Comments on "Data Intensive Scientific Discovery"

An interesting book has been published free online on the future of the scientific method and the role of computing, software, and information systems: Data Intensive Scientific Discovery
The ideas link in with the concerns of myself and others with reproducible research, data sharing, data analysis, and open publishing.

Linking text, results, and analyses: Increasing transparency and efficiency

I have recently been thinking about the relationship between text in a final report and data analysis. The broader concern is with making the conduct and reporting of statistical analyses more transparent. I am inspired by the ideas of literate programming, Sweave, and open access to data.

Something to aspire to:

• Raw data is shared  (ethics, copyright, and other considerations permitting). 
• Code is shared that shows how the data was imported, transformed, and analysed. This code is well written, commented, and documented.
• The report is shared as opposed to requiring a paid subscription.
• Report output including tables, figures, and some text is linked directly to the analyses in code.

While the aspirations transcend R, I like the prospect of having analyses in R integrated with a final report. The inclusion of tables and figures , at least conceptually is a straightforward idea. However, the inclusion of text in a results section is a little fuzzier. Surely, text in a results section (I'll call it "results text" for short) varies in how it relates to actual analyses. Thus, I had the following questions: 1) What is the unit of results text? 2) How does results text vary and what should be automatically supplied by R?; 3) For results text that should not be supplied by R, how should it be integrated into an analysis process?

Initial thoughts: After a little reflection I had the following thoughts: