Collaborative research: ABI Development: Making Advanced Statistical Tools Accessible for Quantitative Research Synthesis and Discovery in Ecology and Evolutionary Biology

合作研究：ABI 开发：使先进的统计工具可用于生态学和进化生物学的定量研究综合和发现

• 批准号: 1262442
• 负责人: Byron Wallace
• 金额: $ 50.27万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262442&HistoricalAwards=false
• 关键词: Collaborative; research; ABI; Development; Making

An award is made to build an easy-to-use program for carrying out and teaching meta-analysis in in the fields of ecology and evolution (E&E), by adapting and developing an existing medical meta-analysis program for the unique needs of the scientific community in these fields. Meta-analysis is the quantitative synthesis of independent studies. It has become an important approach for summarizing research results and generalizing from their findings in E&E, as it has in medicine and the social sciences. There is a compelling need for new statistical software for meta-analysis in E&E employing current, cutting edge methodology. Making these statistical tools widely available is highly likely to propel new scientific advances and insights, as did the first generation of meta-analysis software in E&E. This new software, called OpenMeta for Ecology and Evolution (OpenMEE), will be open-source and cross-platform program, will facilitate implementation of statistical methods appropriate for meta-analysis of ecological and evolutionary data, and will include a user interface specifically designed for the unique challenges and conventions of research synthesis in these fields. Then intuitive graphical user interface (GUI) will seamlessly integrate with analytic routines written in the popular open-source statistical programming language R. Novice users will thus be able to take advantage of such routines to carry out sophisticated meta-analyses without needing to know advanced programming. Furthermore, OpenMEE is designed so that those users who are competent programmers can incorporate new statistical developments in a straightforward manner, allowing OpenMEE to be developed and updated indefinitely by the user community. OpenMEE will be introduced with cutting edge statistical developments for meta-analysis in E&E, including complex meta-regression models and phylogenetic analyses. Putting much more powerful tools for quantitative research synthesis into the hands of scientists in E&E will have dramatic effects on research progress in these fields.The scientists working on this project have a solid record of successfully mentoring high school students, undergraduates, graduate students, and postdoctoral fellows, including those belonging to underrepresented groups, and will continue to work actively to recruit and train people at all of these levels. OpenMEE will be disseminated in workshops and short courses, as well as electronically. In recruiting participants for these courses, the researchers will work to identify and attract graduate students and postdoctoral researchers from underrepresented groups as well as others; they will be able to structure the fees for the courses so as to be able to offer travel fellowships for students lacking support to attend the courses. Finally, underrepresented minority students and others will be recruited for research experience through the NSF‐funded Center for Science and Mathematics Education at Stony Brook University, and by recruiting from the rich resource of the high underrepresented student population at University of South Florida. More information about this project may be found at: http://www.cebm.brown.edu/OpenMEE.

通过适应和制定现有的医学荟萃分析计划来满足这些领域的科学界的独特需求，以在生态和进化领域（E＆E）进行易于使用的计划，以在生态与进化领域（E＆E）进行荟萃分析。荟萃分析是独立研究的定量综合。它已成为总结研究结果并从E＆E中概括的重要方法，就像它在医学和社会科学方面一样。在E＆E中，采用电流，最先进的方法论，需要新的统计软件来进行荟萃分析。像E＆E中第一代荟萃分析软件一样，使这些统计工具广泛可用，可以推动新的科学进步和见解。这个称为生态和进化的OpenMeta（OpenMEE）的新软件将是开源和跨平台程序，将有助于实施适合于生态和进化数据的荟萃分析的统计方法，并将包括专门针对这些领域研究中独特的挑战和典范的用户界面。然后，直观的图形用户界面（GUI）将与流行的开源统计编程语言编写的分析例程无缝集成。因此，新手用户将能够利用此类例程来执行复杂的荟萃分析，而无需了解高级编程。此外，OpenMee的设计是使那些具有合格程序员的用户可以直接地合并新的统计开发，从而允许用户社区无限期地开发和更新OpenMee。 OpenMEE将通过E＆E中荟萃分析的最先进统计发展引入，包括复杂的元回归模型和系统发育分析。将更强大的工具放置在E＆E中的科学家手中，将对这些领域的研究进度产生巨大影响。从事该项目的科学家拥有成功指导高中生，本科生，研究生，研究生和博士学位后的人的稳定记录，包括那些属于未成年人群的人，并将继续从事培训和培训这些阶层，并将继续招募这些人，并培训这些人的招募人群，这些人都在招募这些人，并将培训这些人，这些人都在招募这些人。 OpenMee将在研讨会和短期课程以及电子方式中传播。在招募这些课程的参与者时，研究人员将努力识别和吸引来自代表性不足的团体以及其他人的研究生和博士后研究人员；他们将能够为课程的费用构建费用，从而为缺乏支持参加课程的学生提供旅行奖学金。最后，将通过斯托尼·布鲁克大学（Stony Brook University）的NSF及其资助的科学与数学教育中心（NSF）招募人数不足的少数族裔学生和其他人，并从南佛罗里达大学的高度代表性学生人数不足的学生人数中招募。有关该项目的更多信息，请访问：http：//www.cebm.brown.edu/openmee。