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Attention Score in Context
| Title |
NSDF: Neuroscience Simulation Data Format
|
|---|---|
| Published in |
Neuroinformatics, November 2015
|
| DOI | 10.1007/s12021-015-9282-5 |
| Pubmed ID | |
| Authors |
Subhasis Ray, Chaitanya Chintaluri, Upinder S. Bhalla, Daniel K. Wójcik |
| Abstract |
Data interchange is emerging as an essential aspect of modern neuroscience. In the areas of computational neuroscience and systems biology there are multiple model definition formats, which have contributed strongly to the development of an ecosystem of simulation and analysis tools. Here we report the development of the Neuroscience Simulation Data Format (NSDF) which extends this ecosystem to the data generated in simulations. NSDF is designed to store simulator output across scales: from multiscale chemical and electrical signaling models, to detailed single-neuron and network models, to abstract neural nets. It is self-documenting, efficient, modular, and scalable, both in terms of novel data types and in terms of data volume. NSDF is simulator-independent, and can be used by a range of standalone analysis and visualization tools. It may also be used to store variety of experimental data. NSDF is based on the widely used HDF5 (Hierarchical Data Format 5) specification and is open, platform-independent, and portable. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 5% |
| Belarus | 1 | 3% |
| Unknown | 37 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 18% |
| Researcher | 7 | 18% |
| Student > Bachelor | 6 | 15% |
| Student > Master | 6 | 15% |
| Other | 3 | 8% |
| Other | 5 | 13% |
| Unknown | 6 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 20% |
| Agricultural and Biological Sciences | 5 | 13% |
| Engineering | 4 | 10% |
| Neuroscience | 3 | 8% |
| Physics and Astronomy | 2 | 5% |
| Other | 6 | 15% |
| Unknown | 12 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 02 November 2016.
All research outputs
#6,963,629
of 22,834,308 outputs
Outputs from Neuroinformatics
#133
of 405 outputs
Outputs of similar age
#108,835
of 386,487 outputs
Outputs of similar age from Neuroinformatics
#1
of 5 outputs
Altmetric has tracked 22,834,308 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 405 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 66% 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 386,487 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them