Facets (new session)
Description
Metadata
Settings
owl:sameAs
Inference Rule:
b3s
b3sifp
dbprdf-label
facets
http://dbpedia.org/resource/inference/rules/dbpedia#
http://dbpedia.org/resource/inference/rules/opencyc#
http://dbpedia.org/resource/inference/rules/umbel#
http://dbpedia.org/resource/inference/rules/yago#
http://dbpedia.org/schema/property_rules#
http://www.ontologyportal.org/inference/rules/SUMO#
http://www.ontologyportal.org/inference/rules/WordNet#
http://www.w3.org/2002/07/owl#
ldp
oplweb
skos-trans
virtrdf-label
None
About:
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
Goto
Sponge
NotDistinct
Permalink
An Entity of Type :
schema:ScholarlyArticle
, within Data Space :
wasabi.inria.fr
associated with source
document(s)
Type:
Academic Article
research paper
schema:ScholarlyArticle
New Facet based on Instances of this Class
Attributes
Values
type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
Creator
Thelwall, Mike
source
ArXiv
abstract
The COVID-19 pandemic requires a fast response from researchers to help address biological, medical and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals and the public may need to quickly identify important new studies. In response, this paper assesses the coverage of scholarly databases and impact indicators during 21 March to 18 April 2020. The results confirm a rapid increase in the volume of research, which particularly accessible through Google Scholar and Dimensions, and less through Scopus, the Web of Science, PubMed. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited three weeks later. Researchers needing wide scope literature searches (rather than health focused PubMed or medRxiv searches) should start with Google Scholar or Dimensions and can use tweet and Mendeley reader counts as indicators of likely importance.
has issue date
2020-04-22
(
xsd:dateTime
)
has license
arxiv
sha1sum (hex)
3eeda31b131ae2c9586195b5ee7f66e6bf4a1cfc
resource representing a document's title
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
resource representing a document's body
covid:3eeda31b131ae2c9586195b5ee7f66e6bf4a1cfc#body_text
is
schema:about
of
named entity 'studies'
named entity 'high'
named entity 'citation counts'
named entity 'articles'
named entity 'social media'
»more»
◂◂ First
◂ Prev
Next ▸
Last ▸▸
Page 1 of 6
Go
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