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

当前的生物学研究正在产生越来越大、越来越复杂的数据集。这些数据可以代表，例如：DNA序列，大脑图像，或生态系统中的物种数量。在每种情况下，解开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早在数据泛滥之前就设计好了。因此，今天的研究生没有为他们未来的职业生涯接受充分的培训。与此同时，人们越来越担心科学家有时无法重现已发表的研究结果。这种无能往往源于糟糕的数据分析策略。美国生物学研究任务的未来成功取决于训练学生使用严格且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训早期研究生，对生物数据进行定量分析，从而满足这一需求。本课程的首要目标是教会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，以支持他们自己的研究的可重复性和严谨性。一个跨学科的数量生物学家团队将指导和领导这个项目，让学生接触到可以在未来工作中提供建议的教师。训练计划从为期一周的密集住宿训练营开始，将来自不同生物学子领域的学生聚集在一起，促进团队合作，为跨学科研究做好准备。新兵训练营包括计算机编程、统计学和现代生物学建模的入门教程，以及大型数据集统计方法的更高级教程，以及组织和共享代码和数据的实践课程。训练营的最后是一系列的研讨会，在这些研讨会上，学生们将他们所学到的知识应用到跨越广泛领域的真实生物数据中。随后的校园课程建立并回顾了这些概念，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。我们假设这个项目将培养为未来科学劳动力做好充分准备的学员。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。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、新的、具有潜在变革性的STEM研究生教育培训模式。研究生教育创新轨道专门用于试点，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.