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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An eQTL biological data visualization challenge and approaches from the visualization community
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-s8-s8 |
| Pubmed ID | |
| Authors |
Christopher W Bartlett, Soo Yeon Cheong, Liping Hou, Jesse Paquette, Pek Yee Lum, Günter Jäger, Florian Battke, Corinna Vehlow, Julian Heinrich, Kay Nieselt, Ryo Sakai, Jan Aerts, William C Ray |
| Abstract |
In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hong Kong | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 7% |
| Belgium | 3 | 5% |
| Canada | 1 | 2% |
| Japan | 1 | 2% |
| Finland | 1 | 2% |
| Unknown | 45 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 29% |
| Researcher | 16 | 29% |
| Student > Master | 5 | 9% |
| Student > Doctoral Student | 4 | 7% |
| Other | 3 | 5% |
| Other | 7 | 13% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 42% |
| Computer Science | 9 | 16% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Medicine and Dentistry | 3 | 5% |
| Psychology | 3 | 5% |
| Other | 11 | 20% |
| Unknown | 3 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 16 September 2012.
All research outputs
#3,761,472
of 22,678,224 outputs
Outputs from BMC Bioinformatics
#1,456
of 7,249 outputs
Outputs of similar age
#25,851
of 163,872 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 104 outputs
Altmetric has tracked 22,678,224 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,249 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 79% 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 163,872 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.