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Crowdsourced earthquake early warning

Overview of attention for article published in Science Advances, April 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
32 news outlets
blogs
10 blogs
twitter
96 tweeters
facebook
9 Facebook pages
googleplus
5 Google+ users

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
90 Mendeley
citeulike
1 CiteULike
Title
Crowdsourced earthquake early warning
Published in
Science Advances, April 2015
DOI 10.1126/sciadv.1500036
Pubmed ID
Authors

Sarah E. Minson, Benjamin A. Brooks, Craig L. Glennie, Jessica R. Murray, John O. Langbein, Susan E. Owen, Thomas H. Heaton, Robert A. Iannucci, Darren L. Hauser

Abstract

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Twitter Demographics

The data shown below were collected from the profiles of 96 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Brazil 1 1%
Israel 1 1%
Spain 1 1%
Greece 1 1%
Unknown 84 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 30%
Student > Master 18 20%
Researcher 13 14%
Student > Bachelor 8 9%
Professor > Associate Professor 6 7%
Other 17 19%
Unknown 1 1%
Readers by discipline Count As %
Earth and Planetary Sciences 30 33%
Computer Science 18 20%
Engineering 15 17%
Unspecified 9 10%
Environmental Science 4 4%
Other 13 14%
Unknown 1 1%

Attention Score in Context

This research output has an Altmetric Attention Score of 412. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 April 2016.
All research outputs
#22,617
of 13,013,316 outputs
Outputs from Science Advances
#201
of 2,902 outputs
Outputs of similar age
#455
of 222,889 outputs
Outputs of similar age from Science Advances
#5
of 52 outputs
Altmetric has tracked 13,013,316 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,902 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 124.9. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 222,889 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.