| Title |
An introduction to scripting in Ruby for biologists
|
|---|---|
| Published in |
BMC Bioinformatics, July 2009
|
| DOI | 10.1186/1471-2105-10-221 |
| Pubmed ID | |
| Authors |
Jan Aerts, Andy Law |
| Abstract |
The Ruby programming language has a lot to offer to any scientist with electronic data to process. Not only is the initial learning curve very shallow, but its reflection and meta-programming capabilities allow for the rapid creation of relatively complex applications while still keeping the code short and readable. This paper provides a gentle introduction to this scripting language for researchers without formal informatics training such as many wet-lab scientists. We hope this will provide such researchers an idea of how powerful a tool Ruby can be for their data management tasks and encourage them to learn more about it. |
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 % |
|---|---|---|
| Brazil | 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 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 8% |
| Japan | 4 | 3% |
| United Kingdom | 3 | 2% |
| Germany | 2 | 2% |
| Russia | 2 | 2% |
| Belgium | 2 | 2% |
| Netherlands | 2 | 2% |
| Sweden | 2 | 2% |
| India | 2 | 2% |
| Other | 13 | 10% |
| Unknown | 87 | 67% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 52 | 40% |
| Student > Ph. D. Student | 19 | 15% |
| Other | 10 | 8% |
| Student > Master | 9 | 7% |
| Student > Bachelor | 8 | 6% |
| Other | 24 | 18% |
| Unknown | 8 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 73 | 56% |
| Biochemistry, Genetics and Molecular Biology | 13 | 10% |
| Computer Science | 11 | 8% |
| Medicine and Dentistry | 6 | 5% |
| Environmental Science | 3 | 2% |
| Other | 14 | 11% |
| Unknown | 10 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 16 January 2015.
All research outputs
#3,599,317
of 22,651,245 outputs
Outputs from BMC Bioinformatics
#1,333
of 7,236 outputs
Outputs of similar age
#15,530
of 109,814 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 30 outputs
Altmetric has tracked 22,651,245 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 81% 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 109,814 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.