| Title |
On the pragmatic design of literature studies in software engineering: an experience-based guideline
|
|---|---|
| Published in |
Empirical Software Engineering, January 2017
|
| DOI | 10.1007/s10664-016-9492-y |
| Authors |
Marco Kuhrmann, Daniel Méndez Fernández, Maya Daneva |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 3 | 17% |
| Brazil | 2 | 11% |
| United States | 2 | 11% |
| India | 1 | 6% |
| Israel | 1 | 6% |
| Belgium | 1 | 6% |
| Netherlands | 1 | 6% |
| New Zealand | 1 | 6% |
| Luxembourg | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 4 | 22% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 12 | 67% |
| Members of the public | 5 | 28% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | <1% |
| Unknown | 158 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 24 | 15% |
| Student > Ph. D. Student | 23 | 14% |
| Student > Bachelor | 15 | 9% |
| Researcher | 14 | 9% |
| Professor | 14 | 9% |
| Other | 34 | 21% |
| Unknown | 35 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 87 | 55% |
| Engineering | 10 | 6% |
| Business, Management and Accounting | 6 | 4% |
| Social Sciences | 3 | 2% |
| Agricultural and Biological Sciences | 2 | 1% |
| Other | 9 | 6% |
| Unknown | 42 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 12 December 2017.
All research outputs
#3,020,282
of 24,699,496 outputs
Outputs from Empirical Software Engineering
#60
of 755 outputs
Outputs of similar age
#58,868
of 430,316 outputs
Outputs of similar age from Empirical Software Engineering
#5
of 26 outputs
Altmetric has tracked 24,699,496 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 755 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 92% 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 430,316 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 86% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.