Collaborative Research: OPUS: Permutational Biometry: Synthesizing the Analytics of Data Analysis in Ecology and Evolution

合作研究：OPUS：排列生物测定：综合生态学和进化中的数据分析

• 批准号: 2146220
• 负责人: Michael Collyer
• 金额: $ 11.17万
• 依托单位: Chatham College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146220&HistoricalAwards=false
• 关键词: Collaborative; Research; OPUS; Permutational; Biometry

In this project, the investigators will synthesize their work in statistical theory. The analysis of biological data has long been accomplished using standard (parametric) statistical methods. However, these tools lack solutions for many complex questions. As an alternative, resampling of data using computers provides a general procedure that can replace traditional methods in cases where they are insufficient. These new methods have the potential to provide a tool for biologists that is less restrictive than traditional tests. A full-length book will be produced and made freely available providing broad impacts. Web-based tutorials demonstrating all topics in the book will also be provided. Both products will leverage existing software the investigators have developed and distributed freely to the scientific community.By synthesizing 25 years of permutation-based analytics they have developed, the investigators will arrive at an entirely new philosophy for performing data analysis in the biological sciences. This new paradigm results from the union of their methods in four interrelated areas: 1) Resampling of Residuals in a Permutation Procedure (RRPP), 2) permutation-based effect sizes, 3) permutation-based pairwise comparisons, and 4) permutation-based evaluation of independent or correlated datasets. The result will be a biometric perspective that emphasizes both hypothesis testing and biological signal strength determination, where the generation of empirical sampling distributions enables both components simultaneously. The approach is both univariate and multivariate, and can be used to evaluate trends in independent, or correlated observations. When complete the synthesis will provide a comprehensive learning platform, that combines thorough explanation of permutational analytics, and integrates this emerging statistical perspective with hands-on tutorials that utilize existing software for implementation. This advance has the potential to transform how biologists conduct their research, as the RRPP synthetic paradigm provides analytical tools that are capable of addressing hypotheses that parametric procedures cannot.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在这个项目中，研究人员将在统计理论中综合他们的工作。长期以来，使用标准（参数）统计方法来完成生物数据的分析。但是，这些工具缺乏许多复杂问题的解决方案。作为替代方案，使用计算机对数据进行重新采样提供了一个一般过程，可以在不足的情况下替换传统方法。这些新方法有潜力为生物学家提供工具，而生物学家的限制性不如传统测试。一本全长的书将被制作并免费提供，从而提供广泛的影响。还将提供基于Web的基于Web的教程，展示本书中的所有主题。两种产品都将利用研究人员已开发并自由分发给科学界的现有软件。通过合成25年的基于置换的分析，研究人员将获得一种全新的哲学，用于在生物科学中进行数据分析。这种新的范式是由于它们在四个相互关联的领域的方法的结合而产生的：1）在置换程序中重新采样残差（RRPP），2）基于置换的效应大小，3）基于置换的成对比较，以及4）4）基于独立或相关数据集的置换评估。结果将是一种生物识别视角，强调假设检验和生物信号强度的确定，其中经验抽样分布的产生可以同时实现这两种成分。该方法既是单变量又是多变量，可用于评估独立或相关的观测值的趋势。完成合成后，将提供一个全面的学习平台，结合了对排列分析的彻底解释，并将这种新兴统计观点与动手教程相结合，该教程利用现有软件进行实施。这项进步有可能改变生物学家如何进行研究，因为RRPP合成范式提供了能够解决参数程序无法解决的假设的分析工具。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识绩效和更广泛影响的评估来通过评估来支持的。