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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Scoria: a Python module for manipulating 3D molecular data
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2017
|
| DOI | 10.1186/s13321-017-0237-8 |
| Pubmed ID | |
| Authors |
Patrick Ropp, Aaron Friedman, Jacob D. Durrant |
| Abstract |
Third-party packages have transformed the Python programming language into a powerful computational-biology tool. Package installation is easy for experienced users, but novices sometimes struggle with dependencies and compilers. This presents a barrier that can hinder the otherwise broad adoption of new tools. We present Scoria, a Python package for manipulating three-dimensional molecular data. Unlike similar packages, Scoria requires no dependencies, compilation, or system-wide installation. One can incorporate the Scoria source code directly into their own programs. But Scoria is not designed to compete with other similar packages. Rather, it complements them. Our package leverages others (e.g. NumPy, SciPy), if present, to speed and extend its own functionality. To show its utility, we use Scoria to analyze a molecular dynamics trajectory. Our FootPrint script colors the atoms of one chain by the frequency of their contacts with a second chain. We are hopeful that Scoria will be a useful tool for the computational-biology community. A copy is available for download free of charge (Apache License 2.0) at http://durrantlab.com/scoria/ . Graphical abstract . |
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 | 2 | 11% |
| Spain | 1 | 5% |
| Canada | 1 | 5% |
| India | 1 | 5% |
| Dominican Republic | 1 | 5% |
| Brazil | 1 | 5% |
| United Kingdom | 1 | 5% |
| Israel | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 58% |
| Scientists | 7 | 37% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 21% |
| Student > Ph. D. Student | 6 | 18% |
| Student > Bachelor | 5 | 15% |
| Student > Doctoral Student | 3 | 9% |
| Professor | 3 | 9% |
| Other | 5 | 15% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 8 | 24% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Agricultural and Biological Sciences | 4 | 12% |
| Chemical Engineering | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
| Other | 5 | 15% |
| Unknown | 5 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 10 April 2020.
All research outputs
#1,299,206
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#64
of 934 outputs
Outputs of similar age
#26,013
of 323,600 outputs
Outputs of similar age from Journal of Cheminformatics
#3
of 11 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 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 particularly well, scoring higher than 93% 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 323,600 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 91% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.