↓ Skip to main content

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB

Overview of attention for article published in BMC Bioinformatics, November 2006
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
167 Dimensions

Readers on

mendeley
171 Mendeley
citeulike
7 CiteULike
connotea
9 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 171 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%
Czechia 1 <1%
Brazil 1 <1%
Hong Kong 1 <1%
Iceland 1 <1%
Russia 1 <1%
Other 3 2%
Unknown 145 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 37%
Student > Ph. D. Student 38 22%
Student > Master 19 11%
Other 14 8%
Professor > Associate Professor 11 6%
Other 22 13%
Unknown 4 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 86 50%
Computer Science 29 17%
Biochemistry, Genetics and Molecular Biology 22 13%
Medicine and Dentistry 7 4%
Engineering 5 3%
Other 15 9%
Unknown 7 4%

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 20 October 2018.
All research outputs
#4,100,586
of 13,647,261 outputs
Outputs from BMC Bioinformatics
#1,922
of 5,080 outputs
Outputs of similar age
#80,590
of 279,510 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 43 outputs
Altmetric has tracked 13,647,261 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,080 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 53% 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 279,510 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 66% of its contemporaries.
We're also able to compare this research output to 43 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 62% of its contemporaries.