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Language trees with sampled ancestors support a hybrid model for the origin of Indo-European languages

Overview of attention for article published in Science, July 2023
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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 (97th percentile)

Citations

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19 Dimensions

Readers on

mendeley
66 Mendeley
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Title
Language trees with sampled ancestors support a hybrid model for the origin of Indo-European languages
Published in
Science, July 2023
DOI 10.1126/science.abg0818
Pubmed ID
URN
urn:nbn:se:su:diva-220000
Authors

Paul Heggarty, Cormac Anderson, Matthew Scarborough, Benedict King, Remco Bouckaert, Lechosław Jocz, Martin Joachim Kümmel, Thomas Jügel, Britta Irslinger, Roland Pooth, Henrik Liljegren, Richard F Strand, Geoffrey Haig, Martin Macák, Ronald I Kim, Erik Anonby, Tijmen Pronk, Oleg Belyaev, Tonya Kim Dewey-Findell, Matthew Boutilier, Cassandra Freiberg, Robert Tegethoff, Matilde Serangeli, Nikos Liosis, Krzysztof Stroński, Kim Schulte, Ganesh Kumar Gupta, Wolfgang Haak, Johannes Krause, Quentin D Atkinson, Simon J Greenhill, Denise Kühnert, Russell D Gray

X Demographics

X Demographics

The data shown below were collected from the profiles of 1,429 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Researcher 13 20%
Student > Bachelor 7 11%
Professor 6 9%
Student > Master 6 9%
Other 9 14%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 15%
Biochemistry, Genetics and Molecular Biology 8 12%
Arts and Humanities 7 11%
Earth and Planetary Sciences 5 8%
Linguistics 3 5%
Other 16 24%
Unknown 17 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1661. 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 24 July 2024.
All research outputs
#6,885
of 26,377,159 outputs
Outputs from Science
#367
of 83,958 outputs
Outputs of similar age
#195
of 369,662 outputs
Outputs of similar age from Science
#9
of 399 outputs
Altmetric has tracked 26,377,159 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 83,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 66.6. This one has done particularly well, scoring higher than 99% 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 369,662 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 399 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 97% of its contemporaries.