Wiz: A Web-Based Tool for Interactive Visualization of Big Data.

big data data visualisation for Industry 4.0 data visualization digitalization interactive plots multi-dimensional data analytics principle component analysis web app

Journal

Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765

Informations de publication

Date de publication:
13 Nov 2020
Historique:
received: 16 05 2020
revised: 14 07 2020
accepted: 25 08 2020
entrez: 9 12 2020
pubmed: 10 12 2020
medline: 10 12 2020
Statut: epublish

Résumé

In an age of information, visualizing and discerning meaning from data is as important as its collection. Interactive data visualization addresses both fronts by allowing researchers to explore data beyond what static images can offer. Here, we present Wiz, a web-based application for handling and visualizing large amounts of data. Wiz does not require programming or downloadable software for its use and allows scientists and non-scientists to unravel the complexity of data by splitting their relationships through 5D visual analytics, performing multivariate data analysis, such as principal component and linear discriminant analyses, all in vivid, publication-ready figures. With the explosion of high-throughput practices for materials discovery, information streaming capabilities, and the emphasis on industrial digitalization and artificial intelligence, we expect Wiz to serve as an invaluable tool to have a broad impact in our world of big data.

Identifiants

pubmed: 33294864
doi: 10.1016/j.patter.2020.100107
pii: S2666-3899(20)30141-0
pmc: PMC7691393
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100107

Informations de copyright

© 2020 The Authors.

Déclaration de conflit d'intérêts

P.Z.M. has financial interest through Monoclinic Ltd. D.F.-J. has financial interest through Immaterial Ltd. The other authors declare no competing interests.

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Auteurs

Christopher Balzer (C)

Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK.
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

Rama Oktavian (R)

Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK.

Mohammad Zandi (M)

Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK.

David Fairen-Jimenez (D)

Adsorption & Advanced Materials Laboratory (AML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK.

Peyman Z Moghadam (PZ)

Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK.

Classifications MeSH