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Physics-based manifold learning in scaffolds for tissue engineering: Application to inverse problems

Overview of attention for article published in Frontiers in Materials, October 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
2 X users

Readers on

mendeley
5 Mendeley
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Title
Physics-based manifold learning in scaffolds for tissue engineering: Application to inverse problems
Published in
Frontiers in Materials, October 2022
DOI 10.3389/fmats.2022.957877
Authors

Alba Muixí, Sergio Zlotnik, Alberto García-González, Pedro Díez

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 20%
Professor 1 20%
Unknown 3 60%
Readers by discipline Count As %
Unspecified 1 20%
Social Sciences 1 20%
Unknown 3 60%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 March 2023.
All research outputs
#19,884,062
of 25,305,422 outputs
Outputs from Frontiers in Materials
#533
of 3,028 outputs
Outputs of similar age
#301,081
of 434,436 outputs
Outputs of similar age from Frontiers in Materials
#28
of 189 outputs
Altmetric has tracked 25,305,422 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,028 research outputs from this source. They receive a mean Attention Score of 1.6. This one has done well, scoring higher than 75% 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 434,436 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 189 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.