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.
Timeline
X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Databases for Wheat Genomics and Crop Improvement
|
---|---|
Chapter number | 18 |
Book title |
Wheat Biotechnology
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7337-8_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7335-4, 978-1-4939-7337-8
|
Authors |
Yuxuan Yuan, Armin Scheben, Chon-Kit Kenneth Chan, David Edwards |
Abstract |
The genomics revolution brought on by advances in high-throughput sequencing has led to the production of vast amounts of data. Databases play an essential role in storing and managing this information to make it available to researchers and crop breeders. This chapter provides an outline of how to use databases and tools for wheat genome research. |
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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 13 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Unspecified | 3 | 23% |
Student > Ph. D. Student | 3 | 23% |
Student > Doctoral Student | 2 | 15% |
Student > Bachelor | 1 | 8% |
Student > Master | 1 | 8% |
Other | 1 | 8% |
Unknown | 2 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Unspecified | 3 | 23% |
Agricultural and Biological Sciences | 3 | 23% |
Biochemistry, Genetics and Molecular Biology | 2 | 15% |
Computer Science | 1 | 8% |
Medicine and Dentistry | 1 | 8% |
Other | 1 | 8% |
Unknown | 2 | 15% |
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 16 September 2017.
All research outputs
#20,447,499
of 23,002,898 outputs
Outputs from Methods in molecular biology
#9,937
of 13,156 outputs
Outputs of similar age
#356,155
of 421,223 outputs
Outputs of similar age from Methods in molecular biology
#842
of 1,074 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,156 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 421,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.