TLS: Early prediction of the impact of research through large-scale analysis and modeling citation dynamics

TLS：通过大规模分析和引用动态建模来早期预测研究的影响

• 批准号: 0830388
• 负责人: Luis Amaral
• 金额: $ 40万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830388&HistoricalAwards=false
• 关键词: TLS; Early; prediction; impact; research

The number of scientific journals and the number of papers published in those journals is increasing at a fast pace: currently, there are roughly 9,000 scientific journals, a twenty-fold increase from the 400 scientific journals available in 1955. The size and growth of the research literature places a tremendous burden on decision-making agents, such as funding agencies, university administrators, and reviewers, who have to evaluate the quality of research of individuals and institutions in a fast and efficient manner. In addition, the evaluation of individuals and institutions relies heavily on the assessment of the ultimate impact of published research, typically measured as the number of publications or the number of citations. Despite the oversimplification of using just a few numbers to quantify the scientific merit of a body of research, the entire science and technology community is relying more and more on citation-based statistics as a tool for evaluating the research performance of individuals and institutions and the development of tools that facilitate the evaluation task is crucial in order to ensure the highest quality standard for funded research.A major challenge is that stakeholders often want an estimate of the impact of a paper long before citations start to accumulate. This project aims precisely at facilitating such a task by developing tools to predict, soon after publication, the ultimate impact of research. This project uses longitudinal data available for papers published since 1955, drawing upon the concept that one can infer patterns of citation accumulation from large-scale analysis of historical data. Broader Impact: The outcome of this research is of relevance to decision makers that are called on to evaluate the productivity of researchers and institutions, as well as the impact of their work. The goal of this project is to develop transparent statistically-sound methods in order to enable institutions and funding agencies to make better informed decisions based on an objective assessment of the impact of published research.

科学期刊的数量和在这些期刊上发表的论文数量正在快速增长：目前，大约有9，000种科学期刊，比1955年的400种科学期刊增加了20倍。研究文献的规模和增长给决策者带来了巨大的负担，例如资助机构，大学管理人员和评审人员，他们必须以快速有效的方式评估个人和机构的研究质量。此外，对个人和机构的评价在很大程度上依赖于对已发表研究的最终影响的评估，通常以出版物的数量或引用次数来衡量。尽管用几个数字来量化一个研究机构的科学价值过于简单，整个科学技术界越来越依赖于引用，将基于统计的数据作为评估个人和机构研究绩效的工具，并开发有助于评估任务的工具，对于确保受资助研究的最高质量标准至关重要。利益相关者通常希望在引用开始积累之前很久就对论文的影响力进行估计。该项目的目的正是通过开发工具来促进这一任务，以便在出版后不久预测研究的最终影响。该项目使用了自1955年以来发表的论文的纵向数据，利用了人们可以从历史数据的大规模分析中推断引文积累模式的概念。更广泛的影响：这项研究的结果与决策者有关，他们被要求评估研究人员和机构的生产力及其工作的影响。该项目的目标是开发透明的、可靠的方法，以便使机构和资助机构能够根据对已发表研究的影响的客观评估作出更明智的决定。