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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Reaction-diffusion in the NEURON simulator
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, January 2013
|
| DOI | 10.3389/fninf.2013.00028 |
| Pubmed ID | |
| Authors |
Robert A. McDougal, Michael L. Hines, William W. Lytton |
| Abstract |
In order to support research on the role of cell biological principles (genomics, proteomics, signaling cascades and reaction dynamics) on the dynamics of neuronal response in health and disease, NEURON's Reaction-Diffusion (rxd) module in Python provides specification and simulation for these dynamics, coupled with the electrophysiological dynamics of the cell membrane. Arithmetic operations on species and parameters are overloaded, allowing arbitrary reaction formulas to be specified using Python syntax. These expressions are then transparently compiled into bytecode that uses NumPy for fast vectorized calculations. At each time step, rxd combines NEURON's integrators with SciPy's sparse linear algebra library. |
X Demographics
The data shown below were collected from the profiles of 3 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 Kingdom | 1 | 33% |
| Sweden | 1 | 33% |
| France | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 3% |
| Unknown | 60 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 27% |
| Student > Ph. D. Student | 9 | 15% |
| Student > Master | 7 | 11% |
| Professor > Associate Professor | 6 | 10% |
| Student > Doctoral Student | 5 | 8% |
| Other | 12 | 19% |
| Unknown | 6 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 17 | 27% |
| Engineering | 13 | 21% |
| Agricultural and Biological Sciences | 11 | 18% |
| Physics and Astronomy | 4 | 6% |
| Computer Science | 3 | 5% |
| Other | 5 | 8% |
| Unknown | 9 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 15 November 2013.
All research outputs
#14,322,495
of 24,226,848 outputs
Outputs from Frontiers in Neuroinformatics
#458
of 795 outputs
Outputs of similar age
#166,295
of 289,058 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#24
of 36 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 795 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 289,058 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.