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Percentage-based Author Contribution Index: a universal measure of author contribution to scientific articles

Overview of attention for article published in Research Integrity and Peer Review, November 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#32 of 116)
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

news
1 news outlet
twitter
66 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
58 Mendeley
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Title
Percentage-based Author Contribution Index: a universal measure of author contribution to scientific articles
Published in
Research Integrity and Peer Review, November 2017
DOI 10.1186/s41073-017-0042-y
Pubmed ID
Authors

Stéphane Boyer, Takayoshi Ikeda, Marie-Caroline Lefort, Jagoba Malumbres-Olarte, Jason M. Schmidt

Abstract

Deciphering the amount of work provided by different co-authors of a scientific paper has been a recurrent problem in science. Despite the myriad of metrics available, the scientific community still largely relies on the position in the list of authors to evaluate contributions, a metric that attributes subjective and unfounded credit to co-authors. We propose an easy to apply, universally comparable and fair metric to measure and report co-authors contribution in the scientific literature. The proposed Author Contribution Index (ACI) is based on contribution percentages provided by the authors, preferably at the time of submission. Researchers can use ACI to compare the contributions of different authors, describe the contribution profile of a particular researcher or analyse how contribution changes through time. We provide such an analysis based on contribution percentages provided by 97 scientists from the field of ecology who voluntarily responded to an online anonymous survey. ACI is simple to understand and to implement because it is based solely on percentage contributions and the number of co-authors. It provides a continuous score that reflects the contribution of one author as compared to the average contribution of all other authors. For example, ACI(i) = 3, means that author i contributed three times more than what the other authors contributed on average. Our analysis comprised 836 papers published in 2014-2016 and revealed patterns of ACI values that relate to career advancement. There are many examples of author contribution indices that have been proposed but none has really been adopted by scientific journals. Many of the proposed solutions are either too complicated, not accurate enough or not comparable across articles, authors and disciplines. The author contribution index presented here addresses these three major issues and has the potential to contribute to more transparency in the science literature. If adopted by scientific journals, it could provide job seekers, recruiters and evaluating bodies with a tool to gather information that is essential to them and cannot be easily and accurately obtained otherwise. We also suggest that scientists use the index regardless of whether it is implemented by journals or not.

Twitter Demographics

The data shown below were collected from the profiles of 66 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 24%
Professor 7 12%
Other 6 10%
Student > Master 6 10%
Student > Doctoral Student 5 9%
Other 13 22%
Unknown 7 12%
Readers by discipline Count As %
Medicine and Dentistry 9 16%
Social Sciences 8 14%
Arts and Humanities 4 7%
Environmental Science 4 7%
Chemistry 3 5%
Other 16 28%
Unknown 14 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 49. 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 20 August 2021.
All research outputs
#728,183
of 22,883,326 outputs
Outputs from Research Integrity and Peer Review
#32
of 116 outputs
Outputs of similar age
#17,146
of 328,282 outputs
Outputs of similar age from Research Integrity and Peer Review
#2
of 4 outputs
Altmetric has tracked 22,883,326 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 116 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 67.9. This one has gotten more attention than average, scoring higher than 72% 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 328,282 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 94% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.