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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Developing a kidney and urinary pathway knowledge base
|
|---|---|
| Published in |
Journal of Biomedical Semantics, May 2011
|
| DOI | 10.1186/2041-1480-2-s2-s7 |
| Pubmed ID | |
| Authors |
Simon Jupp, Julie Klein, Joost Schanstra, Robert Stevens |
| Abstract |
Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 6% |
| United Kingdom | 2 | 4% |
| Brazil | 1 | 2% |
| India | 1 | 2% |
| Iran, Islamic Republic of | 1 | 2% |
| Australia | 1 | 2% |
| Japan | 1 | 2% |
| Spain | 1 | 2% |
| Unknown | 42 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 36% |
| Student > Ph. D. Student | 11 | 21% |
| Professor > Associate Professor | 6 | 11% |
| Student > Postgraduate | 3 | 6% |
| Other | 3 | 6% |
| Other | 5 | 9% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 32% |
| Agricultural and Biological Sciences | 16 | 30% |
| Medicine and Dentistry | 3 | 6% |
| Immunology and Microbiology | 2 | 4% |
| Psychology | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 11 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 July 2017.
All research outputs
#14,697,544
of 24,143,470 outputs
Outputs from Journal of Biomedical Semantics
#201
of 364 outputs
Outputs of similar age
#83,335
of 114,837 outputs
Outputs of similar age from Journal of Biomedical Semantics
#9
of 9 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 43rd percentile – i.e., 43% 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 114,837 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.