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Visuo-dynamic self-modelling of soft robotic systems

Overview of attention for article published in Frontiers in Robotics and AI, June 2024
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Title
Visuo-dynamic self-modelling of soft robotic systems
Published in
Frontiers in Robotics and AI, June 2024
DOI 10.3389/frobt.2024.1403733
Authors

Richard Marques Monteiro, Jialei Shi, Helge Wurdemann, Fumiya Iida, Thomas George Thuruthel

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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 06 June 2024.
All research outputs
#23,416,163
of 26,086,865 outputs
Outputs from Frontiers in Robotics and AI
#1,785
of 1,803 outputs
Outputs of similar age
#122,329
of 152,562 outputs
Outputs of similar age from Frontiers in Robotics and AI
#6
of 14 outputs
Altmetric has tracked 26,086,865 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,803 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 152,562 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.