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: 9244046
• 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/9244046
• 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; Geography; 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; experimental study; improved; massive open online courses; open source; programs; prospective; public health relevance; randomized trial; 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（由适用提供）：科学结果的可重复性和可复制性存在危机。在科学和杨树出版社中，这场危机是越来越多的关注的根源。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中数据分析技能的短缺。有一个新兴共识，解决危机的最佳方法是增加数据分析培训，尤其是在可重复性和可复制性方面。 In this application we (1) proposal the first formal statistical model for reproducibility 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 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全球数据分析师。为了实现目标（2）和（3），我们将使用世界上最大和最高的数据科学计划：约翰·霍普金斯数据科学专业化。该项目的调查人员开发的这种专业化包括每个月提供的九门课程。自2014年4月启动该计划以来，这些课程已有超过200万次入学率，几乎所有的经验都被记录为数据。此外，本系列的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，流行的博客以及数据科学专业化来传播结果，以最大程度地改善数据科学培训并减少科学的复制和可重复性问题。 THS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，即使是一小部分，我们也会影响全球数据分析行为。