of librarians, all translations are viewed as expressions of the same work; rights societies have a very different concept of "work", and regard each translation as a distinct "work"." For countries with multiple official languages, many publications are multilingual and distinctions between the original language and translations could be confusing for users. When the resources are written in both Roman and non-Roman scripts, bibliographic descriptions could get more intricate due to the display of native scripts, transliteration and translations. This presentation will explore and examine whether current metadata models, linked data editors as well as discovery systems would enhance the discovery of multilingual resources. Various use cases will be used to describe the challenges and opportunities for improvements. For instance, what are the issues of describing these resources in the Library of Congress BIBFRAME Editor and Sinopia Editor currently? Shall we adjust our approaches to accommodate these specific demands for better discovery? In addition, some use cases will be used to illustrate complex issues of digital collections at New York University, such as Arabic Collections Online (ACO) and the Hemispheric Institute Digital Video Library (HIDVL). In the case of ACO, the multilingual metadata needs of the digital library website are sometimes at odds with rules governing MARC-based cataloging practices. This case hints at future challenges we may face as we move legacy metadata to linked data environments, imposing new models and expectations on our metadata. As a tri-lingual project, HIDVL presents related opportunities to think through how we might improve storage of multilingual metadata. We will conclude with ideas for further research as well as recommendations and suggestions using the models of Wikipedia/Wikidata for multiple language interfaces.

GaNCH: Using Linked Open Data for Georgia's Natural, Cultural, and Historic Organizations' Disaster Response, Cliff Landis and Allyson F. Smith (Atlanta University Center Robert W. Woodruff Library)
Slides
Through a one-year LYRASIS Catalyst Grant, the Atlanta University Center Robert W. Woodruff Library is creating a publicly editable directory of Georgia's Natural, Cultural and Historical Organizations (NCHs), allowing for quick retrieval of coordinate location and contact information for disaster response. Directory information is being compiled, updated, and uploaded to Wikidata, the linked open data database from the Wikimedia Foundation. Directory information will then be delivered via a website, allowing federal, state, and regional emergency responders to quickly search for NCHs in disaster areas. Partnerships with state professional NCH organizations ensure that the compiled datasets will be sustained over time. Session attendees will learn how the directory was created, and how to replicate the project to preserve their own region's rich natural, cultural, and historical resources using free online tools.

Faculty Curation and Linked Open Data: BigDIVA.org, Laura Mandell (Texas A&M University Advanced Research Consortium)
Slides
The intended audience for this session are librarians and digital humanists interested in visualization tools that are being developed for Linked Open Data, especially those that enable serendipity in research and discovery of open-access materials via searches that integrate proprietary resources as well. The Advanced Research Consortium (ARC) is the overarching organization for NINES, 18thConnect, Medieval Electronic Scholarly Alliance (Mesa), ModNets, and three forthcoming data aggregators: the American Antiquarian Society's Networked Early American Resources, Disability Studies, and Asian American Literature. Each aggregator and scholarly community provides peer review for digital projects and approves ingesting other open-access materials provided by libraries and museums. Once accepted by the scholarly community -- nines.org, 18thConnect.org, mesa-medieval.org, modnets.org -- RDF metadata for each project, vendor, and institution is ingested into the ARC database, along with proprietary materials essential to researchers in those fields from JSTOR, ProjectMuse, Gale, and ProQuest, among others. The complete ARC catalog is visible through BigDIVA.org, the Big Data Infrastructure Visualization Application. ARC has partnered on a grant received from the Canadian Foundation for Innovation to transform the current ARC metadata (XML-RDF) into Linked Open Data and to transform Big Diva into an LOD visualization tool. We would like to present and receive feedback about our metadata, search capacities, the BigDIVA tool and our ideas for transforming BigDIVA into an LOD Viewer.

Session Facilitators
avatar for Jason Kovari

Jason Kovari

Director, Cataloging and Metadata Services, Cornell University

Presenters
avatar for Alexandra Provo

Alexandra Provo

New York University
avatar for Cliff Landis

Cliff Landis

Digital Initiatives Librarian, Atlanta University Center Robert W. Woodruff Library
Cliff Landis is Digital Initiatives Librarian at the Atlanta University Center Robert W. Woodruff Library. His research interests include linked open data, archival technologies, digitization, metadata, and the coevolution of humanity and technology.
avatar for Laura Mandell

Laura Mandell

Professor, Director, Texas A&M University


Thursday July 23, 2020 10:00am - 11:30am PDT