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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, August 2019
|
| DOI | 10.3389/fncom.2019.00054 |
| Pubmed ID | |
| Authors |
Leon Stefanovski, Paul Triebkorn, Andreas Spiegler, Margarita-Arimatea Diaz-Cortes, Ana Solodkin, Viktor Jirsa, Anthony Randal McIntosh, Petra Ritter, for the Alzheimer's Disease Neuroimaging Initiative |
X Demographics
The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 13% |
| Colombia | 1 | 4% |
| France | 1 | 4% |
| United Kingdom | 1 | 4% |
| Switzerland | 1 | 4% |
| Canada | 1 | 4% |
| Unknown | 16 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 83% |
| Scientists | 4 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 168 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 32 | 19% |
| Researcher | 23 | 14% |
| Student > Master | 20 | 12% |
| Student > Bachelor | 19 | 11% |
| Student > Doctoral Student | 9 | 5% |
| Other | 21 | 13% |
| Unknown | 44 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 36 | 21% |
| Engineering | 15 | 9% |
| Psychology | 11 | 7% |
| Computer Science | 10 | 6% |
| Medicine and Dentistry | 9 | 5% |
| Other | 30 | 18% |
| Unknown | 57 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 24. 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 16 October 2023.
All research outputs
#1,614,613
of 25,711,518 outputs
Outputs from Frontiers in Computational Neuroscience
#59
of 1,475 outputs
Outputs of similar age
#33,041
of 354,876 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#1
of 24 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. 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 354,876 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 90% of its contemporaries.
We're also able to compare this research output to 24 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 95% of its contemporaries.