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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DNAism: exploring genomic datasets on the web with Horizon Charts
|
|---|---|
| Published in |
BMC Bioinformatics, January 2016
|
| DOI | 10.1186/s12859-016-0891-2 |
| Pubmed ID | |
| Authors |
David Rio Deiros, Richard A. Gibbs, Jeffrey Rogers |
| Abstract |
Computational biologists daily face the need to explore massive amounts of genomic data. New visualization techniques can help researchers navigate and understand these big data. Horizon Charts are a relatively new visualization method that, under the right circumstances, maximizes data density without losing graphical perception. Horizon Charts have been successfully applied to understand multi-metric time series data. We have adapted an existing JavaScript library (Cubism) that implements Horizon Charts for the time series domain so that it works effectively with genomic datasets. We call this new library DNAism. Horizon Charts can be an effective visual tool to explore complex and large genomic datasets. Researchers can use our library to leverage these techniques to extract additional insights from their own datasets. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 13% |
| United States | 1 | 13% |
| Unknown | 6 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 63% |
| Members of the public | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 17 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 28% |
| Student > Master | 4 | 22% |
| Student > Bachelor | 3 | 17% |
| Student > Doctoral Student | 1 | 6% |
| Professor | 1 | 6% |
| Other | 4 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 44% |
| Agricultural and Biological Sciences | 3 | 17% |
| Biochemistry, Genetics and Molecular Biology | 2 | 11% |
| Chemical Engineering | 1 | 6% |
| Economics, Econometrics and Finance | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 1 | 6% |
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 13 December 2019.
All research outputs
#7,818,540
of 25,478,886 outputs
Outputs from BMC Bioinformatics
#2,798
of 7,706 outputs
Outputs of similar age
#116,824
of 406,341 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 134 outputs
Altmetric has tracked 25,478,886 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,706 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 62% 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 406,341 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 70% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.