NRT-IGE: Reproducibility and Rigor in Quantitative Biology: A Hands-on Approach

目前的生物学研究正在产生越来越大和复杂的数据集。例如，这些数据可以表示：DNA序列、大脑图像或生态系统中的物种数量。在每种情况下，解锁这些大数据集中的信息都需要复杂的数学和计算方法。生物科学研究生的标准课程在数据泛滥之前就已经设计好了。因此，今天的研究生没有得到足够的培训，为他们未来的职业生涯。与此同时，人们越来越担心科学家有时无法复制已发表的研究结果。这种无能往往是由于糟糕的数据分析策略造成的。美国生物研究使命的未来成功取决于训练学生使用既严格又可重复的数据分析方法。芝加哥大学研究生教育创新（IGE）项目的国家科学基金会研究培训（NRT）奖旨在通过开发一种新的有效方法来培养早期研究生对生物数据进行定量分析来满足这一需求。该计划的总体目标是教学生批判性地评估科学文献中的定量分析方法，并获得良好的编程习惯，支持他们自己研究的可重复性和严谨性。一个跨学科的定量生物学家团队将指导和领导该计划，让学生接触到可以在未来工作中为他们提供建议的教师。培训计划开始于为期一周的密集住宅靴子营地，汇集了生物学不同子领域的学生，以促进团队合作，并为跨学科研究做好准备。靴子训练营包括计算机编程、统计学和现代生物学建模方面的入门教程，以及大型数据集统计方法方面的更高级教程，以及组织和共享代码和数据方面的实践课程。靴子训练营以一系列的研讨会结束，在研讨会上，学生们将他们所学的知识应用于跨越广泛领域的真实的生物数据。随后的校园课程建立在这些概念的基础上，并将严谨性和可重复性的培训与负责任的研究概念相结合。我们假设，该计划将产生谁是为未来的科学劳动力做好准备的学员。我们将通过测验、调查和有针对性的访谈来评估这种干预的影响。讲习班中使用的所有教材和数据集将在网上共享，以便任何大学都可以在自己的校园内实施类似的培训模块。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆的，新的，潜在的变革性模型，用于STEM研究生教育培训。研究生教育创新轨道专门致力于试点，测试和评估新颖，创新和潜在的变革性研究生教育方法。

Current research in biology is producing increasingly large and complex sets of data. These data could represent, for example: DNA sequences, images of the brain, or number of species in an ecosystem. In each case, unlocking the information within these big data sets requires sophisticated mathematical and computational approaches. The standard curriculum for graduate students in biological sciences was designed well before this data deluge. As a consequence, today's graduate students are not being adequately trained for their future careers. At the same time, there are growing concerns that scientists are sometimes unable to reproduce published findings. This inability often results from poor data analysis strategies. The future success of the US biological research mission hinges on training students to use data analysis approaches that are both rigorous and reproducible. This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of Chicago seeks to meet this need by developing a new and effective approach to the training of early stage graduate students in the quantitative analysis of biological data.The overarching goal of this program is to teach students to critically evaluate quantitative analysis methods in the scientific literature, and to acquire good programming habits that support reproducibility and rigor in their own research. An interdisciplinary team of quantitative biologists will direct and lead the program, exposing students to the faculty that can advise them in future work. The training program begins with an intensive residential week-long boot camp that brings together students across diverse sub-fields of biology to promote teamwork and prepare them for interdisciplinary research. The boot camp includes introductory tutorials in computer programming, statistics, and modeling in modern biology, as well as more advanced tutorials in statistical approaches to large data sets and practical lessons in organizing and sharing code and data. The boot camp is capped off with a series of workshops in which students apply what they have learned to real biological data spanning a wide range of fields. A subsequent on-campus course builds on and reviews these concepts, and integrates training in rigor and reproducibility with concepts of responsible research. We hypothesize that this program will produce trainees who are well-prepared for the future scientific workforce. We will evaluate the impact of this intervention through quizzes, surveys, and targeted interviews. All teaching materials and data sets used in the workshops will be shared online so that any university can implement a similar training module on their own campus. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative models for STEM graduate education training. The Innovations in Graduate Education Track is dedicated solely to piloting, testing, and evaluating novel, innovative, and potentially transformative approaches to graduate education.