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X Demographics
Mendeley readers
Attention Score in Context
Title |
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
|
---|---|
Published in |
BMC Bioinformatics, March 2010
|
DOI | 10.1186/1471-2105-11-126 |
Pubmed ID | |
Authors |
Martin Paluszewski, Thomas Hamelryck |
Abstract |
Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations). |
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 % |
---|---|---|
Taiwan | 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 3 | 4% |
Germany | 2 | 3% |
United States | 2 | 3% |
France | 1 | 1% |
Portugal | 1 | 1% |
Canada | 1 | 1% |
India | 1 | 1% |
Spain | 1 | 1% |
Belgium | 1 | 1% |
Other | 0 | 0% |
Unknown | 57 | 81% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 17 | 24% |
Student > Ph. D. Student | 11 | 16% |
Professor | 8 | 11% |
Professor > Associate Professor | 7 | 10% |
Student > Master | 6 | 9% |
Other | 18 | 26% |
Unknown | 3 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 22 | 31% |
Agricultural and Biological Sciences | 19 | 27% |
Engineering | 8 | 11% |
Chemistry | 6 | 9% |
Biochemistry, Genetics and Molecular Biology | 4 | 6% |
Other | 7 | 10% |
Unknown | 4 | 6% |
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 20 April 2012.
All research outputs
#15,243,120
of 22,664,644 outputs
Outputs from BMC Bioinformatics
#5,359
of 7,247 outputs
Outputs of similar age
#76,568
of 93,735 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 56 outputs
Altmetric has tracked 22,664,644 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 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 93,735 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.