Keeping my workflow reproducible and fast. How do I do this?
Answer: LaTeX, scriptable pipelines, cloud wrangling (), asynchronous queues, scientific workbooks, academic writing automation, and datasets, which includes some open data sets sharing tips.
See also text editors, citation management, python, open notebook science.
Background: open notebook science.
[…]our process combines the code review of engineering with the peer review of academia, wrapped in tools to make it all go at startup speed. As in code reviews, we check for code correctness and best practices and tools. As in peer reviews, we check for methodological improvements, connections with preexisting work, and precision in expository claims. We typically don’t aim for a research post to cover every corner of investigation, but instead prefer quick iterations that are correct and transparent about their limitations.
- Stevens, J.-L. R., Elver, M., & Bednar, J. A.(2013). An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook. Frontiers in Neuroinformatics, 7, 44. DOI. Online.
- McKerns, M. M., Strand, L., Sullivan, T., Fang, A., & Aivazis, M. A. G.(2012). Building a Framework for Predictive Science. arXiv:1202.1056 [cs]. Online.