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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<!-- Edit me start! -->
<title>An Integrated and Trusted Scientific and Statistical Computing Core</title>
<meta name="description" content="Slides describing my experience and ideas for NIH SSCC ">
<meta name="author" content=" Yaroslav O. Halchenko ">
<!-- Edit me end! -->
<link rel="stylesheet" href="reveal.js/dist/reset.css">
<link rel="stylesheet" href="reveal.js/dist/reveal.css">
<link rel="stylesheet" href="reveal.js/dist/theme/beige.css">
<!-- Theme used for syntax highlighted code -->
<link rel="stylesheet" href="reveal.js/plugin/highlight/monokai.css">
</head>
<body>
<div class="reveal">
<div class="slides">
<!-- Start of slides -->
<section>
<section>
<a href="http://centerforopenneuroscience.org/"><img data-src="pics/con-ccn-dartmouth-letterhead.svg"></a>
<h2>An Integrated and Trusted Scientific and Statistical Computing Core</h2>
<div style="margin-top:1em;text-align:center">
<table style="border: none;">
<tr>
<td>
Yaroslav O. Halchenko<br><small><a href="https://twitter.com/yarikoptic" target="_blank">
<img data-src="pics/twitter.png" style="height:30px;margin:0px" />@yarikoptic</a></small>
</td>
<td></td>
</tr>
<tr>
<td>
<small><br><a href="http://centerforopenneuroscience.org/" target="_blank">Center for Open Neuroscience</a>
<br><a href="https://pbs.dartmouth.edu/" target="_blank">Department of Psychological and Brain Sciences</a>
<br><a href="https://www.dartmouth.edu/ccn/" target="_blank">Center for Cognitive Neuroscience</a><br>
<a href="http://www.dartmouth.edu" target="_blank">Dartmouth
College</a></small>
<img style="width:200px; margin: 0px" data-src="pics/slides-qr.png"/>
</td>
<td>
</td>
</tr>
</table>
</div>
<!--
<p style="z-index: 100;position: fixed;background-color:#ede6d5;font-size:35px;box-shadow: 10px 10px 8px #888888;margin-top:0px;margin-bottom:100px;margin-left:1000px">
<img src="pics/QRcode_hhu.png" height="200">
</p>
<br><br><small>
Slides: <a href="https://doi.org/10.5281/zenodo.6346849" target="_blank">
DOI 10.5281/zenodo.6346849</a> (Scan the QR code)
<br>
</small>
-->
<small>
<!-- TODO place them there; Add QR code how in template -->
Live slides/Sources (git clone - add /.git):
<a href="https://datasets.datalad.org/centerforopenneuroscience/talks/2022-nih-compcore/">https://datasets.datalad.org/centerforopenneuroscience/talks/2022-nih-compcore/</a>
<br>
<small>
<a href="http://repronim.org" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/repronim-logo-vertical.svg"/></a>
<a href="https://open-brain-consent.readthedocs.io" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/OBC_LogoCheck.svg"/></a>
<!-- <a href="https://bids.neuroimaging.io" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/BIDS_Logo.png"/></a> -->
<a href="http://pymvpa.org" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/pymvpa_icon.png"/></a>
<a href="http://neuro.debian.net" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/neurodebian.png"/></a>
<a href="http://datalad.org" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/datalad_logo_posters_banner.svg"/></a>
<a href="https://github.com/myyoda/myyoda" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/yoda.svg"/></a>
<a href="https://dandiarchive.org" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/dandi-logo-square.svg"/></a>
</small>
</small>
</section>
</section>
<section data-transition-speed="zoom">
<section>
<img style="height:800px" data-src="pics/yarik-goal.svg"/>
</section>
<section>
<h1>Who am I?</h1>
<img style="height:400px" data-src="pics/borrowed/twitter-unsolicited-advice.png"/>
</section>
<section>
<h3>Brief Bio</h3>
<p>Born in Siberia (RSFSR, USSR), Grew up in Ukraine, Matured in U.S.A.</p>
<ul style="font-size:120%">
<li class="fragment"><b>-1994 Physics Mathematics Gymnasium #17 (Ukraine):</b><br>
<small> Regional&State Physics and Programming
competitions. MS-DOS. Borland Pascal</small></li>
<li class="fragment"><b>-1999 VSTU (Ukraine):
<span style="float:right;">Masters in Opto-Electronic Engineering</span></b><br>
<small> (@yarikoptic). State Physics and International ACM Programming
competitions. Spine diagnostic apparatus. Member of "Small Academy of Science of
Ukraine". SPIE. Soros Fellowship (twice). MS-DOS/Windows.
Borland Pascal, Delphi, VBA
</small></li>
<li class="fragment"><b>-2003 University of New Mexico:
<span style="float:right;">Masters in Computer Science</span></b><br>
<small><a href="http://www.bcl.hamilton.ie/~barak/">B.Pearlmutter</a>.
SOBI/JADE ICA for single trial MEG. Favorite course: Data structures
and algorithms.
<a href="https://debian.org">Debian GNU/Linux</a>.
C, Matlab, shell. CVS
</small></li>
<li class="fragment"><b>-2009 Rutgers-Newark/NJIT:
<span style="float:right;">Ph.D. in Computer Science</span></b><br>
<small><a href="http://rubic-web.rutgers.edu/people.html">S.Hanson</a>.
<a href="http://dx.doi.org/10.1162/neco.2007.09-06-340">fMRI decoding (RFE SVM)</a>.
<a href="https://link.springer.com/article/10.1385/NI:2:1:071?noAccess=true">RUMBA</a>.
HPC sysadmin (cfengine, PBS).
<a href="https://www.fz-juelich.de/SharedDocs/Personen/INM/INM-7/EN/Hanke_m.html">M.Hanke</a>.
Debian pkg-exppsy (for FSL and PyEPL).
<a href="https://nm.debian.org/person/yoh/">Official Debian developer</a>.
<a href="https://arxiv.org/abs/1307.2150">fMRI/EEG (TRANS)fusion</a>.
<a href="http://pymvpa.org">PyMVPA</a>.
C++, Python. SVN, GIT. 1 wife, 3 kids
</small></li>
<li class="fragment"><b>- NOW Dartmouth College, PBS Department: <br>
<span style="float:right;">Postdoc, Scientist, Research Assistant/Associate Professor</span></b><br>
<small><a href="https://haxbylab.dartmouth.edu/">J.Haxby</a>.
<a href="http://pymvpa.org">PyMVPA</a>
(<a href="http://dx.doi.org/10.1016/j.neuron.2011.08.026">Hyperalignment</a>,
...),
<a href="https://neuro.debian.net">NeuroDebian</a>,
<a href="https://datalad.org">DataLad</a>,
<a href="https://repronim.org">ReproNim</a>,
<a href="https://dandiarchive.org">DANDI</a>,
...
<a href="https://github.com/yarikoptic/coop">Chicken Coop</a>,
...
<a href="https://centerforopenneuroscience.org/">CON</a>:
</small>
</li>
</ul>
<aside class="notes"><ul>
<li>Russian Soviet Federative Socialist Republic</li>
</ul></aside>
</section>
<section>
<div class="r-stack">
<img style="width:2000px" data-src="pics/con-webshot-20150812-front-up.png"/>
<!-- TODO update this compilation! prepend with proper Ack-->
<img class="fragment" style="width:1000px" data-src="pics/con-ack-bmbf.png"/>
<img class="fragment" data-src="pics/borrowed/con-webshot-20150812-front-down.png"/>
<img class="fragment" style="width:2000px" data-src="pics/con-principles.png"/>
<!-- <img class="fragment" style="width:500px"
data-src="pics/con-ack-joeybenetc-extended.svg"/> -->
</div>
</section>
</section>
<section data-transition="slide">
<section>
<h2> Integration & Trust Tiers</h2>
<ul style="font-size:120%">
<li> Social <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"><img style="height:2.5em;margin:0;" data-src="pics/repronim-logo-vertical.svg"/><img style="height:2.5em;margin:0;" data-src="pics/OBC_LogoCheck.svg"/></span></li>
<li> Data acquisition <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"> <img style="height:2.5em;margin:0;" data-src="pics/borrowed/reproin-logo.jpg"/></span></li>
<li> Methods/Analytics <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"> <img style="height:2.5em;margin:0;" data-src="pics/pymvpa_icon.png"/></span> </li>
<li> Software systems <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"> <img style="height:2.5em;margin:0;" data-src="pics/neurodebian_logo.svg"/></span> </li>
<li> Data management <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"> <img style="height:2.5em;margin:0;" data-src="pics/datalad_D.svg"/><img style="height:2.5em;margin:0;" data-src="pics/yoda.svg"/></span></li>
<li> Services <span style="vertical-align:middle;" class="fragment" data-fragment-index="2"> <img style="height:3em;margin:0;" data-src="pics/dandi-logo-square.svg"/></span></li>
</ul>
</section>
</section>
<section>
<section>
<h1>Trust is largely a social aspect</h1>
<h2>How do we convince ourselves (and others) that our
data/software/results are correct?</h2>
<h3>Hint: statistics is just a part of an answer</h3>
</section>
<section>
<h2>How do we convince ourselves?</h2>
<p style="font-size:120%">We share accountability with</p>
<ul style="font-size:120%">
<li>3rd-party: recursive definition<br> (hardware and software)</li>
<li>data collection/QA team</li>
<li>in-house developers&administrators team</li>
<li>analytic/scientists team</li>
</ul>
</section>
<section>
<h2>How can we minimize unexplained variance?</h2>
<h3>Noise ⊆ Unexplained variance</h3>
<ul style="font-size: 120%">
<li>minimize <b>human</b> repetitive/boring <b>IO</b> == <b>maximize human efficiency</b>: <!-- TODO -->
<ul style="font-size: 80%">
<li>automate data collection/harmonization - minimize human factor</li>
<li>strive to collect more auxiliary (meta)data - might be
"the variance"</li>
<li>standardize processing pipelines - minimize parametrization</li>
<li>provide efficient overviews/summaries - avoid "black box"</li>
</ul>
</li>
<li>encourage (and automate) <b>simulations</b>: software/data testing</li>
<li>encourage (and code-in) <b>assertions</b>: assumptions checking</li>
<li>facilitate <b>peer-review</b>: by team not only reviewers</li>
<li>facilitate <b>provenance</b>/accountability tracking</li>
<li>facilitate <b>re-use</b>: user testing</li>
<li><b>avoid blind trust</b> the 3rd party</li>
<li>to deliver to humans: provide high-level <b>guidelines</b></li>
</ul>
<aside class="notes">
<br>In the dark ages, we called "Magic" anything we could not explain.
<br>- Nowadays in neuroimaging we typically call it simply "Noise".
<br>- Minimization of human IO is not getting humans out of the loop
== higher efficiency
</pre>
</aside>
</section>
<section data-background-color="white">
<h2>3rd party?</h2>
<img style="width:1500px"
data-src="pics/god-is-at-the-computer.jpg"/>
<br>
<small>Unknown artist/origin, borrowed from <br><a href="http://blogs.quovantis.com/god-programmer/">http://blogs.quovantis.com/god-programmer/</a></small>
<br><br>
<h3>Reality: Most of the data is DERIVED data!</h3>
<aside class="notes">
<br>After all, Arthur C. Clarke said in 1962 "Any sufficiently advanced technology is indistinguishable from magic".
<br> so if not magicians -- must be gods who deliver us all the goods
</aside>
</section>
<section>
<h2>Including the one from ...</h2>
<img style="height:80%" data-src="pics/MRI-scanner.png"/>
</section>
<section>
<h2>Nuisance study: Conclusion on Phatom data</h2>
<!-- <img data-src="pics/f1000-webshot-20200930.png"/>-->
<span style="vertical-align:middle;">
<img style="height:550px" data-src="pics/f1000-webshot-20200930-fig2.png"/>
<img style="height:550px" data-src="pics/f1000-webshot-20200930-tab2.png"/>
</span>
<p><b>Variance in SNR through time in phantom QA data can <b>very well</b> (R2=0.53) be
explained by intrinsic factors</b></p>
<small>
Cheng CP and <b>Halchenko YO.</b> A new virtue of phantom MRI data: explaining variance in human participant data [version 1]. F1000Research 2020, 9:1131 <a href=https://doi.org/10.12688/f1000research.24544.1>https://doi.org/10.12688/f1000research.24544.1</a>
<br><a href="http://datasets.datalad.org/centerforopenneuroscience/nuisance/presentations/2020-NNL/">http://datasets.datalad.org/centerforopenneuroscience/nuisance/presentations/2020-NNL/</a>
contains a more detailed presentation</small>
</section>
<section>
<h2>Nuisance study: Hypothesis confirmed</h2>
<!-- <img data-src="pics/f1000-webshot-20200930.png"/>-->
<img style="height:550px" data-src="pics/f1000-webshot-20200930-fig3.png"/>
<img style="height:550px" data-src="pics/f1000-webshot-20200930-tab3.png"/>
<p><b>A proxy measure of <b>MRI scanner health</b>, such as SNR from phantom
data, can explain some variance in human data results</b></p>
<small>
Cheng CP and <b>Halchenko YO.</b> A new virtue of phantom MRI data: explaining variance in human participant data [version 1]. F1000Research 2020, 9:1131 <a href=https://doi.org/10.12688/f1000research.24544.1>https://doi.org/10.12688/f1000research.24544.1</a>
<br><a href="http://datasets.datalad.org/centerforopenneuroscience/nuisance/presentations/2020-NNL/">http://datasets.datalad.org/centerforopenneuroscience/nuisance/presentations/2020-NNL/</a>
contains a more detailed presentation</small>
</section>
<section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Take home
- *Conclusions*:
- More variance can be explained than what we thought
- *Work could be done together with data acquisition sites to*:
- **increase sensitivity of MRI scanner health *deterioration***
- **include phantom QA data in your normative databases**
- **provide phantom QA metrics so they could be conveniently<br> included
in the human studies QA/analyses**
- **improve automated screening/QA of human participants data**
- *Researchers can benefit from **guidelines**!*
Notes:
- guidelines related to data acquisition and beyond
- such guidelines also should strive to be "minimized" or "quantized"
</textarea></section>
<section data-background="pics/repronim-logo-vertical.svg" data-background-opacity="0.5" data-background-size="20%" data-background-position="right top">
<h2>Example 5-step guidelines from P41 ReproNim</h2>
<h3>Reproducible by Design</h3>
<a href="http://5steps.repronim.org"><img style="height: 900px" data-src="pics/repronim-5steps.png"/></a>
<aside class="notes"><ul>
<li>Let's follow the guidelines, and look at the first step of Study Design - what challenge it is trying to solve</li>
</ul></aside>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h2>Challenge: Human data re-use requires permission to share!</h2>
<img style="width:1200px" data-src="pics/borrowed/crcns-vim-1-takendown.png"/>
</section>
<section data-transition="slide">
<img style="width:400px" data-src="pics/OBC_LogoCheck.svg"/>
<br><a href="https://open-brain-consent.readthedocs.io/">open-brain-consent.readthedocs.io</a>
<br><br><small>Born in 2014</small>
<br><br><small>
<a href="https://onlinelibrary.wiley.com/doi/10.1002/hbm.25351">
E. Bannier, G. Barker, V. Borghesani, N. Broeckx, P. Clement, K. E. Emblem, S. Ghosh, E. Glerean, K. J.
Gorgolewski, M. Havu, <b>Y. O. Halchenko</b>, P. Herholz, A. Hespel, S. Heunis, Y. Hu, C.-P. Hu, D. Huijser,
M. I. Vayá, R. Jancalek, V. K. Katsaros, M.-L. Kieseler, C. Maumet, C. A. Moreau, H.-J. Mutsaerts,
R. Oostenveld, E. Ozturk-Isik, N. P. L. Espinosa, J. Pellman, C. R. Pernet, F. B. Pizzini, A. Š. Trbalić, P.-J.
Toussaint, M. V. di Oleggio Castello, F. Wang, C. Wang, and H. Zhu. The Open Brain Consent: Informing
research participants and obtaining consent to share brain imaging data. Human Brain Mapping, feb 2021.
doi: 10.1002/hbm.25351
</a></small>
</section>
<section>
<h3>Federally regulated but locally “managed”</h3>
<img style="width:1200px" data-src="pics/obc-main.png"/>
</section>
<section>
<img style="width:1200px" data-src="pics/obc-ultimate.png"/>
</section>
<section>
<img style="width:1200px" data-src="pics/obc-tools.png"/>
</section>
<section>
<h2>Outcomes</h2>
<ul>
<li>Over 20 contributors</li>
<li>8 translations of the Ultimate Form</li>
<li>12 translations of the GDPR Ultimate Form/DUA</li>
<li>More data can potentially be shared, <br>
more data potentially be re-used/fixed/more trusted</li>
<li>IRB Committees can use OBC instead of analyzing ad-hoc wording
<br>(benefit similar to Software licenses)</li>
<li>New related resource:
<br> <a href="https://osp.od.nih.gov/wp-content/uploads/Informed-Consent-Resource-for-Secondary-Research-with-Data-and-Biospecimens.pdf">NIH "Informed Consent for Secondary Research with Data and <br>
Biospecimens: Points to Consider and Sample Language for<br>
Future Use and/or Sharing (May 2022)."</a> </li>
</ul>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h2>Challenge: how to minimize human IO necessary to "understand"
data?</h2>
<h3 class="fragment">Answer: Standardize!</h3>
</section>
<section data-transition="slide">
<img style="width:2000px" data-src="pics/bids-logo-wide.png"/>
<br>
<small>
<a href="https://www.nature.com/articles/sdata201644">
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff,
E. P., Flandin, G., Ghosh, S. S., Glatard, T., <b>Halchenko, Y. O.</b>, Handwerker,
D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N.,
Nichols, T. E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V.,
Triplett, W., Turner, J. A., Varoquaux, G., and Poldrack, R. A. (2016). The
brain imaging data structure, a format for organizing and describing outputs
of neuroimaging experiments. Scientific Data, 3:160044</a>
</small>
</section>
<section>
<h2>Benefits from datasets standardization into BIDS</h2>
<ul>
<li><b>You have seen one BIDS dataset -- you have seen them
all!</b></li>
<li>BIDS is both human- and machine- friendly</li>
<ul>
<li>From 1.7.0 WiP to make BIDS specification itself machine
readable!</li>
<li>That would avoid necessity for hard-coding BIDS in
client software</li>
</ul>
<li>BIDS compliance could be automatically verified using
<a href="https://github.com/bids-standard/bids-validator">bids-validator</a>
BIDS-App</li>
<li><a href="https://bids-apps.neuroimaging.io/apps/">BIDS-apps</a> (such as mriqc, fmriprep, etc) provide a turnkey solution for BIDS datasets</li>
<li>PyBIDS, matlab-bids, etc. can assist scripting use of BIDS
datasets</li>
<li>... many more benefits ...</li>
<li><b>Everyone in neuroimaging should be encouraged to
collect/operate on data in BIDS format!</b></li>
<li><b>NIH intramural program would benefit from participation
and even steering in the development of community-driven
standards</b></li>
</ul>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h2>Challenge: conversion of data into BIDS could be painful</h2>
</section>
<section data-transition="slide">
<h2>ReproIn</h2>
<img style="width:500px"
data-src="pics/borrowed/reproin-logo.jpg"/>
<br><a href="https://reproin.repronim.org">reproin.repronim.org</a>
<br><br><small>Born in 2016</small>
</section>
<section>
<h2>ReproIn: BIDS at the scanner</h2>
<img style="width:2000px" data-src="pics/dbic-conversions-p1.png"/>
</section>
<section>
<h2>ReproIn: automatically converted into BIDS using HeuDiConv</h2>
<img style="width:2000px" data-src="pics/dbic-conversions-p2.png"/>
</section>
<section>
<h2>ReproIn: Benefits</h2>
<ul>
<li> Minimal single time investment of adhering to sequence
naming convention
<ul>
<li>convert at will</li>
<li>catch/fix problems with acquisition early
(bids-validator!)<br>
(no more <span style="color:red;font-style:italic;">"Half of my subjects lack X%-of-the-data"</span>)</li>
</ul>
<li> All datasets within center can be organized into a hierarchy reflecting<br>
hierarchy at the scanner console<br>
(no more <span style="color:red;font-style:italic;">"Where that long gone RA buried original data?"</span>)</li>
<li> Sidecar .json files in BIDS contain <emph>useful</emph> DICOM fields<br>
(no more of <span style="color:red;font-style:italic;">"I've lost the post-it with slice order"</span>)
<li> DICOM files are retained under <code>sourcedata/</code> <br>
(easy to re-convert if needed) </li>
<li>Optionally DataLad can be used to provide extra benefits</li>
</ul>
<p><b>Such reduction in human entry and data manipulation<br> through
automation and early validation increases trust <br>in correctness
of raw neuroimaging data!</b></p>
</section>
<section data-background="pics/magewell.jpg" data-background-opacity="0.3" data-background-size="500px" data-background-position="right">
<h2>Beyond ReproIn</h2>
<ul style="font-size: 110%">
<li><a href="https://github.com/ReproNim/reprostim/">ReproStim</a>
(prototype deployed
at <a href="https://www.dartmouth.edu/dbic">DBIC</a>):<br>
automate recording of <b>ALL</b> audio-video stimuli as
presented to participants:
<ul style="font-size: 80%">
<li>transparent to experimenter audio/video grabber</li>
<li>will automatically "slice" recordings into BIDS
datasets</li>
<li>near exact re-executability of any audio/video stimuli</li>
<li>imagine all of your neuroimaging data usable for
forward modeling/AI</li>
<li>already helped to recover randomization order for a
session</li>
</ul>
<li>ReproEvents (WiP): automate collection of events
<ul style="font-size: 80%">
<li>transparent to experimenter USB grabber</li>
<li>automatically populate BIDS _events.tsv files</li>
<li>accompany with support in stimuli software
(PsychoPy, PTB-3) <br>to provide synchronization cues and HED tags</li>
</ul>
</li>
<li><a href="https://github.com/con/noisseur">con/noisseur</a>
(Concept): automate verification of entered/displayed
information</li>
</ul>
<p><b>Automated (comes for free, trusted more) collection of auxiliary<br> data
types to make collected data more useful for others to
re-use.</b></p>
<note style="font-size: 80%">
<p>More on automation of data collection in webinar <br> "Reproducible Execution of Data
Collection/Processing". <br><a href="http://datasets.datalad.org/repronim/artwork/talks/webinar-2020-reprocomp/#/">Slides</a>
and <a href="https://youtu.be/dwBtrpI2iS0">Video</a> accessible from <a href="https://www.repronim.org/webinar-series.html">https://www.repronim.org/webinar-series.html</a>
</p>
</note>
<aside class="notes"><ul>
<li>
[1913 Webster]
The connoisseur is "one who knows," as opposed to the
dilettant, who only "thinks he knows." --Fairholt.
</li>
<li>From WordNet (r) 3.0 (2006) [wn]:
connoisseur
n 1: an expert able to appreciate a field; especially in the
fine arts [syn: {connoisseur}, {cognoscente}]
From Moby Thesaurus II by Grady Ward, 1.0 [moby-thesaurus]:
</li>
</ul></aside>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h2>Challenge 2007: No standard versatile framework for
analysis of neuroimaging data using Machine Learning methodologies</h2>
<h3>Secret Sauce: no code == no reproducibility == little trust</h3>
</section>
<section data-transition="slide">
<img style="width:2000px"
data-src="pics/pymvpa_logo_fromfusionposter.svg"/>
<br>
<br><br><small>Born in 2007</small>
<small>
<a href="http://dx.doi.org/10.1007/s12021-008-9041-y">
M. Hanke, <b>Y. O. Halchenko</b>, P. B. Sederberg, S. J. Hanson, J. V. Haxby, and S. Pollmann. PyMVPA:
A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7:37–53, 2009a.
doi: 10.1007/s12021-008-9041-y</a></small>
</section>
<section>
<h1>PyMVPA Features</h1>
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2664559/">
<img style="width:1200px" data-src="pics/pymvpa-features.png"/></a>
</section>
<!-- TODO -- move into notes to talk about on previous slide
<section data-markdown>
<textarea data-template>
## PyMVPA: Integration of Methods/Modalities
- **1st open source machine learning (ML) toolkit for neuroimaging data**
- Unified data structure (Dataset) for analysis of
- channel data (EEG/MEG)
- volumetric data (nibabel)
- surface data (based on AFNI)
- sample/feature attributes (conditions/coordinates)
- Unified (reversible) Mapper/Learner concepts:
- Dimensionality reduction: masking, SVD, etc.
- Searchlight-ing & <a href="http://www.franciscopereira.org/searchmight/">Searchmight-ing</a>
- <a href="http://dx.doi.org/10.1016/j.neuron.2011.08.026">Hyperalignments</a>
- Unified interfaces to external libraries
- libsvm
- shogun
- MDP
- scikit-learn
- R libraries (via RPy2)
</textarea>
</section>
-->
<section>
<div class="r-stack">
<img style="width:1500px" data-src="pics/pymvpa-classification.png"/>
<img class="fragment" style="width:1500px" data-src="pics/pymvpa-searchlight-step.png"/>
<img class="fragment" style="width:1500px" data-src="pics/pymvpa-searchlight.png"/>
</div>
</section>
<section>
<h2>Applicable across different modalities</h2>
<!-- <img style="width:1300px" data-src="pics/pymvpa-many-modalities.png"/>-->
<img style="width:1300px" data-src="pics/uniform_analysis.svg"/>
<small>
<a href="http://dx.doi.org/10.3389/neuro.11.003.2009">
M. Hanke†, <b>Y. O. Halchenko</b>† , P. B. Sederberg, E. Olivetti, I. Fründ, J. W. Rieger, C. S. Herrmann, J. V. Haxby, S. J. Hanson, and S. Pollmann. PyMVPA: A unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3, 2009b. doi: 10.3389/neuro.11.003.2009
</a>
</small>
</section>
<section>
<h2>And even for integrating across modalities</h2>
<h3>TRANSFusion: EEG fMRI mapping</h3>
<a href="https://arxiv.org/abs/1307.2150"><img style="width:1300px" data-src="pics/pymvpa-transfusion.png"/></a>
</section>
<section>
<h2>And others could take advantage of it:</h2>
<a href=""><img style="width:1300px" data-src="pics/pymvpa-studies-20160407.png"/></a>
</section>
<section>
<h3>Overall:</h3>
<ul>
<!-- <li>Unification of structures and interfaces allowed for -->
<li>Integration across methodologies, software implementations,
and data modalities</li>
<li>Guarded users against double-dipping regardless of the model complexity</li>
<li><b>Very extensive</b> unit-, example- and documentation testing
improved our and user trust in obtained results</li>
<li>Traveling trainer workshops (akin to AFNI schools)
facilitated training, adoption, and thus "user-testing"</li>
<li>Integration and re-use (instead of re-implementation) of
external functionality
<ul style="font-size: 90%">
<li>made PyMVPA project feasible on a "low-budget" of two
Ph.D. students</li>
<li>benefited upstream (nibabel, MDP, scikit-learn, scipy,
etc) projects</li>
<li>adoption of OpenfMRI layout (precursor to BIDS)
- generalizable analysis pipelines</li>
</ul>
</li>
</ul>
<p><b>It is efficient to offload modality/methodology
specifics to existing solutions, and powered by standards
establish a reusable automated (minimal user input) and
trustworthy analysis pipelines.</b></p>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h3>PyMVPA brought a challenge: Deployment</h3>
<p>"... The PyMVPA manual has a picture of a <b>dude performing pattern-classification on fMRI data with
his freaking cellphone. Awesome</b>. If you can do it on a cellphone,
then I'm set". </p>
<img data-src="pics/pymvpa_on_phone.jpg"/>
<div class="fragment">
<p>I have a computer (MAC). <br> Hours later, I'm wrestling with MAC OS (Leopard) ...</p>
</div>
<div class="fragment">
<p><b>PyMVPA follow up</b>: 12.5 hours to happy time</p>
<p>
<small>
<a href="http://kvaden.blogspot.com/2009/03/installing-pymvpa-on-leopard-mac-os.html">http://kvaden.blogspot.com/2009/03/installing-pymvpa-on-leopard-mac-os.html</a></small></p>
</div>
</section>
<section>
<a href="https://neuro.debian.net">
<img style="width:1200px"
data-src="pics/neurodebian_logo_web_banner.png"/></a>
<br><br><small>Born in 2009</small>
<br><small>
<a href="http://dx.doi.org/10.3389/fninf.2012.00022">
<b>Y. O. Halchenko</b>† and M. Hanke†. Open is not enough. let’s take the next step: An integrated, community-driven computing platform for neuroscience. Frontiers in Neuroinformatics, 6(00022), 2012. doi: 10.3389/fn-inf.2012.00022
</a></small>
</section>
<section>
<h2>Solution to system integration challenge:</h2>
<a href="https://neuro.debian.net">
<img style="width:1200px" data-src="pics/neurodebian-overview.png"/></a>
</section>
<section>
<h2>More detail on why and how:</h2>
<a href="">
<img style="width:1200px" data-src="pics/borrowed/HH12-webshot-20130331.png"/></a>
<small>
<br>
<a href="https://www.frontiersin.org/articles/10.3389/fninf.2012.00022/full">
<b>Halchenko, Y. O.</b> and Hanke, M. (2012). Open is not enough. Let’s take the
next step: An integrated, community-driven computing platform for
neuroscience. Frontiers in Neuroinformatics, 6(00022). PMC3458431</a>
</small>
</section>
<section>
<h2>NeuroDebian from user perspective:</h2>
<img style="width:1200px" data-src="pics/neurodebian-user.png"/>
</section>
<section>
<h2>Under-the-hood for PyMVPA user:</h2>
<img style="width:1200px" data-src="pics/neuropy_history.svg"/>
</section>
<section>
<h2>Under-the-hood for a NeuroDebian developer:</h2>
<img data-src="pics/nd_overview.svg"/>
</section>
<section>
<h2>Overall:</h2>
<h3>Integration of software projects within a distribution</h3>
<ul style="font-size:110%">
<li>greatly reduced user&developer burden in software
installations/maintenance</li>
<li>improved trust in correct operation</li>
<li>prepared projects for inclusion in other distributions
(e.g., Conda-Forge, Fedora, Gentoo) and/or offloading
maintenance to other teams (such as Debian-Med)</li>
<li>allowed to benefit from a <b>huge</b> community of open
source software enthusiasts and experts</li>
<li>helped to ensure adherence to legal
norms</li>
<li>facilitated collaboration, archival, experimentation, and
reproducibility</li>
<li><b>Seek inclusion of your developed software into FOSS
Distributions to maximize benefits and share burden and
accountability</b></li>
<li><b>Containerization then "comes for free"</b></li>
</ul>
</section>
</section>
<section>
<section data-background-gradient="radial-gradient(white, #f7dfd3)">
<h2>But it revealed a shortcoming:</h2>
<h3>Software platforms are not designed for managing data!</h3>
<ul style="font-size:120%">
<li>modularity is not as clearly defined <br>
(interest could be in specific group of files, videos-vs-images or humans-vs-objects, etc)</li>
<li>data is less volatile (lesser versions, only a few files change)</li>
<li>tarballs are inefficient</li>
<li>we cannot host copies of all data</li>
<li>cacophony of authentication schemes, interfaces, protocols</li>
<li>difficulty to share new or derived data</li>
</ul>
</section>
<section data-transition="slide">
<a href="https://datalad.org">
<img style="height:400px"
data-src="pics/datalad_logo_wide.gif"/></a>
<p style="margin-top:200px;font-size:150%">
A data management suite that makes data access and management as
easy as managing code and software!
</p>
<br><br><small>Born in 2013</small>
<small><a href="https://joss.theoj.org/papers/10.21105/joss.03262">
<b>Y. Halchenko</b>, K. Meyer, B. Poldrack, D. Solanky, A. Wagner, J. Gors, D. MacFarlane, D. Pustina, V. Sochat,
S. Ghosh, C. Mönch, C. Markiewicz, L. Waite, I. Shlyakhter, A. de la Vega, S. Hayashi, C. Häusler, J.-B.
Poline, T. Kadelka, K. Skytén, D. Jarecka, D. Kennedy, T. Strauss, M. Cieslak, P. Vavra, H.-I. Ioanas,
R. Schneider, M. Pflüger, J. Haxby, S. Eickhoff, and M. Hanke. DataLad: distributed system for joint
management of code, data, and their relationship. Journal of Open Source Software, 6(63):3262, jul 2021.
doi: 10.21105/joss.03262
</a></small>
</section>
<section data-transition="slide">
<h2>DataLad in one figure</h2>
<img data-src="pics/datalad_process_tuned/00base_preview.png">
</section>
<section>
<h2>Meet one of the "largest" Git "repositories"</h2>
<img style="" data-src="pics/datasets.datalad.org-20220313.png">
<p>over 6,000 Git repositories as "subdatasets", <br>
with access to almost 500TB of neural data</p>
</section>
<!--
<section>
<h2>An example in "files":</h2>
<img style="" height="800px" data-src="pics/virtual_dirtree.png">
<p>For management of computing containers with DataLad, see <a href="https://github.com/datalad/datalad-container/">datalad-container</a> extension, and <a href="https://github.com/ReproNim/containers/">ReproNim/containers</a> DataLad dataset.</p>
</section>
-->
<section>
<h2>
<a href="https://github.com/myyoda/myyoda">github.com/myyoda/myyoda</a>
</h2>
<img data-src="pics/repronim-containers-yoda-lower.png">
<br>
</section>
<!-- TODO: YODA slide -->
<section>
<h2>Data provenance capture</h2>
<img style="" data-src="pics/datalad_process_tuned/run_preview.png">
</section>
<!--
<section data-transition="None">
<h2>Provenance capture</h2>
<ul>
<li>Datasets can capture dataset <b>transformations</b> and their <b>cause</b> in order
to track the entire evolution and lineage of files in datasets</li>
</ul>
<img src="pics/w3cprov.png" width="700">
<ul>
<li>"How did this file came to be?",
"What steps were undertaken to transform the raw data into the published result?",
"Can you recompute this for me?"
</li>
</ul>
</section>
<section data-transition="None">
<h2>Provenance capture</h2>
<ul>
<li><b>Basic provenance</b>: DataLad can capture arbitrary dataset
transformations (e.g., from computing analysis results) and record
the cause of such a change
</li>
<pre><code class="bash" style="max-height:none">$ datalad run -m "Perform eye movement event detection"\
--input 'raw_data/*.tsv.gz' --output 'sub-*' \
bash code/compute_all.sh
-- Git commit -- Michael Hanke < ... @gmail.com>; Fri Sep 21 22:00:47 2019
[DATALAD RUNCMD] Perform eye movement event detection
=== Do not change lines below ===
{
"cmd": "bash code/compute_all.sh",
"dsid": "d2b4b72a-7c13-11e7-9f1f-a0369f7c647e",
"exit": 0,
"inputs": ["raw_data/*.tsv.gz"],
"outputs": ["sub-*"],
"pwd": "."
}
^^^ Do not change lines above ^^^
---
sub-01/sub-01_task-movie_run-1_events.png | 2 +-
sub-01/sub-01_task-movie_run-1_events.tsv | 2 +-
...</code></pre>
</ul>
</section>
-->
<section data-transition="None">
<h2>Provenance capture</h2>
<ul>
<li><b>Computational provenance</b>: Datasets can track <b>software containers</b> (see <a href="https://github.com/datalad/datalad-container/">datalad-container</a>),
and perform and record computations inside it:
</li>
<pre><code class="bash" style="max-height:none">$ datalad containers-run -n neuroimaging-container \
--input 'mri/*_bold.nii --output 'sub-*/LC_timeseries_run-*.csv' \
"bash -c 'for sub in sub-*; do for run in run-1 ... run-8;
do python3 code/extract_lc_timeseries.py \$sub \$run; done; done'"
-- Git commit -- Michael Hanke < ... @gmail.com>; Fri Jul 6 11:02:28 2019
[DATALAD RUNCMD] singularity exec --bind {pwd} .datalad/e...
=== Do not change lines below ===
{
"cmd": "singularity exec --bind {pwd} .datalad/environments/nilearn.simg bash..",
"dsid": "92ea1faa-632a-11e8-af29-a0369f7c647e",
"inputs": [
"mri/*.bold.nii.gz",
".datalad/environments/nilearn.simg"
],
"outputs": ["sub-*/LC_timeseries_run-*.csv"],
...
}
^^^ Do not change lines above ^^^
---
sub-01/LC_timeseries_run-1.csv | 1 +
...</code></pre>
</ul>
</section>
<section data-transition="None">
<h2>Provenance capture</h2>
<ul>
<li>All recorded transformations can be re-computed automatically</li>
<pre><code class="bash" style="max-height:none">$ datalad rerun eee1356bb7e8f921174e404c6df6aadcc1f158f0
[INFO] == Command start (output follows) =====
[INFO] == Command exit (modification check follows) =====
add(ok): sub-01/LC_timeseries_run-1.csv (file)
...
save(ok): . (dataset)
action summary:
add (ok: 45)
save (notneeded: 45, ok: 1)
unlock (notneeded: 45)
...</code></pre>
<ul>
<li>Aid with the reproducibility of a result and verify it (via content hash)</li>
<li>Use complete capture and automatic re-computation as alternative to storage and transport</li>
</li></li>
</ul>
</ul>
</section>
<section>
<h2>Extend DataLad</h2>
<p>Besides DataLad "core", there are extensions:
<ul>
<li>Separate Python packages, anybody can develop their own</li>
<li>Support for tailored solutions</li>
<li>Can provide additional commands, procedures, metadata extractors,
...</li>
<li>Available extensions
<ul>
<!-- <li><strong><a href="https://github.com/psychoinformatics-de/datalad-hirni">hirni</a></strong>: imaging raw data management/entry</li> -->
<li><strong><a href="https://github.com/datalad/datalad-container">containers</a></strong>: support for containerized computational environments</li>
<li><strong><a href="https://github.com/datalad/datalad-crawler">crawler</a></strong>: track web resources in automated data distributions</li>
<li><strong><a href="https://github.com/datalad/datalad-neuroimaging">neuroimaging</a></strong>: neuroimaging research data and workflow</li>
<!-- <li><strong><a href="https://github.com/datalad/datalad-webapp">webapp</a></strong>: REST API for querying/manipulating datasets</li> -->
<li><strong><a href="https://github.com/datalad/datalad-fuse">fuse</a></strong>: use FSSPEC to provide sparse on-demand access to data and<br> FUSE filesystem (on GNU/Linux)</li>
<li><strong><a href="https://github.com/datalad/datalad-osf">osf</a></strong>: interface with the Open Science Framework</li>
<li><strong><a href="https://github.com/datalad/datalad-ukbiobank">ukbiobank</a></strong>: work with the UKbiobank data</li>
<li><strong><a href="https://github.com/datalad/datalad-xnat">xnat</a></strong>: alternative to crawler to track XNAT projects</li>
</ul>
</li>
</ul>
<p>
<note style="font-size:80%">
<a href="http://handbook.datalad.org/en/latest/extension_pkgs.html">http://handbook.datalad.org/en/latest/extension_pkgs.html</a> - canonical list in handbook
<a href="http://docs.datalad.org/en/latest/customization.html#extension-packages">http://docs.datalad.org/en/latest/customization.html#extension-packages</a> - devel docs<br/>
<a href="https://github.com/datalad/datalad-extensions/">https://github.com/datalad/datalad-extensions/</a> - health status<br/>
</note>
</p>
</section>
<section data-background="pics/decentral_RDM_overview.png" data-background-opacity="0.8" data-background-size="30%" data-background-position="top 10px right 10px">
<h2>DataLad for users</h2>
<ul>
<li>minimized tedious human IO to obtain data</li>
<li>harmonized management of all human digital artifacts/data</li>
<li>facilitated modularity, integration and re-use</li>
<li>provided guarantees of data integrity</li>
<li>facilitated verification of data availability</li>
<li>enabled collection of annotated and re-executable
provenance</li>
<li>gave an awesome good night read: <a href="http://handbook.datalad.org/">handbook.datalad.org</a></li>
<li>hours of audio/video enrichment: <a href="https://youtube.com/c/datalad">youtube.com/c/datalad</a></li>
<li>... collaboration, etc ...</li>
</ul>
</section>
<section data-background="pics/tall-burger.png" data-background-opacity="0.2" data-background-size="80%">
<h2>DataLad for its developers:</h2>
<ul>
<li><b>Layering of technologies:</b>
<ul>
<li><b>Base technologies</b>: git, ssh, rsync, rsync, rclone, ...</li>
<li><b>Data logistics</b>: git-annex</li>
<li><b>Core product</b>: DataLad</li>
<li><b>Downstream projects</b>: DataLad extensions, OpenNeuro, CONP, ...</li>
</ul>