I was just reading the following two articles/notes. While they are not entirely about reproducible research, I think they reflect well the worries that many researchers have about current “publish or perish” research practices. Not sure I agree with all of it, but they do make a number of good remarks.
D. Geman, Ten Reasons Why Conference Papers Should be Abolished, Johns Hopkins University, Nov. 2007.
Y. Ma, Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information, ECE, University of Illinois, Nov. 2007.
I just got a pointer to ResearchAssistant, a Java tool to keep better track of research experiments. It stores the exact circumstances under which experiments are performed, with parameter values etc. Looks like a very nice tool for reproducible research. At least, it should make it very easy when writing a paper to trace back how certain results were obtained. If you’re doing research in Java, I’d certainly recommend taking a look at RA!
The tool is also described in the following paper:
D. Ramage and A. J. Oliner, RA: ResearchAssistant for the Computational Sciences, Workshop on Experimental Computer Science (ExpCS), June 2007.
Some more interesting reading:
K. Price, Anything You Can Do, I Can Do Better (No You Can’t)…, Computer Vision, Graphics, and Image Processing, Vol. 36, pp. 387-391, 1986, 
doi:10.1016/0734-189X(86)90083-6.
Abstract: Computer vision suffers from an overload of written information but a dearth of good evaluations and comparisons. This paper discusses why some of the problems arise and offers some guidelines we should all follow.
Very nice reading material, and (although I know these ideas are around for quite some time already) I was amazed to see so many parallels to our recent IEEE Signal Processing Magazine paper, already in this paper by Price from 1986. That’s more than 20 years ago! Price talks about the reproducibility problems in computer vision and image processing, writing we should “stand on other’s shoulders, not on other’s toes”. He also did a study on reproducibility of a set of about 42 papers, verifying the size of the dataset and clarity of the problem statement. Price concludes as follows: “Researchers should make the effort to obtain implementations of other researchers’ systems so that we can better understand the limitations of our own work.”
Again, interesting to see how these issues and worries have been around for more than 20 years in the field of image processing. It’s about time to drastically improve our standards, I think!
I would really recommend this article to anyone interested in issues around reproducible research.
I just read the following paper:
A. J. Rossini and F. Leisch, Literate statistical practice, UW Biostatistics Working Paper Series 194, University of Washington, WA, USA, 2003.
Although I am not a statistician, this was a very interesting paper to me. It gives a nice description of a possible literate programming approach in statistics. The authors propose a very versatile type of document combining documentation and code/statistical analyses, interweaved as in the original description of literate programming by Knuth. From this versatile document, which contains a complete description of the research work, multiple reports can be extracted, such as an article, an internal report, an overview of the various analyses that were performed, etc.
I just read the article about Netflix’ Million Dollar Programming Prize on IEEE Spectrum.
Robert M. Bell, Jim Bennett, Yehuda Koren, and Chris Volinsky, The Million Dollar Programming Prize, IEEE Spectrum Online, http://www.spectrum.ieee.org/may09/8788.
Interesting article, showing again how contests proposing a challenge can inspire a lot of great work, and allow an ‘objective’ comparison between algorithms. I think they provide a great way to motivate researchers to work on real problems, with testing on standardized datasets.
I am glad to let you know that our paper has been published in the latest issue of IEEE Signal Processing Magazine:
P. Vandewalle, J. Kovacevic and M. Vetterli, Reproducible Research in Signal Processing – What, why, and how, IEEE Signal Processing Magazine, Vol. 26, Nr. 3, pp. 37-47, 2009, DOI: 10.1109/MSP.2009.932122.
Have you ever tried to reproduce the results presented in a research paper? For many of our current publications, this would unfortunately be a challenging task. For a computational algorithm, details such as the exact data set, initialization or termination procedures, and precise parameter values are often omitted in the publication for various reasons, such as a lack of space, a lack of self-discipline, or an apparent lack of interest to the readers, to name a few. This makes it difficult, if not impossible, for someone else to obtain the same results. In our experience, it is often even worse as even we are not always able to reproduce our own experiments, making it difficult to answer questions from colleagues about details. Following are some examples of e-mails we have received: “I just read your paper X. It is very completely described, however I am confused by Y. Could you provide the implementation code to me for reference if possible?” “Hi! I am also working on a project related to X. I have implemented your algorithm but cannot get the same results as described in your paper. Which values should I use for parameters Y and Z?”
Enjoy reading! And feel free to post your comments!
Last month, a few former colleagues at LCAV did some cross-testing of the reproducible research compendia available at rr.epfl.ch. And I must say, from the results I have seen so far, it is quite a sobering experience. Many of those which I considered to be definitely reproducible didn’t pass the test (entirely). I guess that shows again how difficult it is to make work really reproducible, even if you fully intend to do it. So that also leads me to my conviction that for papers that do not have code and data online, it is almost impossible to reproduce the exact results. There is work to be done on the road to reproducible research!
I’ll need to look further into the reasons why even some of my own work did not pass the test.
The current issue of Computing in Science and Engineering (CiSE) is a special issue on reproducible research, edited by two pioneers in the field: Jon Claerbout and Sergey Fomel. They have assembled a great set of articles from experts with a lot of first-hand, personal reproducible research experience, so I would highly recommend this to my colleague researchers!
I got a pointer earlier this week to a New York Times article about R. A very interesting article about the use of R in scientific communities and industrial research, mainly for statistical analysis. R is open source software, so it is free and has already taken advantage from contributions made by various authors. And (although I haven’t used it myself yet), it is a great tool for reproducible research. Using the package Sweave, authors can write a single document containing their article and the R code to reproduce the results and put them in place. This ensures that all the material is in a single place.
It also shows something about the amazing power of open source software developed by a community of authors (and typically users at the same time).
On doing research
I was just reading the following two articles/notes. While they are not entirely about reproducible research, I think they reflect well the worries that many researchers have about current “publish or perish” research practices. Not sure I agree with all of it, but they do make a number of good remarks.
D. Geman, Ten Reasons Why Conference Papers Should be Abolished, Johns Hopkins University, Nov. 2007.
Y. Ma, Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information, ECE, University of Illinois, Nov. 2007.