Re: [O] A manuscript on "reproducible research" introducing org-mode

[Christophe Pouzat]

Aloha Tom,

Not yet in print, still on the accepted papers list
(http://www.sciencedirect.com/science/journal/aip/09284257), sorry. It
seems that I chose the "slowest" neuroscience journal!

Your JSS paper of last month (with Eric, Dan and Carsten) is great by
the way. It seems that I missed the announcements on the list when the
pre-print was posted, otherwise I would have managed to cite it in mine.

The bibtex entry for my paper (just downloaded from Elsevier site) is:

@article{Delescluse2011,
title = "Making neurophysiological data analysis reproducible: Why and how?",
journal = "Journal of Physiology-Paris",
volume = "",
number = "0",
pages = " - ",
year = "2011",
note = "",
issn = "0928-4257",
doi = "10.1016/j.jphysparis.2011.09.011",
url = "http://www.sciencedirect.com/science/article/pii/S0928425711000374";,
author = "Matthieu Delescluse and Romain Franconville and Sébastien Joucla and 
Tiffany Lieury and Christophe Pouzat",
keywords = "Software",
keywords = "R",
keywords = "Emacs",
keywords = "Matlab",
keywords = "Octave",
keywords = "LATEX",
keywords = "Org-mode",
keywords = "Python",
abstract = "Reproducible data analysis is an approach aiming at complementing 
classical printed scientific articles with everything required to independently 
reproduce the results they present. “Everything” covers here: the data, the 
computer codes and a precise description of how the code was applied to the 
data. A brief history of this approach is presented first, starting with what 
economists have been calling replication since the early eighties to end with 
what is now called reproducible research in computational data analysis 
oriented fields like statistics and signal processing. Since efficient tools 
are instrumental for a routine implementation of these approaches, a 
description of some of the available ones is presented next. A toy example 
demonstrates then the use of two open source software programs for reproducible 
data analysis: the “Sweave family” and the org-mode of emacs. The former is 
bound to R while the latter can be used with R, Matlab, Python and many more 
“generalist” data processing software. Both solutions can be used with 
Unix-like, Windows and Mac families of operating systems. It is argued that 
neuroscientists could communicate much more efficiently their results by 
adopting the reproducible research paradigm from their lab books all the way to 
their articles, thesis and books."
}

I will post on the list the "official" bibliographic reference as soon
as the paper is in print.

Take care,

Christophe  


address@hidden (Thomas S. Dye) writes:

> Aloha Christophe,
>
> Has this article appeared in print?  If so, can you forward publication
> details? 
>
> All the best,
> Tom
>
> Christophe Pouzat <address@hidden> writes:
>
>> "Thomas S. Dye" <address@hidden> a écrit&nbsp;:
>>
>>> Christophe Pouzat <address@hidden> writes:
>>>
>>>> Dear all,
>>>>
>>>> M. Delescluse, R. Franconville, S. Joucla, T. Lieury and myself (C.
>>>> Pouzat) have just put a manuscript entitled: "Making
>>>> neurophysiological data analysis reproducible. Why and how?" on a
>>>> pre-print server: http://hal.archives-ouvertes.fr/hal-00591455/fr/
>>>> Although the paper has been written for a neurobiological journal, the
>>>> reader does not have to be a neuroscientist to read and understand it.
>>>> A toy example illustrating the use of org-mode + Babel (with Python
>>>> and Octave) takes a fair part of the manuscript. Other tools like R +
>>>> Sweave are presented and many more are mentioned.
>>>>
>>>> I thank Eric Schulte for comments on the manuscript and Eric (again)
>>>> together with the whole org-mode / Babel community for developing such
>>>> a great tool.
>>>>
>>>> Any comment, remark, suggestion on the manuscript is of course welcome.
>>>>
>>>> Christophe
>>>>
>>
>>> Aloha Christophe,
>>>
>>> Thank you for an interesting and useful paper.  I was happy with the
>>> distinction you draw between reproducible analysis and reproducible
>>> research, which certainly applies to my field of archaeology where
>>> unique sites are typically destroyed by the data collection effort.  I
>>> also think the emphasis you place on data preprocessing is just the
>>> right approach; inclusion of the raw data in a reproducible analysis
>>> opens up many possibilities, which must be a benefit to a scientific
>>> community's pursuit of knowledge.
>>>
>>> May I offer a suggestion?  Carsten Dominik published the Org Mode 7
>>> Manual last year and it would be nice to see it cited in your paper.
>>>
>>> @book{dominik10:_org_mode_refer_manual,
>>>   author =       {Carsten Dominik},
>>>   title =        {The Org Mode 7 Reference Manual: Organize Your Life
>>>   with GNU Emacs},
>>>   publisher =    {Network Theory Ltd.},
>>>   year =         2010
>>> }
>>>
>>> All the best,
>>> Tom
>>> --
>>> Thomas S. Dye
>>> http://www.tsdye.com
>>>
>>
>> Dear Tom,
>>
>> Thanks for these interesting and positive comments. I apologize for
>> forgetting the obvious reference to Carsten's reference manual. I will
>> definitely include it in the next version.
>> I hope that people in my field will come to think the way you do about
>> sharing their raw data. I'm just afraid that the way is still long…
>> but the goal is reachable. Raw data aside, org-mode is surely a tool
>> which should help people experimenting with the "reproducible research
>> paradigm". As I wrote to Eric (Schulte), M. Delescluse and I wrote a
>> first RR manuscript 6 years ago based on R/Sweave. The manuscript
>> never got submitted for different reasons, among them, the amount of
>> work required to learn R and LaTeX. Learning about org-mode convinced
>> me that it would be worth re-activating the project.
>>
>> Christophe
>>
>> Most people are not natural-born statisticians. Left to our own
>> devices we are not very good at picking out patterns from a sea of
>> noisy data. To put it another way, we are all too good at picking out
>> non-existent patterns that happen to suit our purposes.
>> Bradley Efron & Robert Tibshirani (1993) An Introduction to the Bootstrap
>>
>> --
>>
>> Christophe Pouzat
>> Laboratoire de Physiologie Cerebrale
>> CNRS UMR 8118
>> UFR biomedicale de l'Universite Paris-Descartes
>> 45, rue des Saints Peres
>> 75006 PARIS
>> France
>>
>> tel: +33 (0)1 42 86 38 28
>> fax: +33 (0)1 42 86 38 30
>> mobile: +33 (0)6 62 94 10 34
>> web: http://www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat.html
>>

-- 

Most people are not natural-born statisticians. Left to our own
devices we are not very good at picking out patterns from a sea of
noisy data. To put it another way, we are all too good at picking out
non-existent patterns that happen to suit our purposes.
Bradley Efron & Robert Tibshirani (1993) An Introduction to the Bootstrap

--

Christophe Pouzat
MAP5 - Mathématiques Appliquées à Paris 5
CNRS UMR 8145
45, rue des Saints-Pères
75006 PARIS
France

tel: +33142863828
mobile: +33662941034
web: http://www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat.html