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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep Neural Networks for Optimal Team Composition
|
|---|---|
| Published in |
arXiv, June 2019
|
| DOI | 10.3389/fdata.2019.00014 |
| Pubmed ID | |
| Authors |
Anna Sapienza, Palash Goyal, Emilio Ferrara |
X Demographics
The data shown below were collected from the profiles of 32 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 | 6 | 19% |
| United Kingdom | 3 | 9% |
| Russia | 2 | 6% |
| Turkey | 1 | 3% |
| Spain | 1 | 3% |
| Switzerland | 1 | 3% |
| Italy | 1 | 3% |
| Japan | 1 | 3% |
| Unknown | 16 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 66% |
| Scientists | 11 | 34% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 23% |
| Student > Master | 8 | 13% |
| Researcher | 8 | 13% |
| Student > Doctoral Student | 4 | 7% |
| Professor | 2 | 3% |
| Other | 4 | 7% |
| Unknown | 20 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 22% |
| Engineering | 4 | 7% |
| Business, Management and Accounting | 3 | 5% |
| Psychology | 3 | 5% |
| Economics, Econometrics and Finance | 2 | 3% |
| Other | 12 | 20% |
| Unknown | 23 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 17 June 2019.
All research outputs
#2,047,314
of 25,193,883 outputs
Outputs from arXiv
#33,730
of 1,026,194 outputs
Outputs of similar age
#42,953
of 360,208 outputs
Outputs of similar age from arXiv
#1,023
of 26,510 outputs
Altmetric has tracked 25,193,883 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,026,194 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 96% 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 360,208 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 26,510 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 96% of its contemporaries.