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Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification

Overview of attention for article published in Mathematics and Mechanics of Solids, January 2024
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

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4 X users

Citations

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1 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification
Published in
Mathematics and Mechanics of Solids, January 2024
DOI 10.1177/10812865231206544
Authors

Jan Hinrichsen, Lea Feiler, Nina Reiter, Lars Bräuer, Martin Schicht, Friedrich Paulsen, Silvia Budday

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 50%
Unknown 1 50%
Readers by discipline Count As %
Environmental Science 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 January 2024.
All research outputs
#7,746,634
of 25,159,758 outputs
Outputs from Mathematics and Mechanics of Solids
#11
of 78 outputs
Outputs of similar age
#53,288
of 188,484 outputs
Outputs of similar age from Mathematics and Mechanics of Solids
#1
of 1 outputs
Altmetric has tracked 25,159,758 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 78 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 84% 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 188,484 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them