[Haskell-cafe] [ANN] Laborantin: experimentation framework

[Tom Nielsen]

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-neurobiology/publications/braincurry

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 <lucas.dicioccio at gmail.com>wrote:

> 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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