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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/reproducible-research
课程评论：没有评论
课程名称：可重现研究 课程概述：本课程聚焦于以可重现的方式报告现代数据分析的概念和工具。可重现研究的理念是，数据分析以及更一般的科学声明，需与数据和软件代码一同发布，以便他人验证研究结果并进行进一步的构建。随着数据分析变得越来越复杂，涉及更大数据集和更复杂的计算，对可重现性的需求急剧增加。可重现性允许人们关注数据分析的实际内容，而不是书面总结中的表面细节。此外，可重现性使得分析对他人更具实用性，因为实际进行分析的数据和代码都是可用的。本课程将重点介绍文献统计分析工具，这些工具允许在单一文档中发布数据分析，以便他人能够轻松执行相同的分析以获得相同的结果。 课程大纲： 第1周：概念、想法与结构 本周将覆盖可重现研究的基本概念，可能对一些学员来说比较陌生。我们还将讨论如何结构化和组织数据分析，以提高其可重现性。建议按照网页上列出的视频顺序观看，但不按顺序观看也不会影响学习效果。 第2周：Markdown与knitr 本周我们将介绍一些开发可重现文档的核心工具。我们将讲解文学编程工具knitr，并展示如何将其与Markdown结合，发布可重现的网页文档。此外，我们还将介绍第一次同行评估，要求学员使用knitr撰写一份可重现的数据分析报告。 第3周：可重现研究检查清单与基于证据的数据分析 本周将讨论确保数据分析可重现性的一些基本检查清单。尽管仅遵循检查清单并不足够，但它提供了一个适用于几乎任何分析领域的必要最低标准。 第4周：案例研究与评论 本周你将观看两个关于科学研究中可重现性重要性的案例研究。
Name:Week 1: Concepts, Ideas, & Structure
Description:This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.
Name:Week 2: Markdown & knitr
Description:This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.
Name:Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
Description:This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.
Name:Week 4: Case Studies & Commentaries
Description:This week there are two case studies involving the importance of reproducibility in science for you to watch.
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
