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 |
Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, May 2017
|
| DOI | 10.3389/fninf.2017.00032 |
| Pubmed ID | |
| Authors |
Jorge L. Bernal-Rusiel, Nicolas Rannou, Randy L. Gollub, Steve Pieper, Shawn Murphy, Richard Robertson, Patricia E. Grant, Rudolph Pienaar |
| Abstract |
In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution. |
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 % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 22% |
| Student > Bachelor | 5 | 14% |
| Student > Ph. D. Student | 5 | 14% |
| Student > Master | 4 | 11% |
| Professor > Associate Professor | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 10 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 22% |
| Medicine and Dentistry | 5 | 14% |
| Neuroscience | 4 | 11% |
| Engineering | 3 | 8% |
| Nursing and Health Professions | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 11 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 May 2017.
All research outputs
#15,457,417
of 22,968,808 outputs
Outputs from Frontiers in Neuroinformatics
#554
of 752 outputs
Outputs of similar age
#194,771
of 310,759 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#14
of 17 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 752 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 20th percentile – i.e., 20% 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 310,759 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.