The following article was published earlier today by the International Journal of Digital Curation (IJDC).
Title
Challenges and Directions in 3D and VR Data Curation: Findings from a Nominal Group Study
Authors
Nathan Frank Hall
Virginia Tech
Juliet Hardesty
Indiana University
Zack Lischer-Katz
University of Oklahoma
Jennifer Johnson
Indiana University – Purdue University Indianapolis
Matt Cook
University of Oklahoma
Julie Griffin
Virginia Tech
Andrea Ogier
Virginia Tech
Tara Carlisle
University of Oklahoma
Zhiwu Xie
Virginia Tech
Robert McDonald
University of Colorado
Jamie Wittenberg
Indiana University
Source
International Journal of Digital Curation (IJDC).
Vol 14 No 1 (2019)
DOI: 10.2218/ijdc.v14i1.588
Abstract
This study identifies challenges and promising directions in the curation of 3D data. 3D visualization shows great promise for a range of scholarly fields through interactive engagement with and analysis of spatially complex artifacts, spaces, and data. While the new affordability of emerging 3D capture technologies presents greater academic possibilities, academic libraries need more effective workflows, policies, standards, and practices to ensure that they can support the creation, discovery, access, preservation, and reproducibility of 3D data sets. This study uses nominal group technique with invited experts across several disciplines and sectors to identify common challenges in the creation and re-use of 3D data for the purpose of developing library strategy for supporting curation of 3D data. This article identifies staffing needs for 3D imaging; alignment with IT resources; the roll of archivists in addressing unique challenges posed by these datasets; the importance of data annotation, metadata, and transparency for research integrity and reproducibility; and features for storage, access, and management to facilitate re-use by researchers and educators. Participants identified three main challenges for supporting 3D data that align with the strengths of libraries: 1) development of crosswalks and aggregation tools for discipline-specific metadata models, data dictionaries for 3D research, and aggregation tools for expanding discovery; 2) development of an open source viewer that supports streaming and annotation on archival formats of 3D models and makes archival master files accessible, while also serving derivative files based on user requirements; and 3) widespread of adoption of better documentation and technical metadata for image capture and modeling processes in order to support replicability of research, reproducibility of models, and transparency of scientific process.
Direct to Full Text Article
18 pages; PDF.
Supplemental Files
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