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

EAGER：通过数据挖掘和直接跟踪方法评估科学软件的既定和替代引用、归因和影响力指标

• 批准号: 1448069
• 负责人: Piotr Sliz
• 金额: $ 30万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1448069&HistoricalAwards=false
• 关键词: EAGER; Assessing; Established; Alternative; Citation

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.

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。科学界对识别和奖励有用和有影响力的计算工具的生产非常感兴趣。评估科学意义和影响的传统指标，如引用，可能无法充分反映程序的创建，传播和使用所产生的总效用和利益。对计算工具创造者的激励和奖励的基本问题，即使在传统的引用和资助框架内，仍然不清楚，准确评估使用与效用与生产力的问题还有待严格审查。本研究的目标是改进当前对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，并部署工具以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，发展社会技术系统和方法，传播通过直接跟踪研究收集的数据，SBGrid软件用户联盟中的软件使用将为程序作者和开发人员提供有关其软件的使用、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化的手段，用于重新编码和记录其计算工作流程和协议，并帮助减轻在汇编计算方法和程序的全面书目信息方面的困难。除了改进未来在评估科学软件使用指标方面的进展外，还将采用数据挖掘技术来回顾性地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产出的历史和当代数据库和数据储存库，以评估传统科学影响措施的有效性和准确性。挖掘的数据还将被分析和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。