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Mendeley readers
Attention Score in Context
| Title |
Towards interoperable and reproducible QSAR analyses: Exchange of datasets
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2010
|
| DOI | 10.1186/1758-2946-2-5 |
| Pubmed ID | |
| Authors |
Ola Spjuth, Egon L Willighagen, Rajarshi Guha, Martin Eklund, Jarl ES Wikberg |
| Abstract |
QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 3 | 5% |
| United States | 3 | 5% |
| United Kingdom | 2 | 3% |
| Netherlands | 1 | 2% |
| Bulgaria | 1 | 2% |
| India | 1 | 2% |
| Cyprus | 1 | 2% |
| Iran, Islamic Republic of | 1 | 2% |
| Germany | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 50 | 78% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 23% |
| Researcher | 14 | 22% |
| Professor > Associate Professor | 7 | 11% |
| Student > Master | 6 | 9% |
| Student > Bachelor | 5 | 8% |
| Other | 11 | 17% |
| Unknown | 6 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 23% |
| Agricultural and Biological Sciences | 12 | 19% |
| Computer Science | 9 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 11% |
| Engineering | 4 | 6% |
| Other | 9 | 14% |
| Unknown | 8 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 01 May 2013.
All research outputs
#2,644,641
of 25,079,131 outputs
Outputs from Journal of Cheminformatics
#235
of 942 outputs
Outputs of similar age
#9,594
of 99,815 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 25,079,131 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 942 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. 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 99,815 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 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them