| Title |
Confidence limits, error bars and method comparison in molecular modeling. Part 1: The calculation of confidence intervals
|
|---|---|
| Published in |
Perspectives in Drug Discovery and Design, June 2014
|
| DOI | 10.1007/s10822-014-9753-z |
| Pubmed ID | |
| Authors |
A. Nicholls |
| Abstract |
Computational chemistry is a largely empirical field that makes predictions with substantial uncertainty. And yet the use of standard statistical methods to quantify this uncertainty is often absent from published reports. This article covers the basics of confidence interval estimation for molecular modeling using classical statistics. Alternate approaches such as non-parametric statistics and bootstrapping are discussed. |
X Demographics
The data shown below were collected from the profiles of 19 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 | 6 | 32% |
| Sweden | 1 | 5% |
| Comoros | 1 | 5% |
| Australia | 1 | 5% |
| Netherlands | 1 | 5% |
| Chile | 1 | 5% |
| United Kingdom | 1 | 5% |
| Switzerland | 1 | 5% |
| Unknown | 6 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 63% |
| Scientists | 7 | 37% |
Mendeley readers
The data shown below were compiled from readership statistics for 236 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 2% |
| Portugal | 3 | 1% |
| Brazil | 2 | <1% |
| United Kingdom | 2 | <1% |
| Spain | 2 | <1% |
| Argentina | 1 | <1% |
| Sweden | 1 | <1% |
| Israel | 1 | <1% |
| Poland | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 218 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 64 | 27% |
| Researcher | 60 | 25% |
| Student > Master | 23 | 10% |
| Student > Bachelor | 16 | 7% |
| Other | 11 | 5% |
| Other | 33 | 14% |
| Unknown | 29 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 88 | 37% |
| Agricultural and Biological Sciences | 28 | 12% |
| Biochemistry, Genetics and Molecular Biology | 14 | 6% |
| Engineering | 13 | 6% |
| Physics and Astronomy | 10 | 4% |
| Other | 46 | 19% |
| Unknown | 37 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 41. 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 27 November 2023.
All research outputs
#1,001,156
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#5
of 949 outputs
Outputs of similar age
#9,507
of 242,246 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#1
of 15 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 99% 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 242,246 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 96% 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 has done particularly well, scoring higher than 93% of its contemporaries.