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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mega2: validated data-reformatting for linkage and association analyses
|
|---|---|
| Published in |
Source Code for Biology and Medicine, December 2014
|
| DOI | 10.1186/s13029-014-0026-y |
| Pubmed ID | |
| Authors |
Robert V Baron, Charles Kollar, Nandita Mukhopadhyay, Daniel E Weeks |
| Abstract |
In a typical study of the genetics of a complex human disease, many different analysis programs are used, to test for linkage and association. This requires extensive and careful data reformatting, as many of these analysis programs use differing input formats. Writing scripts to facilitate this can be tedious, time-consuming, and error-prone. To address these issues, the open source Mega2 data reformatting program provides validated and tested data conversions from several commonly-used input formats to many output formats. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Researcher | 5 | 25% |
| Professor | 3 | 15% |
| Student > Master | 2 | 10% |
| Other | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 55% |
| Agricultural and Biological Sciences | 4 | 20% |
| Medicine and Dentistry | 2 | 10% |
| Computer Science | 1 | 5% |
| Unknown | 2 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 27 August 2020.
All research outputs
#6,782,590
of 22,772,779 outputs
Outputs from Source Code for Biology and Medicine
#37
of 127 outputs
Outputs of similar age
#94,213
of 359,774 outputs
Outputs of similar age from Source Code for Biology and Medicine
#1
of 9 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
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 has gotten more attention than average, scoring higher than 70% 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 359,774 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them