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X Demographics
Mendeley readers
Attention Score in Context
| Title |
FCC – An automated rule-based processing tool for life science data
|
|---|---|
| Published in |
Source Code for Biology and Medicine, January 2013
|
| DOI | 10.1186/1751-0473-8-3 |
| Pubmed ID | |
| Authors |
Simon Barkow-Oesterreicher, Can Türker, Christian Panse |
| Abstract |
Data processing in the bioinformatics field often involves the handling of diverse software programs in one workflow. The field is lacking a set of standards for file formats so that files have to be processed in different ways in order to make them compatible to different analysis programs. The problem is that mass spectrometry vendors at most provide only closed-source Windows libraries to programmatically access their proprietary binary formats. This prohibits the creation of an efficient and unified tool that fits all processing needs of the users. Therefore, researchers are spending a significant amount of time using GUI-based conversion and processing programs. Besides the time needed for manual usage, such programs also can show long running times for processing, because most of them make use of only a single CPU. In particular, algorithms to enhance data quality, e.g. peak picking or deconvolution of spectra, add waiting time for the users. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 4% |
| Unknown | 26 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 33% |
| Student > Ph. D. Student | 6 | 22% |
| Student > Bachelor | 5 | 19% |
| Professor > Associate Professor | 2 | 7% |
| Other | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 30% |
| Biochemistry, Genetics and Molecular Biology | 5 | 19% |
| Computer Science | 5 | 19% |
| Engineering | 2 | 7% |
| Chemistry | 2 | 7% |
| Other | 4 | 15% |
| Unknown | 1 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 28 March 2013.
All research outputs
#18,347,414
of 22,721,584 outputs
Outputs from Source Code for Biology and Medicine
#102
of 127 outputs
Outputs of similar age
#219,362
of 282,369 outputs
Outputs of similar age from Source Code for Biology and Medicine
#6
of 6 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 13th percentile – i.e., 13% 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 282,369 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.