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 |
A deep learning mixed-data type approach for the classification of FHR signals
|
|---|---|
| Published in |
Frontiers in Bioengineering and Biotechnology, August 2022
|
| DOI | 10.3389/fbioe.2022.887549 |
| Pubmed ID | |
| Authors |
Edoardo Spairani, Beniamino Daniele, Maria Gabriella Signorini, Giovanni Magenes |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 40% |
| India | 1 | 20% |
| Switzerland | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 16% |
| Unspecified | 2 | 8% |
| Researcher | 2 | 8% |
| Student > Postgraduate | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 5 | 20% |
| Unknown | 8 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 16% |
| Engineering | 3 | 12% |
| Unspecified | 2 | 8% |
| Medicine and Dentistry | 2 | 8% |
| Mathematics | 1 | 4% |
| Other | 4 | 16% |
| Unknown | 9 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 18 August 2022.
All research outputs
#13,399,463
of 23,122,481 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#1,554
of 6,807 outputs
Outputs of similar age
#168,093
of 432,304 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#81
of 696 outputs
Altmetric has tracked 23,122,481 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,807 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 432,304 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 60% of its contemporaries.
We're also able to compare this research output to 696 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.