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 |
Toward mechanistic medical digital twins: some use cases in immunology
|
|---|---|
| Published in |
Frontiers in Digital Health, March 2024
|
| DOI | 10.3389/fdgth.2024.1349595 |
| Pubmed ID | |
| Authors |
Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L. Fonseca, James Glazier, Tomas Helikar, Marti Jett-Tilton, Denise Kirschner, Paul Macklin, Borna Mehrad, Beth Moore, Virginia Pasour, Ilya Shmulevich, Amber Smith, Isabel Voigt, Thomas E. Yankeelov, Tjalf Ziemssen |
X Demographics
The data shown below were collected from the profiles of 4 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 | 2 | 50% |
| Switzerland | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 2 | 25% |
| Professor | 1 | 13% |
| Student > Bachelor | 1 | 13% |
| Student > Doctoral Student | 1 | 13% |
| Unknown | 3 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 2 | 25% |
| Agricultural and Biological Sciences | 1 | 13% |
| Immunology and Microbiology | 1 | 13% |
| Medicine and Dentistry | 1 | 13% |
| Engineering | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 2 | 25% |
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 09 March 2024.
All research outputs
#14,657,487
of 25,457,297 outputs
Outputs from Frontiers in Digital Health
#376
of 834 outputs
Outputs of similar age
#58,889
of 164,598 outputs
Outputs of similar age from Frontiers in Digital Health
#7
of 44 outputs
Altmetric has tracked 25,457,297 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 834 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has gotten more attention than average, scoring higher than 53% 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 164,598 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 63% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.