Training in Innovative Phylogenetics and Comparative Methods at the Society of Systematic Biologists Meeting, January, 2017, Baton Rouge, Louisiana

系统生物学家协会会议上的创新系统发育学和比较方法培训，2017 年 1 月，路易斯安那州巴吞鲁日

• 批准号: 1723656
• 负责人: Jeremy Brown
• 金额: $ 1.88万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723656&HistoricalAwards=false
• 关键词: Training; Innovative; Phylogenetics; Comparative; Methods

This workshop will provide a much-needed training opportunity for graduate students and early-career researchers in the latest cutting edge approaches for analyzing big data in evolutionary biology. Workshop participants will be recruited from diverse levels of expertise with a specific effort to target underrepresented minorities. Participants will be trained in the theory behind, and the application of these tools. Sessions will be taught by expert faculty, who developed these computational tools. Long-term access to workshop materials, such as slides, tutorials, source codes, and exemplar datasets will be made available to the research community and the public via GitHub. Phylogenetic and comparative methods are advancing at a rapid pace, and individual researchers often find it difficult to keep abreast of the latest developments. This workshop will offer the following training sessions: a) understanding comparative methods (featuring the bayou R library and the Arbor platform), b) training in biogeography (featuring BioGeoBears, and RevBayes), c) model selection in phylogeography (via Phrapl), d) visualization and exploration of tree sets using TreeScaper, and e) phylogenetic libraries (via Dendropy).

本次研讨会将为研究生和早期职业研究人员提供一个急需的培训机会，让他们了解进化生物学中分析大数据的最新前沿方法。讲习班的参加者将从不同水平的专门知识中征聘，并特别努力以代表性不足的少数民族为对象。参与者将接受理论培训，以及这些工具的应用。课程将由开发这些计算工具的专家教授。研讨会材料的长期访问，如幻灯片、教程、源代码和范例数据集，将通过GitHub提供给研究社区和公众。系统发育和比较方法正在迅速发展，个别研究人员经常发现很难跟上最新的发展。本次研讨会将提供以下培训课程：a)理解比较方法（以bayou R库和Arbor平台为特色），b)生物地理学培训（以biogeobars和RevBayes为特色），c)系统地理学模型选择（通过Phrapl）， d)使用TreeScaper可视化和探索树集，e)系统发育文库（通过树突）。