A massive study of data science to address the scientific reproducibility crisis

大规模数据科学研究以解决科学再现性危机

• 批准号: 9100338
• 负责人: Jeffrey T. Leek
• 金额: $ 36.45万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9100338
• 关键词: Acute; Address; Affect; Area; Behavior; Characteristics; Communication; Computer software; Congresses; Consensus; Course Content; Data; Data Analyses; Data Analytics; Data Collection; Data Science; Discipline; Disclosure; Drops; Education; Enrollment; Galaxy; Goals; Growth; Health; Heart; Knowledge; Lead; Measures; Medical; Methodology; Methods; Modeling; Price; Process; Protocols documentation; Publications; Randomized; Reproducibility; Research; Research Infrastructure; Research Personnel; Series; Source; Statistical Methods; Statistical Models; Students; Time; Training; Training Programs; United States; Variant; cohort; design; experience; improved; massive open online courses; open source; programs; prospective; public health relevance; randomized trial; research study; skills; statistics; success; tool

 DESCRIPTION (provided by applicant): There is a crisis of reproducibility and replicability of scientific results. This crisis is an increasing source of concern both in the scientific and poplar press. The crisis is so acute that the United States Congress is currently investigating reproducibility of the scientific process. At the heart of the crisis is a shortage of data analytc skill throughout the scientific enterprise. There is an emerging consensus that the best way to address the crisis is to increase data analytic training, particularly around reproducibility and replicability. In this application we (1) propose the first formal statistical model for reproduciility and replicability and then use data and experiments from the largest massive online open program in data science in the world to (2) perform randomized studies to improve our knowledge about which statistical methods and protocols lead to increased reproducibility and replicability in the hands of average users and (3) to analyze learner, course, and content characteristics that increase learner success and throughput to increase the number of trained data analysts worldwide. To accomplish goals (2) and (3) we will use the largest and highest throughput data science program in the world: the Johns Hopkins Data Science Specialization. This specialization, developed by the investigators of this project, consists of nine courses that are offered every month. Since the launch of this program in April 2014, these classes have seen more than two million enrollments and nearly all their experiences have been recorded as data. Furthermore, the MOOC platform for this series permits random assignment of quiz questions and content. We will disseminate our results through open source software, analysis protocols, our popular blog, and the Data Science Specialization to maximally improve data science training and reduce the scientific replication and reproducibility problem. The size of ths program means that by increasing quality of the program and the number of completing students by even a small percentage we can affect global data analytic behavior.

 描述（由申请人提供）：存在科学结果的再现性和可复制性危机。这场危机在科学和白杨新闻界都是一个越来越令人担忧的问题。这场危机是如此严重，以至于美国国会目前正在调查科学过程的可重复性。危机的核心是整个科学企业缺乏数据分析技能。有一个正在形成的共识，即解决危机的最佳方法是增加数据分析培训，特别是围绕可重复性和可复制性的培训。在这个应用程序中，我们（1）提出了第一个正式的可重复性和可复制性的统计模型，然后使用世界上最大的大规模在线数据科学开放项目的数据和实验（2）进行随机研究，以提高我们对统计方法和协议的了解，从而提高普通用户手中的可重复性和可复制性，（3）分析学习者，当然，和内容特征，提高学习者的成功率和吞吐量，以增加全球训练有素的数据分析师的数量。为了实现目标（2）和（3），我们将使用世界上规模最大、吞吐量最高的数据科学计划：约翰霍普金斯数据科学专业化。这一专业由该项目的调查人员开发，包括每月提供的九门课程。自2014年4月启动该计划以来，这些课程已经有超过200万人注册，几乎所有的经验都被记录为数据。此外，该系列的MOOC平台允许随机分配测验问题和内容。我们将通过开源软件，分析协议，我们的流行博客和数据科学专业化来传播我们的结果，以最大限度地改善数据科学培训并减少科学复制和再现性问题。该计划的规模意味着通过提高计划的质量和完成学生的数量，即使是很小的百分比，我们也可以影响全球数据分析行为。