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，研究人员将综合他们在统计理论方面的工作。生物数据的分析长期以来一直使用标准（参数）统计方法完成。然而，这些工具对许多复杂问题缺乏解决方案。作为一种替代办法，使用计算机重新存储数据提供了一种通用程序，在传统方法不足以解决问题时可以取代传统方法。这些新方法有可能为生物学家提供一种比传统测试限制更少的工具。将编写一本具有广泛影响的完整书籍，并免费提供。还将提供基于网络的教程，演示书中的所有主题。这两款产品都将利用研究人员开发并免费分发给科学界的现有软件。通过综合他们开发的25年基于排列的分析，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。这种新的范式源于他们在四个相互关联的领域中的方法的联合：1）置换过程中的残差恢复（RRPP），2）基于置换的效应大小，3）基于置换的成对比较，以及4）独立或相关数据集的基于置换的评估。其结果将是一个生物特征的角度，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生成使这两个组件同时。该方法是单变量和多变量的，可用于评估独立或相关观测的趋势。完成后，综合将提供一个全面的学习平台，结合了对置换分析的透彻解释，并将这种新兴的统计观点与利用现有软件进行实施的实践教程相结合。这一进展有可能改变生物学家如何进行他们的研究，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的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。