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X Demographics
Mendeley readers
Attention Score in Context
| Title |
STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior
|
|---|---|
| Published in |
Frontiers in Neuroscience, June 2019
|
| DOI | 10.3389/fnins.2019.00626 |
| Pubmed ID | |
| Authors |
Shuhei J. Yamazaki, Kazuya Ohara, Kentaro Ito, Nobuo Kokubun, Takuma Kitanishi, Daisuke Takaichi, Yasufumi Yamada, Yosuke Ikejiri, Fumie Hiramatsu, Kosuke Fujita, Yuki Tanimoto, Akiko Yamazoe-Umemoto, Koichi Hashimoto, Katsufumi Sato, Ken Yoda, Akinori Takahashi, Yuki Ishikawa, Azusa Kamikouchi, Shizuko Hiryu, Takuya Maekawa, Koutarou D. Kimura |
X Demographics
The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 3 | 12% |
| United States | 3 | 12% |
| Switzerland | 2 | 8% |
| United Kingdom | 1 | 4% |
| Australia | 1 | 4% |
| Turkey | 1 | 4% |
| Sri Lanka | 1 | 4% |
| Japan | 1 | 4% |
| Unknown | 12 | 48% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 23 | 92% |
| Scientists | 2 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 9 | 15% |
| Student > Master | 8 | 13% |
| Researcher | 7 | 11% |
| Student > Ph. D. Student | 7 | 11% |
| Student > Doctoral Student | 3 | 5% |
| Other | 10 | 16% |
| Unknown | 18 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 11 | 18% |
| Agricultural and Biological Sciences | 9 | 15% |
| Biochemistry, Genetics and Molecular Biology | 5 | 8% |
| Computer Science | 4 | 6% |
| Engineering | 4 | 6% |
| Other | 9 | 15% |
| Unknown | 20 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 35. 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 29 November 2019.
All research outputs
#1,149,620
of 25,385,509 outputs
Outputs from Frontiers in Neuroscience
#502
of 11,543 outputs
Outputs of similar age
#24,938
of 365,627 outputs
Outputs of similar age from Frontiers in Neuroscience
#17
of 324 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,543 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 95% 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 365,627 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 93% of its contemporaries.
We're also able to compare this research output to 324 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 94% of its contemporaries.