Center for Complex Networks and Systems Research, School of Informatics & Computing, Indiana University, Bloomington, United States of America.
PLoS One. 2012;7(9):e43235. doi: 10.1371/journal.pone.0043235. Epub 2012 Sep 12.
The use of quantitative metrics to gauge the impact of scholarly publications, authors, and disciplines is predicated on the availability of reliable usage and annotation data. Citation and download counts are widely available from digital libraries. However, current annotation systems rely on proprietary labels, refer to journals but not articles or authors, and are manually curated. To address these limitations, we propose a social framework based on crowdsourced annotations of scholars, designed to keep up with the rapidly evolving disciplinary and interdisciplinary landscape. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This creates an incentive for users to provide disciplinary annotations of authors, which in turn can be used to compute disciplinary metrics. We first present the system architecture and several heuristics to deal with noisy bibliographic and annotation data. We report on data sharing and interactive visualization services enabled by Scholarometer. Usage statistics, illustrating the data collected and shared through the framework, suggest that the proposed crowdsourcing approach can be successful. Secondly, we illustrate how the disciplinary bibliometric indicators elicited by Scholarometer allow us to implement for the first time a universal impact measure proposed in the literature. Our evaluation suggests that this metric provides an effective means for comparing scholarly impact across disciplinary boundaries.
使用定量指标来衡量学术出版物、作者和学科的影响力，前提是要有可靠的使用和注释数据。数字图书馆广泛提供引文和下载计数。然而，当前的注释系统依赖于专有标签，仅针对期刊，而不针对文章或作者，并且需要手动维护。为了解决这些限制，我们提出了一个基于众包注释学者的社交框架，旨在跟上快速发展的学科和跨学科领域。我们描述了一个名为 Scholarometer 的系统，该系统通过计算基于引文的影响力指标为学者提供服务。这为用户提供了对作者进行学科注释的激励，从而可以用于计算学科指标。我们首先介绍系统架构和一些启发式方法，以处理有噪声的书目和注释数据。我们报告了 Scholarometer 支持的数据共享和交互式可视化服务。使用情况统计数据，说明了通过框架收集和共享的数据，表明所提出的众包方法可以取得成功。其次，我们说明了 Scholarometer 引出的学科计量指标如何使我们首次能够实现文献中提出的通用影响力衡量标准。我们的评估表明，该指标为跨学科边界比较学术影响力提供了一种有效手段。
