Results 241 to 250 of about 5,432,324 (400)
Searching for a research agenda for the Library and Information Science community [PDF]
Anne Goulding
openalex +1 more source
This article introduces the Dataspace Management System (DSMS), a methodological framework realized in software, designed as a technology stack to power dataspaces with a focus on advanced knowledge management in materials science and manufacturing. DSMS leverages heterogeneous data through semantic integration, linkage, and visualization, aligned with
Yoav Nahshon+7 more
wiley +1 more source
A Study on Intellectual Structure of Library and Information Science in Korea
Yeong-Jun Yoo
openalex +2 more sources
In pursuit of modern data management techniques, this study presents an in‐lab pipeline combining electronic laboratory notebooks (eLabFTW) and Python scripts for creating semantically enriched, interoperable, machine‐actionable data. Automating data mapping enhances usability, collaboration, and unified knowledge representation.
Markus Schilling+7 more
wiley +1 more source
Library and Information Science Professionals as Community Action Researchers in an Academic Setting: Top Ten Directions to Further Institutional Change for People of Diverse Sexual Orientations and Gender Identities [PDF]
Bharat Mehra, Donna Braquet
openalex +1 more source
Ontologies for FAIR Data in Additive Manufacturing: A Use Case‐Based Evaluation
An ontology‐based approach for generating findable, accessible, interoperable, reusable data in additive manufacturing is explored, focusing on powder bed fusion. The article highlights the benefits of enhanced data findability and digital twin enablement, while addressing challenges like data integration complexity and the need for specialized ...
Thomas Bjarsch+2 more
wiley +1 more source
The Realities of Relevance: A Survey of Librarians' Use of Library and Information Science Research
Christine Brown, Brett Spencer
openalex +2 more sources
This article provides examples of ontology development in the materials science domain (use‐case of Brinell hardness testing) and gives ontology developers an overview for selecting their desired top‐level ontologies (e.g., BFO, EMMO, PROVO) by considering different evaluation parameters like semantic richness, domain coverage, extensibility ...
Hossein Beygi Nasrabadi+3 more
wiley +1 more source