↓ Skip to main content

Semantic text mining support for lignocellulose research

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2012
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
41 Mendeley
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
Semantic text mining support for lignocellulose research
Published in
BMC Medical Informatics and Decision Making, April 2012
DOI 10.1186/1472-6947-12-s1-s5
Pubmed ID
Authors

Marie-Jean Meurs, Caitlin Murphy, Ingo Morgenstern, Greg Butler, Justin Powlowski, Adrian Tsang, René Witte

Abstract

Biofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 2 5%
Italy 1 2%
Japan 1 2%
Luxembourg 1 2%
Unknown 36 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Student > Master 6 15%
Researcher 5 12%
Student > Bachelor 5 12%
Professor > Associate Professor 4 10%
Other 9 22%
Readers by discipline Count As %
Computer Science 14 34%
Agricultural and Biological Sciences 12 29%
Environmental Science 3 7%
Business, Management and Accounting 3 7%
Engineering 3 7%
Other 6 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 May 2012.
All research outputs
#7,161,397
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#659
of 1,122 outputs
Outputs of similar age
#57,692
of 117,727 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 7 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 35th percentile – i.e., 35% 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 117,727 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.