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#LyX 2.0.0 created this file. For more info see http://www.lyx.org/
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\begin_body
\begin_layout Title
\noindent
Proposal for INCF Training in neuroinformatics: Software development for
reproducible neuroimaging research in Python.
\end_layout
\begin_layout Section
\noindent
Topic
\end_layout
\begin_layout Standard
\noindent
\align block
As the complexity of computational methods used in scientific research increases
, there is also an increasing awareness of the need to develop methods to
maintain the reproducibility of research results (as expressed, for example,
in an editorial in
\emph on
Nature
\emph default
on February 17th, 2011).
This concern is particularly pertinent for neuroimaging, a field which
often requires many steps of computation to be performed on large, complex
data sets.
Python is increasingly becoming the lingua franca of reproducible research
in many fields, including neuroscience.
This course will teach neuroimaging researchers to use modern open-source
software development tools, in order to conduct reproducible research.
The course will teach best practices in software development (“software
carpentry”,
\begin_inset CommandInset href
LatexCommand href
target "http://software-carpentry.org"
\end_inset
), as well as specific solutions to analysis problems in neuroimaging, with
an emphasis on problem-solving with real data.
The first day of the course will focus on the basic Python stack of tools
for scientific programming (version control, ipython, numpy, scipy, matplotlib,
etc.).
Days 2-4 will each be divided into three two-hour sessions devoted to general
topics, such as statistics, machine learning, time series analysis, and
visualization, as well as presenting several neuroimaging-specific Python
libraries including nipy, dipy, nibabel, nitime, pymvpa, and nipype (see
http://nipy.org).
The last day of the course will have the students work in small groups
(2-3 people) on selected team projects to analyze real data using the tools
we've presented.
\end_layout
\begin_layout Standard
\noindent
\align block
The course will fill gaps in the traditional training of practitioners of
neuroimaging: though some scientists in this field receive formal training
in programming and fundamentals of computer science, there is very little
training available in neuroinformatics.
In order to focus the scope of the course, we have defined the following
three major goals of learning:
\end_layout
\begin_layout Enumerate
\noindent
\align block
Participants will acquire computing practices to facilitate reproducible
research.
This included knowledge in practices of scientific software engineering
and knowledge of specific tools for version control, data handling, collaborati
on and documentation, which facilitate these practices.
\end_layout
\begin_layout Enumerate
\noindent
\align block
Participants will acquire knowledge and skills in neuroimaging data analysis.
In particular, participants will acquire domain-specific knowledge in a
variety of topics in modern neuorimaging, including diffusion imaging,
time-series analysis, machine learning, graph-theory, etc.
\end_layout
\begin_layout Enumerate
\noindent
\align block
Through interaction with instructors and practical exercises, participants
will gain confidence in their programming skills and will increase their
capacity to productively perform reproducible computational analysis, to
answer a variety of scientific questions.
\end_layout
\begin_layout Standard
\noindent
\align block
Fulfilling these goals will also provide us with a clear measure of the
success of the training.
\end_layout
\begin_layout Section
\noindent
Target audience
\end_layout
\begin_layout Standard
\noindent
\align block
Neuroimaging researchers (mainly graduate students and post-doctoral researchers
\begin_inset Foot
status open
\begin_layout Plain Layout
Target groups 3 and 4 in the summary of the 1st INCF Workshop on Needs for
Training in Neuroinformatics (2008)
\end_layout
\end_inset
) interested in learning best practices in the use and development of reproducib
le research tools.
The target audience are researchers with some programming experience in
Matlab, Python, or C/C++.
The course will admit 30 participants, to maintain a good instructor/student
ratio.
In particular, we will seek to make the course available to members of
groups under-represented in neuroinformatics, such as women.
\end_layout
\begin_layout Section
\noindent
Organizers
\end_layout
\begin_layout Standard
\noindent
\align block
Matthew Brett, Jarrod Millman, Fernando Perez, Ariel Rokem.
\end_layout
\begin_layout Section
\noindent
Dates
\end_layout
\begin_layout Standard
\noindent
\align block
August 5th - August 10th, 2012
\end_layout
\begin_layout Section
\noindent
Location
\end_layout
\begin_layout Standard
\noindent
\align block
University of California, Berkeley (USA)
\end_layout
\begin_layout Section
\noindent
Time table
\end_layout
\begin_layout Paragraph*
Sunday evening:
\end_layout
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Arrival in Berkeley
\end_layout
\begin_layout Paragraph*
Monday
\end_layout
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10-12 AM: Overview and Introduction to neuroinformatics and reproducible
research (Jarrod Millman)
\end_layout
\begin_layout Standard
1-3 PM: Scientific computing with Python (numpy/scipy/matplotlib/ipython;
Fernando Perez and Stefan van der Walt)
\end_layout
\begin_layout Standard
4-6 PM: Version control and literate programming (git/github/sphinx; Matthew
Brett and Fernando Perez)
\end_layout
\begin_layout Paragraph
Tuesday
\end_layout
\begin_layout Standard
10-12 AM: Neuroimaging data formats (nibabel/nipy; Matthew Brett and Michael
Hanke)
\end_layout
\begin_layout Standard
1-3 PM: Image processing (nipy/scikits.image; Stefan van der Walt)
\end_layout
\begin_layout Standard
\noindent
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Software library
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