You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Dipy, a library for the analysis of diffusion MRI data
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, February 2014
|
| DOI | 10.3389/fninf.2014.00008 |
| Pubmed ID | |
| Authors |
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan van der Walt, Maxime Descoteaux, Ian Nimmo-Smith |
| Abstract |
Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing. |
X Demographics
The data shown below were collected from the profiles of 16 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 | 7 | 44% |
| United Kingdom | 2 | 13% |
| France | 1 | 6% |
| Unknown | 6 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 63% |
| Scientists | 6 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 537 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 2% |
| Canada | 8 | 1% |
| United Kingdom | 4 | <1% |
| Germany | 3 | <1% |
| Spain | 3 | <1% |
| China | 1 | <1% |
| Japan | 1 | <1% |
| France | 1 | <1% |
| Unknown | 507 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 105 | 20% |
| Researcher | 95 | 18% |
| Student > Master | 70 | 13% |
| Student > Bachelor | 35 | 7% |
| Student > Doctoral Student | 29 | 5% |
| Other | 88 | 16% |
| Unknown | 115 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 100 | 19% |
| Engineering | 65 | 12% |
| Medicine and Dentistry | 60 | 11% |
| Computer Science | 53 | 10% |
| Psychology | 28 | 5% |
| Other | 84 | 16% |
| Unknown | 147 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 24 May 2023.
All research outputs
#1,376,477
of 25,837,817 outputs
Outputs from Frontiers in Neuroinformatics
#31
of 849 outputs
Outputs of similar age
#13,567
of 242,412 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#1
of 13 outputs
Altmetric has tracked 25,837,817 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 849 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. 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 242,412 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 93% of its contemporaries.
We're also able to compare this research output to 13 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 92% of its contemporaries.