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).

该研讨会将为研究生和早期职业研究人员提供急需的培训机会，帮助他们掌握分析进化生物学大数据的最新前沿方法。研讨会参与者将从不同的专业水平中招募，并专门针对代表性不足的少数群体。参与者将接受背后理论以及这些工具的应用培训。课程将由开发这些计算工具的专家教授授课。研究社区和公众将通过 GitHub 长期访问研讨会材料，例如幻灯片、教程、源代码和示例数据集。系统发育和比较方法正在快速发展，个别研究人员常常发现很难跟上最新发展。本次研讨会将提供以下培训课程：a) 了解比较方法（以 bayou R 库和 Arbor 平台为特色），b) 生物地理学培训（以 BioGeoBears 和 RevBayes 为特色），c) 系统发育地理学中的模型选择（通过 Phrapl）， d) 使用 TreeScaper 可视化和探索树集，以及 e) 系统发育库（通过 Dendropy）。