How to implement an algorithm from a scientific paper
In my field (ecology and evolutionary biology) there's a small but concerted push to get people to publish "executable papers" where all code and data is available via GitHub, on an iPython notebook, etc. so that all figures and test cases can easily be reproduced by reviewers and researchers. If you're publishing research, it shouldn't be the reader's job to parse your paper and reimplement the research you describe; it should be on you to make your results replicable, and I think this is a standard we need to insist upon. I can't count how many papers I've read lately that were missing either crucial methods or underlying data so that replication was impossible.<p>edit: Here's an example:<p><a href="https://github.com/weecology/white-etal-2012-ecology" rel="nofollow">https://github.com/weecology/white-etal-2012-ecology</a><p><pre><code>    Run portions of the analysis pipeline:
    Empirical analyses: python mete_sads.py ./data/ empir
    Simulation analyses: python mete_sads.py ./data/ sims
    Figures: python mete_sads.py ./data/ figs</code></pre>