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X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Understanding and Exploiting Dependent Variables with Deep Metric Learning
|
---|---|
Chapter number | 8 |
Book title |
Intelligent Systems and Applications
|
Published in |
arXiv, September 2020
|
DOI | 10.1007/978-3-030-55180-3_8 |
Book ISBNs |
978-3-03-055179-7, 978-3-03-055180-3
|
Authors |
Niall O’Mahony, Sean Campbell, Anderson Carvalho, Lenka Krpalkova, Gustavo Velasco-Hernandez, Daniel Riordan, Joseph Walsh, Niall O' Mahony, O’Mahony, Niall, Campbell, Sean, Carvalho, Anderson, Krpalkova, Lenka, Velasco-Hernandez, Gustavo, Riordan, Daniel, Walsh, Joseph |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 14% |
Netherlands | 1 | 14% |
Japan | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 86% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 7 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 1 | 14% |
Researcher | 1 | 14% |
Lecturer | 1 | 14% |
Student > Master | 1 | 14% |
Unknown | 3 | 43% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 1 | 14% |
Physics and Astronomy | 1 | 14% |
Neuroscience | 1 | 14% |
Engineering | 1 | 14% |
Unknown | 3 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 09 September 2020.
All research outputs
#13,798,575
of 24,093,053 outputs
Outputs from arXiv
#204,377
of 1,018,817 outputs
Outputs of similar age
#191,604
of 402,105 outputs
Outputs of similar age from arXiv
#6,450
of 32,356 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 78% 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 402,105 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 51% of its contemporaries.
We're also able to compare this research output to 32,356 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.