ReScience journal

If computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and replicating results, computational science still lags behind. In the best case, authors may provide the sources of their research as a compressed archive and feel confident their research is replicable. But this is not exactly true. Buckheit & Donoho (1995) explained almost 20 years ago that, an article about computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data that produced the result. The computational part in computational sciences implies the use of computers, operating systems, tools, frameworks, libraries and data. This leads to such a large number of combinations (taking into account the version for each component) that the chances to have the exact same configuration as one of your colleagues are nearly zero. This draws consequences in our respective computational approaches in order to make sure that computational research can be actually and faithfully replicated.

ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible. To achieve such a goal, the whole editing chain is radically different from any other traditional scientific journal. ReScience lives on github where each new implementation is made available together with comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee that any researcher can re-use it. If you ever replicated computational results from the literature in your research, ReScience is the perfect place to publish this new implementation.

The Editorial Board

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