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BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

Overview of attention for article published in BMC Bioinformatics, June 2011
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3 X users

Citations

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105 Dimensions

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110 Mendeley
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11 CiteULike
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Title
BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results
Published in
BMC Bioinformatics, June 2011
DOI 10.1186/1471-2105-12-257
Pubmed ID
Authors

Ubbo Visser, Saminda Abeyruwan, Uma Vempati, Robin P Smith, Vance Lemmon, Stephan C Schürer

Abstract

High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 3 3%
Netherlands 2 2%
Brazil 2 2%
France 1 <1%
Bulgaria 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 93 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 32%
Student > Ph. D. Student 21 19%
Other 9 8%
Student > Master 9 8%
Student > Bachelor 7 6%
Other 21 19%
Unknown 8 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 26%
Computer Science 24 22%
Chemistry 16 15%
Engineering 8 7%
Medicine and Dentistry 5 5%
Other 15 14%
Unknown 13 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 March 2016.
All research outputs
#15,207,446
of 24,143,470 outputs
Outputs from BMC Bioinformatics
#4,864
of 7,506 outputs
Outputs of similar age
#82,933
of 118,020 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 102 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% 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 118,020 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.