Ten Years Reproducibility Challenge
Did you ever try to run old code that you wrote for a scientific article you published years ago? Did you encounter any problems? Were you successful? We are curious to hear your story. This is the reason why we are editing a special issue of ReScience to collect these stories.
The ten years reproducibility challenge is an invitation for researchers to try to run the code they’ve created for a scientific publication that was published more than ten years ago. This code can be anything (statistical analysis, numerical simulation, data processing, etc.), can be written in any language and can address any scientific domain. The only mandatory condition to enter the challenge is to have published a scientific article before 2010, in a journal or a conference with proceedings, which contains results produced by code, irrespectively of whether this code was published in some form at the time or not.
Note that we do not ask you to write a new version of your old code. We ask you instead to try to make your old code to run on modern hardware/software (with minimal modifications) in order to check if you can obtain the exact same results that were publised at least ten years ago.
Sounds easy? We have good reasons to think this might be more difficult than you think. And maybe the first problem you’ll have to solve is to find your own source code (see the resources section for help).
The challenge will run for a few months, until ~April 1st, 2020~April 30th, 2020 (because we’ll need time to review the different submissions). We’ll organize a workshop in Bordeaux (France) ~on June 22th, 2020~ in autumn (date to be defined) to present the results. If you intend to participate to the challenge, make sure to also come to the workshop to hear some scary stories.
How to enter the challenge ?
The only mandatory condition to enter the challenge is to have published a scientific article before January 1st 2010 and this article must have some computational aspects (i.e. code).
Step 1 You need to declare your interest in the challenge by posting on this GitHub issue the article you intend to reproduce (having your article open access would be great). This does not engage you in any way. We’ll only use this GitHub thread to gently spam you with a few reminders when we’ll be close to the deadline (01/04/2020).
Step 2 You have to try to make your old code run on your current machine, documenting in the process what is necessary to make it work. For example, you may have to downgrade your system or some libraries, to modify your original code because some library is nowhere to be found, to reinstall a specific compiler or interpreter, etc. In the end, there may be large number of different situations: you are unable to run or compile the code, you are able to run the code but it does not give you the expected results (or no result at all), the program crashed regularly (before getting results), you do not remember how to run your program, etc.
Step 3 You have to write a reproducibility report for ReScience (any number of pages) and submit it. You’ll have to indicate that this submission is for the ten years special issue. Since these reproducibility reports are rather different from regular ReScience articles, please check the author guidelines that we have prepared specifically for this special issue (and be aware that they might still evolve a bit).
Step 4 Interact with reviewers during the open peer review and have your article published open access (no APC, ReScience is free to read and free to publish).
Step 5 Come to visit us in Bordeaux and attend the workshop in June (date not yet settled) where we’ll introduce the results of the challenge. We’ll also have a bunch of great talks on reproducibility and replicability.
Frequently Asked Questions
Is there a prize for the challenge? Yes, you will get all our consideration. More seriously, there is nothing to gain but insight on the reasons why a code becomes non reproducible. We can also send you a print of the cool poster above (A2 format).
Can I run the code from someone else? No, because we want original authors to re-run their own code. However, if you’re interested in trying to run code from someone else, you can submit a “reproducibility report” that will be published in a regular issue of ReScience (if accepted after open peer-review).
I cannot find my source code, can I enter the challenge? It mostly depends if you have a good story to explain why you do not have your code anymore. Writing an article just to say you failed because you did not find the code does not sound very exciting. But still, we would like to hear from you. Be assured you’re certainly not the only one in that situation.
My original code has been used, converted, modified, upgraded and I’m sure it is reproducible That’s good. But what about the original version of the code, the one you wrote for your article ? This is this code we want to put to the test.
Is it ok to submit an article stating I cannot reproduce my results? Yes. We actually expect this case to correspond to the majority of situations. There are many possible reasons and we’re interested in knowing the exact reasons that prevented you from getting your original results.
Is it ok to submit a one page article stating everything ran fine? Yes. But you’ll need to give us some details on how you did it (what language, what OS, how are you sure you get the same results, etc.). This will serve for a study on what are the best languages, libraries, and tools for reproducibility.
The Wayback Machine is a digital archive of the World Wide Web and other information on the Internet. It was launched in 2001 by the Internet Archive, a nonprofit organization based in San Francisco, California, United States.
Software Heritage’s ambition is to collect, preserve, and share all software that is publicly available in source code form. On this foundation, a wealth of applications can be built, ranging from cultural heritage to industry and research.
Guix supports transactional upgrades and roll-backs, unprivileged package management, and more. When used as a standalone distribution, Guix supports declarative system configuration for transparent and reproducible operating systems.
Debian is a free operating system (OS) with reproducible build capacity, archive of the packages for the past releases (since 1998) and daily snapshots since 2005, making it feasible to find old versions of software of interest and debootstrap old environments.
The National Museum of Computing is home to the world’s largest collection of working historic computers.