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Mendeley readers
Attention Score in Context
| Title |
Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation
|
|---|---|
| Published in |
BMC Genomic Data, October 2013
|
| DOI | 10.1186/1471-2156-14-104 |
| Pubmed ID | |
| Authors |
Nitika Sandhu, Sunita Jain, Arvind Kumar, Balwant Singh Mehla, Rajinder Jain |
| Abstract |
Water scarcity and drought have seriously threatened traditional rice cultivation practices in several parts of the world, including India. Aerobic rice that uses significantly less water than traditional flooded systems has emerged as a promising water-saving technology. The identification of QTL conferring improved aerobic adaptation may facilitate the development of high-yielding aerobic rice varieties. In this study, experiments were conducted for mapping QTL for yield, root-related traits, and agronomic traits under aerobic conditions using HKR47 × MAS26 and MASARB25 × Pusa Basmati 1460 F2:3 mapping populations. |
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.
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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| Indonesia | 1 | 2% |
| India | 1 | 2% |
| Mexico | 1 | 2% |
| Philippines | 1 | 2% |
| Unknown | 60 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 37% |
| Researcher | 11 | 17% |
| Student > Master | 5 | 8% |
| Student > Postgraduate | 3 | 5% |
| Student > Doctoral Student | 2 | 3% |
| Other | 10 | 15% |
| Unknown | 10 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 46 | 71% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Unspecified | 1 | 2% |
| Chemical Engineering | 1 | 2% |
| Psychology | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 13 | 20% |
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 17 June 2014.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from BMC Genomic Data
#1,008
of 1,204 outputs
Outputs of similar age
#199,273
of 225,443 outputs
Outputs of similar age from BMC Genomic Data
#17
of 20 outputs
Altmetric has tracked 25,374,647 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 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. 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 225,443 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 20 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.