| Title |
From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects
|
|---|---|
| Published in |
Empirical Software Engineering, December 2015
|
| DOI | 10.1007/s10664-015-9406-4 |
| Authors |
Ingo Scholtes, Pavlin Mavrodiev, Frank Schweitzer |
X Demographics
The data shown below were collected from the profiles of 11 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 | 2 | 18% |
| Netherlands | 2 | 18% |
| United States | 1 | 9% |
| Canada | 1 | 9% |
| Germany | 1 | 9% |
| Unknown | 4 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 64% |
| Members of the public | 4 | 36% |
Mendeley readers
The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| New Zealand | 1 | <1% |
| Netherlands | 1 | <1% |
| Unknown | 105 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 23% |
| Student > Master | 21 | 19% |
| Student > Doctoral Student | 13 | 12% |
| Researcher | 10 | 9% |
| Professor | 5 | 5% |
| Other | 21 | 19% |
| Unknown | 14 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 42 | 39% |
| Business, Management and Accounting | 11 | 10% |
| Engineering | 10 | 9% |
| Social Sciences | 9 | 8% |
| Environmental Science | 2 | 2% |
| Other | 9 | 8% |
| Unknown | 26 | 24% |
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 13 October 2019.
All research outputs
#3,631,499
of 23,342,092 outputs
Outputs from Empirical Software Engineering
#75
of 713 outputs
Outputs of similar age
#61,237
of 391,343 outputs
Outputs of similar age from Empirical Software Engineering
#1
of 13 outputs
Altmetric has tracked 23,342,092 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 713 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 89% 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 391,343 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 84% of its contemporaries.
We're also able to compare this research output to 13 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 92% of its contemporaries.