Many papers I read in hydrology present research that is very difficult to reproduce. Because, as a scientist, I would like to reproduce the results of what I read (this is science, indeed!) some rules should be followed. I found this group of scientists that initiated a Reproducible Research web site, especially directed to image processing colleagues, but easily extendible to Hydrology, and related fields. They offer a how to guide, which is verbatim reported here below:
"Of course, it all starts with a good description of the theory, algorithm, or experiments in the paper. A block diagram or a pseudo-code description can do miracles! Once this is done, make a web page containing the following information:
- Authors (with links to the authors' websites)
- Full reference of your paper, with current publication status, and a PDF of your paper
- All the code to reproduce all the results, images and tables. Make sure all the code is well documented, and that there is a readme file explaining how to execute it
- All the data (images, measurements, etc) to reproduce all the results, images and tables. Add a readme file explaining what the data represent
- A list of configurations on which you tested your code (software version, platform)
- An e-mail address that people can use for comments and remarks (and to report bugs)
Depending on the field in which you work, it can also be interesting to add the following (optional) information to the web page:
- Images (add their captions, so that people know what Figure xx is about)
- References (with abstracts)
For examples, see this list of reproducible papers at LCAV, EPFL. "
Obviously they also link a blog and links to various RR resources. RR, incidentally, the same as my initials.
Now there is also a book about reproducible research made with R by Christopher Gandrud. The chapter can be found here.