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X Demographics
Mendeley readers
Attention Score in Context
| Title |
InCHlib – interactive cluster heatmap for web applications
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2014
|
| DOI | 10.1186/s13321-014-0044-4 |
| Pubmed ID | |
| Authors |
Ctibor Škuta, Petr Bartůněk, Daniel Svozil |
| Abstract |
Hierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called 'cluster heatmap' is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 25% |
| United States | 1 | 25% |
| Netherlands | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 7% |
| Brazil | 3 | 5% |
| France | 1 | 2% |
| Unknown | 48 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 29% |
| Student > Ph. D. Student | 14 | 25% |
| Student > Master | 12 | 21% |
| Student > Bachelor | 3 | 5% |
| Other | 3 | 5% |
| Other | 3 | 5% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 29% |
| Computer Science | 15 | 27% |
| Chemistry | 9 | 16% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Engineering | 2 | 4% |
| Other | 1 | 2% |
| Unknown | 8 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 11 November 2015.
All research outputs
#12,903,654
of 22,764,165 outputs
Outputs from Journal of Cheminformatics
#625
of 828 outputs
Outputs of similar age
#112,133
of 249,473 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 9 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 23rd percentile – i.e., 23% 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 249,473 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 54% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.