About: Visualising COVID-19 Research

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Visualising COVID-19 Research Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Visualising COVID-19 Research
Creator	Vidal, Ana Padilla, S Chantler, M Chantler, Mike Gharavi, Azimeh Le Bras, Pierre Le_Bras, { Padilla, Stefano Robb, D Robb, David
source	ArXiv
abstract	The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions' COVID-19 dataset and the Allen Institute for AI's CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.
has issue date	2020-05-13(xsd:dateTime)
has license	arxiv
sha1sum (hex)	161c02e08ae3a4038351582a3ee12e7db6e07d7d
resource representing a document's title	Visualising COVID-19 Research
resource representing a document's body	covid:161c02e08ae3a4038351582a3ee12e7db6e07d7d#body_text
is schema:about of	named entity 'SARS-CoV-2' named entity 'top-down' named entity 'data modelling' named entity 'Many' named entity 'SEARCH' named entity 'paper' named entity 'quick' named entity 'data' named entity 'information mapping' named entity 'global outbreak of SARS' named entity 'medical privacy' named entity 'Germany' named entity 'Europe' named entity 'India' named entity 'word cloud' named entity 'bubble map' named entity 'social distancing' named entity 'pie charts' named entity 'evolution' named entity 'topic model' named entity 'factorial' named entity 'large corpora' named entity 'Gibbs Sampling' named entity 'data modelling' named entity 'Latent Dirichlet Allocation' named entity 'challenging times' named entity 'word cloud' named entity 'social distancing' named entity 'text mining' named entity 'topic modelling' named entity 'SARS-CoV-2' named entity 'Italy' named entity 'drill-down' named entity 'home-schooling' named entity 'public health' named entity 'India' named entity 'cosine distance' named entity 'LDA' named entity '2019-nCoV' named entity 'Contact tracing' named entity 'March 2020' named entity 'evolution' named entity 'topic model' named entity 'COVID-19' named entity 'social distancing' named entity 'COVID-19' named entity 'scientific community' named entity 'COVID-19' named entity 'COVID-19' named entity 'large corpora' named entity 'Mental health' named entity 'Wuhan' named entity 'COVID-19 pandemic' named entity 'Latent Dirichlet Allocation' named entity 'contact tracing' named entity 'trend analysis' named entity 'Pneumonia' named entity 'COVID-19' named entity 'text mining' named entity 'bibliometrics' named entity 'research areas' named entity 'co-occurrence' named entity 'topic modelling' named entity 'betweenness centrality' named entity 'COVID-19' named entity 'topic modelling' named entity 'bibliometrics' named entity 'social distancing' named entity '1, 2' named entity 'COVID-19 outbreak' named entity 'cosine distance' named entity 'COVID-19' named entity 'evolution' named entity 'bar chart' named entity 'treemaps' named entity 'Pneumonia' named entity 'bibliometrics' named entity 'bibliometric analysis'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software