| Title |
A generalizable and accessible approach to machine learning with global satellite imagery
|
|---|---|
| Published in |
Nature Communications, July 2021
|
| DOI | 10.1038/s41467-021-24638-z |
| Pubmed ID | |
| Authors |
Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang |
X Demographics
The data shown below were collected from the profiles of 44 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 | 14 | 32% |
| India | 3 | 7% |
| Uganda | 1 | 2% |
| Saudi Arabia | 1 | 2% |
| France | 1 | 2% |
| Canada | 1 | 2% |
| Kenya | 1 | 2% |
| Nigeria | 1 | 2% |
| Japan | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 19 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 32 | 73% |
| Scientists | 8 | 18% |
| Science communicators (journalists, bloggers, editors) | 3 | 7% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 184 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 184 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 14% |
| Researcher | 24 | 13% |
| Student > Master | 21 | 11% |
| Student > Doctoral Student | 13 | 7% |
| Professor | 7 | 4% |
| Other | 23 | 13% |
| Unknown | 70 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 16 | 9% |
| Economics, Econometrics and Finance | 15 | 8% |
| Environmental Science | 15 | 8% |
| Computer Science | 13 | 7% |
| Agricultural and Biological Sciences | 11 | 6% |
| Other | 31 | 17% |
| Unknown | 83 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 142. 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 07 December 2023.
All research outputs
#291,290
of 25,436,226 outputs
Outputs from Nature Communications
#4,332
of 57,068 outputs
Outputs of similar age
#7,986
of 445,891 outputs
Outputs of similar age from Nature Communications
#152
of 1,867 outputs
Altmetric has tracked 25,436,226 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 57,068 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 55.6. This one has done particularly well, scoring higher than 92% 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 445,891 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 1,867 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.