The data shown below were collected from the profiles of 205 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Applications and Techniques for Fast Machine Learning in Science
|
|---|---|
| Published in |
arXiv, April 2022
|
| DOI | 10.3389/fdata.2022.787421 |
| Pubmed ID | |
| Authors |
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 20 | 10% |
| India | 12 | 6% |
| United Kingdom | 5 | 2% |
| Spain | 3 | 1% |
| Canada | 3 | 1% |
| Russia | 3 | 1% |
| Brazil | 2 | <1% |
| Ireland | 2 | <1% |
| Switzerland | 2 | <1% |
| Other | 23 | 11% |
| Unknown | 130 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 162 | 79% |
| Scientists | 42 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 97 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 16% |
| Researcher | 13 | 13% |
| Student > Master | 5 | 5% |
| Student > Doctoral Student | 4 | 4% |
| Professor | 4 | 4% |
| Other | 16 | 16% |
| Unknown | 39 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 12% |
| Engineering | 11 | 11% |
| Physics and Astronomy | 8 | 8% |
| Unspecified | 5 | 5% |
| Chemistry | 3 | 3% |
| Other | 13 | 13% |
| Unknown | 45 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 148. 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 December 2023.
All research outputs
#297,915
of 26,503,921 outputs
Outputs from arXiv
#3,268
of 1,003,568 outputs
Outputs of similar age
#8,292
of 455,157 outputs
Outputs of similar age from arXiv
#84
of 28,325 outputs
Altmetric has tracked 26,503,921 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,003,568 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 99% 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 455,157 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 98% of its contemporaries.
We're also able to compare this research output to 28,325 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 99% of its contemporaries.