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Mendeley readers
Attention Score in Context
| Title |
A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB
|
|---|---|
| Published in |
BMC Bioinformatics, November 2006
|
| DOI | 10.1186/1471-2105-7-489 |
| Pubmed ID | |
| Authors |
Tim F Rayner, Philippe Rocca-Serra, Paul T Spellman, Helen C Causton, Anna Farne, Ele Holloway, Rafael A Irizarry, Junmin Liu, Donald S Maier, Michael Miller, Kjell Petersen, John Quackenbush, Gavin Sherlock, Christian J Stoeckert, Joseph White, Patricia L Whetzel, Farrell Wymore, Helen Parkinson, Ugis Sarkans, Catherine A Ball, Alvis Brazma |
| Abstract |
Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. |
Mendeley readers
The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 5% |
| United Kingdom | 4 | 2% |
| Japan | 3 | 2% |
| Spain | 2 | 1% |
| Switzerland | 1 | <1% |
| Czechia | 1 | <1% |
| Brazil | 1 | <1% |
| Malaysia | 1 | <1% |
| Russia | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 149 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 62 | 35% |
| Student > Ph. D. Student | 37 | 21% |
| Student > Master | 20 | 11% |
| Other | 14 | 8% |
| Professor > Associate Professor | 13 | 7% |
| Other | 23 | 13% |
| Unknown | 6 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 85 | 49% |
| Computer Science | 29 | 17% |
| Biochemistry, Genetics and Molecular Biology | 23 | 13% |
| Medicine and Dentistry | 7 | 4% |
| Engineering | 6 | 3% |
| Other | 17 | 10% |
| Unknown | 8 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 13 November 2022.
All research outputs
#7,576,061
of 23,103,436 outputs
Outputs from BMC Bioinformatics
#3,051
of 7,329 outputs
Outputs of similar age
#24,504
of 70,355 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 62 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
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 50% 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 70,355 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.