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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Development of a domain-specific genetic language to design Chlamydomonas reinhardtii expression vectors
|
|---|---|
| Published in |
Bioinformatics, November 2013
|
| DOI | 10.1093/bioinformatics/btt646 |
| Pubmed ID | |
| Authors |
Mandy L. Wilson, Sakiko Okumoto, Laura Adam, Jean Peccoud |
| Abstract |
Expression vectors used in different biotechnology applications are designed with domain-specific rules. For instance, promoters, origins of replication or homologous recombination sites are host-specific. Similarly, chromosomal integration or viral delivery of an expression cassette imposes specific structural constraints. As de novo gene synthesis and synthetic biology methods permeate many biotechnology specialties, the design of application-specific expression vectors becomes the new norm. In this context, it is desirable to formalize vector design strategies applicable in different domains. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 3 | 50% |
| Norway | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 4% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Denmark | 1 | 2% |
| Unknown | 40 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 36% |
| Researcher | 12 | 27% |
| Professor > Associate Professor | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 53% |
| Biochemistry, Genetics and Molecular Biology | 8 | 18% |
| Computer Science | 5 | 11% |
| Chemical Engineering | 1 | 2% |
| Physics and Astronomy | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 3 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 97. 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 October 2023.
All research outputs
#435,563
of 25,374,917 outputs
Outputs from Bioinformatics
#45
of 12,809 outputs
Outputs of similar age
#3,498
of 229,118 outputs
Outputs of similar age from Bioinformatics
#2
of 184 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. 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 229,118 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 98% of its contemporaries.
We're also able to compare this research output to 184 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 98% of its contemporaries.