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<!DOCTYPE html>
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<head>
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<title>Do - ReproNim</title>
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<section class="section-1">
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<div class="col-sm-4 col-xs-8">
<h1 class="logo"><a href="index.html">ReproNim</a></h1>
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<nav class="navbar pull-right" role="navigation">
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<ul class="nav navbar-nav">
<li><a href="discover.html">Discover</a></li>
<li><a href="describe.html">Describe</a></li>
<li><a href="do.html">Do</a></li>
<li><a href="teach.html">Teach</a></li>
<li><a href="community.html">Community</a></li>
</ul>
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</section>
<section>
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<div class="col-md-12">
<h1 class="main-page-header">Do</h1>
<h3 class="main-sub-header">What are you waiting for?<br> Just <strong>Do</strong> it—Embrace reproducibility from the get-go</h3>
</div>
</div><!-- row -->
<div class="row">
<div class="col-md-12">
<p>Our computational environments and infrastructure are growing more
powerful, more heterogeneous, and more complicated. Reproducibility
can be improved through practices and tools that 1) enable efficient
management of data and scripts while capturing the history and details
of changes and 2) facilitate executing analyses in a fashion that
supports inspection, comparison, validation, modification, and
re-execution.
<br>To become most effective, such solutions should consider all
stages of the scientific process, from planning the experiment and
collecting data to publishing the findings. We aim to assist the
neuroimaging community in improving reproducibility of their results
by <strong>Do</strong>ing</p>
<ul>
<li>data conversion with minimal manual effort while establishing
data provenance from the moment data are collected</li>
<li>collection and analysis of data provenance throughout different steps of
the scientific process</li>
<li>(re)execution of (previous) analyses in a scalable and flexible
manner by taking advantage of both existing infrastructure and new
technologies.</li>
</ul>
<p>
We strongly believe in the benefits of modular design,
collaboration, and re-use. That is why, instead of developing a
single monolithic platform to "solve all the problems", we are
reusing and contributing to existing projects as much as possible.
To that end, in the scope of this project, we actively maintain
relevant software
within <a href="http://neuro.debian.net">NeuroDebian</a> so that
ReproNim and other projects can benefit from this turnkey
platform. We also provided official Debian packaging for
<a href="http://singularity.lbl.gov">Singularity</a> "scientific
containerization" platform to make your research more flexible and
reproducible. With our ongoing software and platform development, we
aim to provide you with a collection of tools that are useful on
their own even if you choose not to use the full suite of products.
</p>
<p>
Currently we are focused on the following projects. You can help us
by trying and starting to use them, contributing, or by sharing your
ideas on how to improve them. We need your feedback (positive or
negative) to make our projects most beneficial
for <strong>your</strong> research.
</p>
</div>
</div>
</div>
</section>
<section class="section-3">
<div class="container">
<div class="row">
<div class="col-sm-4">
<h3><a href="http://github.com/repronim/reproin">ReproIn</a></h3>
<p>
Collect and prepare neuroimaging data in an efficient and automated
way to immediately benefit from the established community
standard <a href="http://bids.neuroimaging.io">Brain Imaging Data
Structure (BIDS)</a> and <a href="http://datalad.org">DataLad</a>, a
distributed data distribution and management system. To achieve that
we
</p>
<ul>
<li>formalize
a <a href="https://github.com/nipy/heudiconv/blob/master/heudiconv/heuristics/reproin.py">specification</a>
and <a href="https://github.com/ReproNim/reproin/blob/master/docs/walkthrough-1.md">procedures</a>
for MRI scanning to allow for flexible, automated, and
non-ambiguous conversion of all acquired MR
data from original DICOM files to BIDS dataset(s)</li>
<li>contribute to the development of
the <a href="http://github.com/nipy/heudiconv">Heuristic DICOM
Converter (HeuDiConv)</a> which provides us the base
implementation platform</li>
<li>integrate with "lab notebook" suites such
as <a href="https://github.com/ReproNim/brainverse">BrainVerse</a></li>
</ul>
<p>
Automatically converting all collected data to BIDS, while
retaining all original DICOMs as well a clear association between
raw and converted data, makes it possible to
</p>
<ul>
<li>
validate and assess the quality of data acquisition by using
the <a href="https://github.com/INCF/bids-validator">BIDS-validator</a>
and <a href="http://mriqc.readthedocs.io">MRIQC</a> to detect
possible problems with the design or acquisition in the earliest
stages of a project and avoid many common mistakes stemming from
manual data conversion and curation
<li>
automatically reconvert all collected data if relevant
defects get detected in the conversion backend utility,
if additional metadata stored in DICOMs is found to be necessary, or
in order to confirm the reproducibility of the data itself
</li>
<li>
make it possible to start data analysis immediately after
collection using any of the BIDS-aware applications
</li>
<li>
facilitate data sharing (private or public) and publication (data
descriptor papers) since all data is immediately almost completely
ready for sharing with a standardized file layout and annotation.
</li>
</ul>
<p>Visit <a href="http://reproin.repronim.org">reproin.repronim.org</a>
for more information.</p>
<p>
ReproIn will
be <a href="https://ww5.aievolution.com/hbm1801/index.cfm?do=ev.viewEv&ev=1379">presented
at OHBM 2018 (#2097 talk at Tue, June 19 10:30-10:42, "Informatics"
session; poster TBA).
</p>.
</div>
<div class="col-sm-4">
<h3><a href="https://github.com/myyoda">YODA</a> Principles</h3>
<p>YODA (YODA’s organigram on data analysis) outlines an approach for
using version control systems such as
<a href="http://git-scm.org">git</a>,
<a href="http://git-annex.branchable.com">git-annex</a>,
and <a href="http://datalad.org">DataLad</a> in a modular fashion to
cover the entire life-span of a research project with reliable,
non-ambiguous tracking and orchestration of all digital products of
a study (e.g., inputs, code, outputs). Modularization facilitates
the independent re-use of parts (e.g., the same data used across
multiple studies, versions of a software library used repeatedly) in
a manner that scales to dataset sizes found in cutting edge
high-resolution neuroimaging research.</p>
<p>
In the scope of the ReproIn project, having all data collected as
DataLad datasets makes it possible to
</p>
<ul>
<li>
start changing and enriching BIDS dataset with information not
available during scanning (condition onsets, dataset metadata,
etc.) while allowing for subsequent updates and merges with new
data coming from the scanner using standard Git mechanisms
</li>
<li>
distribute data for curation and processing across available
infrastructure while relying on git-annex to maintain information
about data availability, and Git providing clear information about
dataset version(s)
</li>
<li>
annotate sensitive data files with metadata to automatically
restrict their public sharing (e.g. original DICOMs, non-defaced anatomicals).
</li>
</ul>
<p>We also extend and contribute new functionality
to <a href="http://datalad.org">DataLad</a> to facilitate
VCS-enabled provenance tracing of execution and results:
<ul>
<li>
<strong><a href="http://docs.datalad.org/en/latest/cmdline.html#reproducible-execution">run/rerun</a></strong>
(see <a href="http://docs.datalad.org/en/latest/usecases/simple_provenance_tracking.html">DataLad
documentation on provenance tracking</a>)
</li>
<li>
<strong><a href="http://docs.datalad.org/en/latest/generated/man/datalad-addurls.html">addurls</a></strong>
to produce DataLad dataset(s) from a set of structured data as
in the <a href="https://github.com/ReproNim/simple_workflow">Simple
Workflow</a> papers.</li>
</ul>
</li>
</ul>
<p>
You can adhere to YODA principles by making a better use of VCS in
your research projects. Visit and contribute to
our <a href="http://www.repronim.org/module-reproducible-basics/02-vcs/">training
materials</a> and <a href="https://github.com/myyoda/template">YODA
template</a> repository.</p>
<p>
A poster presentation at OHBM 2018 (Time TBA), YODA will have.
</p>
</div>
<div class="col-sm-4">
<h3><a href="https://github.com/ReproNim/NICEMAN">NICEMAN</a></h3>
<p>Neuroimaging Computation Environments Manager
(<a href="http://niceman.repronim.org">NICEMAN</a>) is being
developed to help researchers track and manage computation resources
that they have available and to use them in a reproducible and
scalable way. We aim for NICEMAN to</p>
<ul>
<li>collect information about used resources—such as software packages
(Debian, Conda, pip), VCS repositories (Git, SVN), containers
(Docker, Singularity)—possibly enriching already
collected provenance information (e.g., obtained
from <a href="http://www.repronim.org/describe.html">BrainVerse</a>,
<a href="http://reprozip.org">ReproZip</a>, or workflow
engines). We emphasize collecting an exhaustive amount of
information so it could be sufficient to later automatically
re-instantiate necessary components given just a
specification.</li>
<li>analyze collected information to answer questions like
<ul>
<li>"Am I using the same software as in the original analysis?"
<li>"What has changed?"
<li>"Does a given environment satisfy the necessary requirements?"
</ul>
</li>
<li>(when feasible) adjust any given computational environment to
satisfy the specified requirements, or just repopulate a new
(Docker, Singularity, etc.) container which would.</li>
<li>provide a uniform interface to a variety of computational
environments (containers, cloud, etc.) for interactive
sessions and scheduling of computational tasks while allowing
for tracing of computational resources used by the
computation.</li>
</ul>
<p>Visit <a href="http://reproin.repronim.org">reproin.repronim.org</a> for more information.</p>
<p>
NICEMAN will have a poster presentation at OHBM 2018 (Time TBA).
</p>
</div>
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<div class="container">
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<h1 class="main-page-header" id="team">Our Do Team</h1>
</div>
</div>
<div class="row">
<div class="col-sm-3">
<span class="testimonial-img"><img src="https://avatars0.githubusercontent.com/u/12954546?s=460&v=4" style="border-radius: 100%; display: block" alt="" /></span>
<h3>Robert Buccigrossi</h3>
<p></p>
</div>
<div class="col-sm-3">
<span class="testimonial-img"><img src="http://centerforopenneuroscience.org/theme/img/team/yarik.jpg" style="border-radius: 100%; display: block" alt="" /></span>
<h3>Yaroslav Halchenko</h3>
<p></p>
</div>
<div class="col-sm-3">
<span class="testimonial-img"><img src="images/christian-haselgrove.jpg" style="border-radius: 100%; display: block" alt="" /></span>
<h3>Christian Haselgrove</h3>
<p></p>
</div>
<div class="col-sm-3">
<span class="testimonial-img"><img src="http://centerforopenneuroscience.org/theme/img/team/kyle.jpg" style="border-radius: 100%; display: block" alt="" /></span>
<h3>Kyle Meyer</h3>
<p></p>
</div>
</div>
<div class="row">
<div class="col-sm-3">
<span class="testimonial-img"><img src="images/matt-travers.jpg" style="border-radius: 100%; display: block" alt="" /></span>
<h3>Matt Travers</h3>
<p></p>
</div>
</div>
</div>
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