Science is becoming increasingly more interdisciplinary, giving rise to more
diversity in the areas of expertise within research labs and groups. This also
have brought changes to the role researchers in scientific works. As a
consequence, multi-authored scientific papers have now became a norm for high
quality research. Unfortunately, such a phenomenon induces bias to existing
metrics employed to evaluate the productivity and success of researchers. While
some metrics were adapted to account for the rank of authors in a paper, many
journals are now requiring a description of the specific roles of each author
in a publication. Surprisingly, the investigation of the relationship between
the rank of authors and their contributions has been limited to a few studies.
By analyzing such kind of data, here we show, quantitatively, that the
regularity in the authorship contributions decreases with the number of authors
in a paper. Furthermore, we found that the rank of authors and their roles in
papers follows three general patterns according to the nature of their
contributions, such as writing, data analysis, and the conduction of
experiments. This was accomplished by collecting and analyzing the data
retrieved from PLoS ONE and by devising an entropy-based measurement to
quantify the effective number of authors in a paper according to their
contributions. The analysis of such patterns confirms that some aspects of the
author ranking are in accordance with the expected convention, such as the fact
that the first and last authors are more likely to contribute more in a
scientific work. Conversely, such analysis also revealed that authors in the
intermediary positions of the rank contribute more in certain specific roles,
such as the task of collecting data. This indicates that the an unbiased
evaluation of researchers must take into account the distinct types of
scientific contributions.

通过分析数据表明，多作者论文的作者贡献随作者排名的变化而变化。而且，根据作者作用的性质，作者排名和角色分为三种类型。

科学论文作者贡献的模式

Patterns of authors contribution in scientific manuscripts

Many studies on coauthorship networks focus on network topology and network
statistical mechanics. This article takes a different approach by studying
micro-level network properties, with the aim to apply centrality measures to
impact analysis. Using coauthorship data from 16 journals in the field of
library and information science (LIS) with a time span of twenty years
(1988-2007), we construct an evolving coauthorship network and calculate four
centrality measures (closeness, betweenness, degree and PageRank) for authors
in this network. We find out that the four centrality measures are
significantly correlated with citation counts. We also discuss the usability of
centrality measures in author ranking, and suggest that centrality measures can
be useful indicators for impact analysis.

本文研究共同作者网络的微观网络特性，并采用中心性度量对影响力进行分析，发现 4 种中心性度量与引用次数显著相关，证明中心性度量可以是衡量影响力的有用指标。