Hi Lucas,
In connection with your work on Laborantin, you may be interested in our
papers:
Braincurry: A domain-specific language for integrative neuroscience
http://www2.le.ac.uk/departments/biology/research/neuroscience/matheson-neur...
A formal mathematical framework for physiological observations, experiments
and analyses.
http://rsif.royalsocietypublishing.org/content/9/70/1040.long
I found it difficult to excite experimental biologists about the benefit of
adopting experiment description languages. I am now concentrating on a
functional language for statistical data analysis - see
https://bayeshive.com
Tom
On 23 December 2013 09:27, lucas di cioccio
Dear all,
I am happy to announce Laborantin. Laborantin is a Haskell library and DSL for running and analyzing controlled experiments.
Repository: https://github.com/lucasdicioccio/laborantin-hs Hackage page: http://hackage.haskell.org/package/laborantin-hs
Laborantin's opinion is that running proper experiments is a non-trivial and often overlooked problem. Therefore, we should provide good tools to assist experimenters. The hope is that, with Laborantin, experimenters will spend more time on their core problem while racing through the menial tasks of editing scripts because one data point is missing in a plot. At the same time, Laborantin is also an effort within the broad open-science movement. Indeed, Laborantin's DSL separates boilerplate from the actual experiment implementation. Thus, Laborantin could reduce the friction for code and data-reuse.
One family of experiments that fit well Laborantin are benchmarks with tedious setup and teardown procedures (for instance starting, configuring, and stopping remote machines). Analyses that require measurements from a variety of data points in a multi-dimensional parameter space also fall in the scope of Laborantin.
When using Laborantin, the experimenter:
* Can express experimental scenarios using a readable and familiar DSL. This feature, albeit subjective, was confirmed by non-Haskeller colleagues. * Saves time on boilerplate such as writing command-line parsers or encoding dependencies between experiments and analysis results in a Makefile. * Benefits from auto-documentation and result introspection features when one comes back to a project, possibly months or weeks later. * Harnesses the power of Haskell type-system to catch common errors at compile time
If you had to read one story to understand the pain points that Laborantin tries to address, it should be Section 5 of "Strategies for Sound Internet Measurement" (V. Paxson, IMC 2004).
I'd be glad to take question and comments (or, even better, code reviews and pull requests).
Kind regards, --Lucas DiCioccio (@lucasdicioccio on GitHub/Twitter)
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