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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning
|
|---|---|
| Published in |
npj Computational Materials, June 2019
|
| DOI | 10.1038/s41524-019-0202-3 |
| Authors |
Artem A. Trofimov, Alison A. Pawlicki, Nikolay Borodinov, Shovon Mandal, Teresa J. Mathews, Mark Hildebrand, Maxim A. Ziatdinov, Katherine A. Hausladen, Paulina K. Urbanowicz, Chad A. Steed, Anton V. Ievlev, Alex Belianinov, Joshua K. Michener, Rama Vasudevan, Olga S. Ovchinnikova |
X Demographics
The data shown below were collected from the profiles of 17 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 | 35% |
| Ireland | 1 | 6% |
| Australia | 1 | 6% |
| South Africa | 1 | 6% |
| Unknown | 8 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 82% |
| Scientists | 3 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 20% |
| Student > Master | 7 | 13% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Bachelor | 6 | 11% |
| Student > Doctoral Student | 3 | 5% |
| Other | 9 | 16% |
| Unknown | 13 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 18% |
| Agricultural and Biological Sciences | 6 | 11% |
| Computer Science | 3 | 5% |
| Materials Science | 3 | 5% |
| Medicine and Dentistry | 3 | 5% |
| Other | 15 | 27% |
| Unknown | 16 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 25. 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 20 November 2019.
All research outputs
#1,348,802
of 23,151,189 outputs
Outputs from npj Computational Materials
#74
of 885 outputs
Outputs of similar age
#31,814
of 353,825 outputs
Outputs of similar age from npj Computational Materials
#3
of 27 outputs
Altmetric has tracked 23,151,189 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 885 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done particularly well, scoring higher than 91% 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 353,825 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 91% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.