Biomedical Research Training MARC

摘要 优化劳动力的量化培训对于保持国家的科学性至关重要 在促进将改善人类健康的生物医学研究方面的竞争力和领导力。这是有据可查的 统计方法的滥用在基础生物医学科学研究中很常见，甚至在发表在 高影响力期刊(1-3)。这些问题主要源于对统计数据的有限理解，这表明 科学家需要更好的统计培训。我们的MARC学者正在完成不同大学的学士学位专业 和/或同一学院内的不同系。虽然NMSU的许多部门目前提供或要求提供统计数据 培训，这些课程不太可能为基础科学家提供适当的统计准备，因为 不同科学学科之间研究设计的差异。为我们的学生提供围绕样品设计的课程 生物医学研究中经常遇到的大小、研究设计和数据类型是理想的。此应用程序 要求提供资金，丰富所有MARC学者的科学培训，增加他们的概念理解和 分析数据、评估文献、提高统计报告和分析质量所需的技能 各自的研究领域，并培养与科学界内外同行的良好沟通能力 社区。增刊的目的是：1)加强学生对数量化知识的理解和运用。 基于分析和解决问题的方法；以及2)通过以下方式将专业发展课程正式化 实施有效的实践，旨在将学生融入研究社区，并帮助他们 这种联系不仅将促进科学本身的发展，也将促进他们在生物医学研究方面的事业。学生们将进行一次 夏季、秋季和春季学期的动手数据分析工作坊，学生将在其中学习执行基本操作 使用“真实数据”进行描述性和推断性分析。将选择数据集来提供像这些学生一样的挑战 在使用他们自己的数据时可能会遇到(例如，数据输入错误、缺失值、离群值)。他们的获得者 知识将转化为他们自己的研究报告和同行评议文献的批判性评价 在整个学年定期举行的会议期间。这些活动将伴随着核心指导 在研究生院取得学业成功所必需的技能。评估和评价方案做法和成果 融入了方案要素的设计和不断改进。建议的补充活动包括 通过开设课程实现制度化。

ABSTRACT Optimizing the quantitative training of our workforce workforce is essential for maintaining the nation’s scientific competitiveness and leadership in promoting biomedical research that will improve human health. It is well documented that the misuse of statistical methods is common in basic biomedical science research, even among papers published in high impact journals (1-3). These problems stem mainly from a limited understanding of statistics, suggesting that scientists need better statistical training. Our MARC Scholars are completing baccalaureate majors in different colleges and/or different departments within one college. While many departments at NMSU currently offer or require statistics training, these courses are unlikely to provide appropriate statistical preparation for basic scientists given the obvious differences in study designs between science disciplines. Providing our students with curriculum designed around sample sizes, study designs, and types of data that are frequently encountered in biomedical research is ideal. This application requests funds to enrich the scientific training of all MARC Scholars by increasing their conceptual understanding and skills needed to analyze data, assess the literature, improve the quality of statistical reporting and analysis in their respective fields of research, and develop strong communication skills with peers within and outside the scientific community. The Supplement Aims are to: 1) strengthen the students’ understanding and application of a quantitative- based approach when analyzing and solving problems; and 2) formalize the professional development curriculum by implementing effective practices aimed at integrating the students into the research community and helping them make connections that will advance not only the science itself, but their careers in biomedical research. Students will conduct a hands-on data analysis workshop in Summer, Fall and Spring semesters in which students will learn to perform basic descriptive and inferential analyses using “real data.” Data sets will be chosen to provide challenges like those students might encounter in working with their own data (e.g., data entry errors, missing values, outliers). Their acquired knowledge will be translated into their own research presentations and critical evaluations of peer reviewed literature during regularly held meetings throughout the academic year. These activities will be accompanied by core mentorship skills essential for academic success in graduate school. Assessment and evaluation of program practices and outcomes are integrated into the design and continual refinement of programmatic elements. Proposed supplemental activities are institutionalized through course offerings.