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
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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
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% |