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@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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