iSamples (Internet of Samples): Efficient
Geo-related records 3D Visualization for large
datasets and the UI design
Gan, Quan; Vieglais, Dave; Cui, Hong; Mandel, Danny; Ramdeen, Sarah; Song, Sarah Hyunju; Richard, Stephen M.; Deck, John; Kansa, Eric C.; Kansa, Sarah Whitcher; Kunze, John; Lehnert, Kerstin; Meyer, Christopher; Snyder, Rebecca; Walls, Ramona L.
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https://hdl.handle.net/2142/117371
Description
Title
iSamples (Internet of Samples): Efficient
Geo-related records 3D Visualization for large
datasets and the UI design
Author(s)
Gan, Quan
Vieglais, Dave
Cui, Hong
Mandel, Danny
Ramdeen, Sarah
Song, Sarah Hyunju
Richard, Stephen M.
Deck, John
Kansa, Eric C.
Kansa, Sarah Whitcher
Kunze, John
Lehnert, Kerstin
Meyer, Christopher
Snyder, Rebecca
Walls, Ramona L.
Issue Date
2023-03-13
Keyword(s)
iSamples
3D Visualization and Large Geospatial Datasets
Material Samples
Abstract
The Internet of Samples (iSamples) project, funded by the U.S. National Science Foundation, is a multi-disciplinary and multi-institutional project to design, develop, and promote service infrastruc-
ture that will provide services for globally unique, consistent, and conve-
nient identification of material samples; metadata about them; and linking
them to other samples, derived data, and research results published in
the literature. One of the iSamples infrastructures is a web application
(iSamples UI) to support search and discovery functionalities of large material
sample repositories. The web application employed CesiumJS and
custom features for efficient 3D visualization and information discovery in
geo-related datasets and is the first platform to employ a 3D visual-
ization earth map in the material sample area. The user interface has
received overwhelmingly positive feedback on its performance and user
experience. In this poster, we shared some of the tips for implementing
this high-performing application.
Publisher
iSchools
Series/Report Name or Number
iConference 2023 Proceedings
Type of Resource
Other
text
Language
eng
Handle URL
https://hdl.handle.net/2142/117371
Copyright and License Information
Copyright 2023 is held by Quan Gan, Dave Vieglais, Hong Cui, Danny Mandel, Sarah Ramdeen, Sarah Hyunju Song, Stephen M. Richard, John Deck, Eric C. Kansa, Sarah Whitcher Kansa, John Kunze, Kerstin Lehnert, Christopher Meyer, Rebecca Snyder, and Ramona L. Walls. Copyright permissions, when appropriate, must be obtained directly from the author.
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