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.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature
|
|---|---|
| Published in |
Plant Methods, January 2022
|
| DOI | 10.1186/s13007-022-00839-5 |
| Pubmed ID | |
| Authors |
Chaoxin Wang, Doina Caragea, Nisarga Kodadinne Narayana, Nathan T. Hein, Raju Bheemanahalli, Impa M. Somayanda, S. V. Krishna Jagadish |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 50% |
| United Kingdom | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 10% |
| Lecturer | 4 | 10% |
| Other | 2 | 5% |
| Unspecified | 2 | 5% |
| Student > Doctoral Student | 2 | 5% |
| Other | 8 | 20% |
| Unknown | 18 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 20% |
| Agricultural and Biological Sciences | 5 | 13% |
| Unspecified | 2 | 5% |
| Environmental Science | 1 | 3% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 20 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 26 January 2022.
All research outputs
#5,693,857
of 22,994,508 outputs
Outputs from Plant Methods
#320
of 1,087 outputs
Outputs of similar age
#125,417
of 501,300 outputs
Outputs of similar age from Plant Methods
#16
of 34 outputs
Altmetric has tracked 22,994,508 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,087 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 70% 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 501,300 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 74% of its contemporaries.
We're also able to compare this research output to 34 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 55% of its contemporaries.