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Mendeley readers
Attention Score in Context
| Title |
A scalable neuroinformatics data flow for electrophysiological signals using MapReduce
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, March 2015
|
| DOI | 10.3389/fninf.2015.00004 |
| Pubmed ID | |
| Authors |
Catherine Jayapandian, Annan Wei, Priya Ramesh, Bilal Zonjy, Samden D. Lhatoo, Kenneth Loparo, Guo-Qiang Zhang, Satya S. Sahoo |
| Abstract |
Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 17% |
| France | 1 | 17% |
| Canada | 1 | 17% |
| United States | 1 | 17% |
| Sweden | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 26 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 25% |
| Student > Ph. D. Student | 6 | 21% |
| Researcher | 4 | 14% |
| Student > Master | 3 | 11% |
| Professor | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 32% |
| Agricultural and Biological Sciences | 4 | 14% |
| Engineering | 4 | 14% |
| Neuroscience | 3 | 11% |
| Medicine and Dentistry | 2 | 7% |
| Other | 3 | 11% |
| Unknown | 3 | 11% |
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 27 May 2015.
All research outputs
#7,398,509
of 22,792,160 outputs
Outputs from Frontiers in Neuroinformatics
#357
of 749 outputs
Outputs of similar age
#88,799
of 262,012 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#9
of 13 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 749 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one has gotten more attention than average, scoring higher than 51% 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 262,012 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 65% 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 is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.