data science altmetrics
Clicks: 318
ID: 138022
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Star Article
65.0
/100
318 views
218 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
StarRanked #5 of 6 articles by views in journal of pharmaceutical policy and practice
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Altmetrics are indicators about the impacts of scientific research that are calculated using data extracted from the social Web. For example, counting the number of times that an article had been tweeted about or blogged about in Sina Weibo might be used as an altmetric indicator of interest in the article. This perspective argues that altmetrics are very suitable for data science research because they are relatively easy to collect on a large scale, have non-trivial statistical properties, and can be used to help in the important issue of developing methods to understand and assess the impact of scientific research.
| Reference Key |
thelwall2016journaldata
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Mike Thelwall |
| Journal | journal of pharmaceutical policy and practice |
| Year | 2016 |
| DOI |
10.20309/jdis.201610
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.