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.
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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature
|
|---|---|
| Published in |
Research Metrics and Analytics (RMA), November 2020
|
| DOI | 10.3389/frma.2020.596624 |
| Pubmed ID | |
| Authors |
Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han Wu |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 13% |
| Student > Doctoral Student | 2 | 13% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Lecturer | 1 | 7% |
| Student > Ph. D. Student | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 7 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 20% |
| Engineering | 2 | 13% |
| Business, Management and Accounting | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Design | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 7 | 47% |
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 25 November 2020.
All research outputs
#14,445,197
of 25,643,886 outputs
Outputs from Research Metrics and Analytics (RMA)
#205
of 363 outputs
Outputs of similar age
#240,299
of 524,782 outputs
Outputs of similar age from Research Metrics and Analytics (RMA)
#9
of 15 outputs
Altmetric has tracked 25,643,886 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 363 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one is in the 42nd percentile – i.e., 42% 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 524,782 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 53% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.