EAGER: Assessing Established and Alternative Citation, Attribution and Impact Metrics for Scientific Software through Data Mining and Direct Tracking Methods

软件是科学家利用计算能力的手段。许多最广泛使用的研究软件是由其他实践科学家创建的。科学对识别和奖励有用和有影响力的计算工具的生产具有重要的兴趣。评估科学意义和影响的传统指标，如引用，可能无法充分反映项目创建、传播和使用所产生的总体效用和收益。即使在传统的引用和资助框架内，对计算工具创造者的激励和奖励的基本问题仍然不清楚，准确评估使用、效用和生产力的问题还没有得到严格的审查。本研究的目标是改进当前科学软件影响的度量，评估替代度量，开发用于分析研究软件使用的直接度量的方法，并部署工具来帮助研究软件用户和开发人员更好地引用，衡量和评估软件使用，影响和生产力。在SBGrid软件用户联盟中，通过直接跟踪研究软件的使用情况来传播数据的社会技术系统和方法的发展将为程序作者和开发人员提供关于其软件的使用、影响和生产力的独特见解和信息。这种跟踪信息还将为用户提供一种自动的方法，以重新编码和记录他们的计算工作流程和协议，并有助于减轻编制计算方法和程序的全面书目信息的困难。除了改进未来评估科学软件使用指标的进展外，数据挖掘技术还将用于回顾性检查包含软件使用数据和元数据、出版物和引文信息以及研究成果的历史和当代数据库和数据存储库，以评估传统科学影响衡量标准的有效性和准确性。挖掘的数据也将被分析并用于评估新的和现有的替代指标，以衡量研究软件和计算工具的科学影响。

Software is the means by which scientists harness the power of computing. Much of the most widely used research software are those created by other practicing scientists. Science has a critical interest in identifying and rewarding the production of useful and impactful computing tools. Traditional metrics for assessing scientific significance and impact, like citation, may not adequately reflect the total utility and benefit resulting from a program's creation, dissemination and use. Basic questions of incentive and reward for computational tool creators, even within the traditional frameworks of citation and funding, remain unclear, and questions of accurately assessing use vs. utility vs. productivity have yet to be rigorously examined. The goal of this research is to improve current measures for impact of scientific software, evaluate alternative metrics, develop methods for analyzing the direct measurement of research software usage, and deploy tools to aid research software users and developers to better cite, gauge and evaluate software use, impact and productivity.The development of sociotechnical systems and methods for disseminating data gathered through the direct tracking of research software usage within the SBGrid consortium of software users will provide unique insight and information to program authors and developers regarding the use, impact and productivity of their software. This tracking information will also provide users with an automated means of recoding and documenting their computational workflows and protocols, and help relieve the difficulties in compiling comprehensive bibliographic information of computational methodologies and procedures. In addition to improving future progress in assessing the metrics of scientific software use, data mining techniques will be employed to retrospectively examine historical and contemporary databases and data repositories containing software usage data and metadata, publication and citation information, and research output to assess the efficacy and accuracy of traditional measures of scientific impact. The mined data will also be analyzed and used to evaluate new and existing alternative metrics for measuring the scientific impact of research software and computational tools.