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Twitter Demographics
Mendeley readers
Chapter title |
Spectral Graph Convolutions for Population-Based Disease Prediction
|
---|---|
Chapter number | 21 |
Book title |
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
|
Published by |
Springer, Cham, September 2017
|
DOI | 10.1007/978-3-319-66179-7_21 |
Book ISBNs |
978-3-31-966178-0, 978-3-31-966179-7
|
Authors |
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert |
Twitter Demographics
The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
Unknown | 193 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 46 | 24% |
Student > Master | 34 | 18% |
Researcher | 26 | 13% |
Student > Bachelor | 11 | 6% |
Student > Doctoral Student | 9 | 5% |
Other | 24 | 12% |
Unknown | 44 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 81 | 42% |
Engineering | 23 | 12% |
Neuroscience | 12 | 6% |
Agricultural and Biological Sciences | 6 | 3% |
Mathematics | 5 | 3% |
Other | 14 | 7% |
Unknown | 53 | 27% |