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Mendeley readers
Attention Score in Context
| Title |
MetaboMiner – semi-automated identification of metabolites from 2D NMR spectra of complex biofluids
|
|---|---|
| Published in |
BMC Bioinformatics, November 2008
|
| DOI | 10.1186/1471-2105-9-507 |
| Pubmed ID | |
| Authors |
Jianguo Xia, Trent C Bjorndahl, Peter Tang, David S Wishart |
| Abstract |
One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies. |
Mendeley readers
The data shown below were compiled from readership statistics for 187 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 1% |
| Netherlands | 2 | 1% |
| Switzerland | 2 | 1% |
| Germany | 1 | <1% |
| Colombia | 1 | <1% |
| Slovakia | 1 | <1% |
| Denmark | 1 | <1% |
| Russia | 1 | <1% |
| United States | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 175 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 53 | 28% |
| Student > Ph. D. Student | 29 | 16% |
| Student > Master | 19 | 10% |
| Student > Doctoral Student | 17 | 9% |
| Student > Bachelor | 15 | 8% |
| Other | 31 | 17% |
| Unknown | 23 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 56 | 30% |
| Chemistry | 43 | 23% |
| Biochemistry, Genetics and Molecular Biology | 20 | 11% |
| Computer Science | 12 | 6% |
| Medicine and Dentistry | 7 | 4% |
| Other | 19 | 10% |
| Unknown | 30 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 August 2022.
All research outputs
#4,778,112
of 23,103,436 outputs
Outputs from BMC Bioinformatics
#1,824
of 7,329 outputs
Outputs of similar age
#25,856
of 167,112 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 46 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,329 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 gotten more attention than average, scoring higher than 73% 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 167,112 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 78% of its contemporaries.
We're also able to compare this research output to 46 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 69% of its contemporaries.