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Attention Score in Context
| Title |
Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data
|
|---|---|
| Published in |
BMC Bioinformatics, October 2014
|
| DOI | 10.1186/1471-2105-15-s11-s5 |
| Pubmed ID | |
| Authors |
Wenchao Zhang, Patrick X Zhao |
| Abstract |
Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 33% |
| Australia | 1 | 33% |
| Germany | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 104 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 25% |
| Student > Master | 19 | 18% |
| Researcher | 19 | 18% |
| Student > Bachelor | 13 | 12% |
| Student > Doctoral Student | 7 | 7% |
| Other | 8 | 8% |
| Unknown | 14 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 23% |
| Biochemistry, Genetics and Molecular Biology | 19 | 18% |
| Chemistry | 16 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 6% |
| Immunology and Microbiology | 4 | 4% |
| Other | 15 | 14% |
| Unknown | 22 | 21% |
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 15 October 2021.
All research outputs
#3,729,576
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#1,423
of 7,276 outputs
Outputs of similar age
#43,782
of 259,776 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 132 outputs
Altmetric has tracked 22,774,233 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 7,276 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 80% 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 259,776 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 83% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.