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<!doctype html>
<html lang="en">
<head>
<title>SciOps from ReproNim</title>
<meta name="description" content="Slides for the ReproNim Webinar
talk which walks through various
automation aspects of a Reproducible Neuroimaging study workflow">
<meta name="author" content=" Yaroslav O. Halchenko ">
<meta charset="utf-8">
<meta name="apple-mobile-web-app-capable" content="yes" />
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="stylesheet" href="css/main.css" id="theme">
<script src="js/printpdf.js"></script>
</head>
<body>
<div class="reveal">
<div class="slides">
<section>
<section>
<a href="http://centerforopenneuroscience.org/"><img data-src="pics/con-ccn-dartmouth-letterhead.svg"></a>
<h3>SciOps from ReproNim</h3>
<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>
<small><a href="https://fosstodon.org/@yarikoptic" target="_blank"><img data-src="pics/mastodon-icon.svg" style="height:30px;margin:0px;margin-right:0.1em" />@yarikoptic@fosstodon.org</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<br>
New Hampshire, USA</a></small>
<img style="width:200px; margin: 0px" data-src="pics/webinar-2024-reproflow-qrcode.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>
Live slides/<a href="https://github.com/ReproNim/webinar-2024-reproflow">Sources</a>: <a href="http://datasets.datalad.org/repronim/artwork/talks/webinar-2024-reproflow/#/">http://datasets.datalad.org/repronim/artwork/talks/webinar-2024-reproflow/</a>
<br>
<!-- <a href="http://pymvpa.org" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/pymvpa_icon.png"/></a> -->
<!-- <a href="http://repronim.org" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/repronim-logo-vertical.svg"/></a> -->
<a href="http://neuro.debian.net" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/neurodebian.png"/></a>
<a href="https://open-brain-consent.readthedocs.io" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/OBC_LogoCheck.svg"/></a>
<a href="http://datalad.org" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/datalad_logo_posters_banner.svg"/></a>
<a href="https://github.com/myyoda/myyoda" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/yoda.svg"/></a>
<a href="https://dandiarchive.org" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/dandi-logo-square.svg"/></a>
<a href="https://bids.neuroimaging.io/" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/BIDS_Logo.png"/></a>
<a href="https://standforukraine.com/" target="_blank"> <img style="height:140px;margin:2px" data-src="pics/Ukrainian_Blue-Yellow_ribbon.svg"/></a>
</small>
</section>
<section>
<h2>Acknowledgments</h2>
<a href="https://github.com/myyoda/poster/blob/master/ohbm2018.pdf" target="_blank"> <img style="height:150px;margin:20px" data-src="pics/yoda.svg"/></a>
<img style="height:150px;margin-right:50px" data-src="pics/nsf1.jpg" />
<img style="height:150px;margin-right:50px" data-src="pics/nih.png" />
<!-- <section data-background-iframe="https://centerforopenneuroscience.org/whoweare#john_wodder_" data-background-interactive> -->
<iframe src="https://centerforopenneuroscience.org/whoweare#john_wodder_" style="width:100%; height:600px;"></iframe>
</section>
</section>
<section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
<!-- .element: class="fragment" data-fragment-index="1" -->
<img style="height:650px;margin-bottom:30px" data-src="pics/ReproSpectrum.png"/>
<small>From <a href="https://repronim.wordpress.com/2017/09/20/whatrepro/">"What is Reproducibility"</a> by DN Kennedy.</small>
----
# Ultimate Goal/Approach
Reproducibility should become merely a *feature* <br/>(if not a
side-effect) of the *results*<br/><br/>
AKA "Reproducible by Design"
</textarea></section>
<section>
<section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
# But HOW?
----
## Introducing SciOps: Aim for Level 4
<!-- .slide: data-transition="fade" -->
<small>From <a href="https://arxiv.org/abs/2401.00077v1">"A Maturity Model for Operations in Neuroscience Research"</a>, 2023 by Erik C. Johnson, ..., Dimitri Yatsenko <a href="https://arxiv.org/abs/2401.00077v1">https://arxiv.org/abs/2401.00077v1</a></small>
----
## Level 4: Scalable — Introducing SciOps
<!-- .slide: data-transition="fade" -->
<div id="birch" style="position: absolute;
top: -200px;
right: -200px;
width: 500px;
height: 400px;">
<img src="pics/borrowed/CMM-Fig1.png"></img>
</div>
Levels 4 and 5 transform research operations by adopting technologies
for **automation, scalability, and efficient collaboration**. We
collectively refer to these methodologies as SciOps, aligning with the
principles of other successful “Ops” disciplines in the technology
industry: DevOps, DataOps, and MLOps.
<small>From <a href="https://arxiv.org/abs/2401.00077v1">"A Maturity Model for Operations in Neuroscience Research"</a>, 2023 by Erik C. Johnson, ..., Dimitri Yatsenko <a href="https://arxiv.org/abs/2401.00077v1">https://arxiv.org/abs/2401.00077v1</a></small>
----
# Where do we start?
<!-- .slide: data-transition="fade" -->
----
## HOWTO: Guiding principles
<!-- .slide: data-transition="fade" -->
- Be greedy
- get as much as possible (even if you think you don't need it ATM)
- Be lazy
- manually do as little as necessary
- Be not special
- do not come up with *your ways*
----
## HOWTO: Guiding principles
<!-- .slide: data-transition="fade" -->
- Be ~~greedy~~ thorough
- get as much as possible (even if you think you don't need it)
- know what you are going to do and what you have done:
- **automate provenance information collection**
- Be ~~lazy~~ efficient
- manually do as little as necessary
- **automate as much as feasible**
- achieve more than originally planned
- Be ~~not special~~ ~~academic~~ formal
- do not come up with *your ways*
- **use standardized approaches**
- become efficient collaborator
----
## One more: Pareto Principle
more: https://en.wikipedia.org/wiki/Pareto_principle
</textarea>
</section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
# What about a Recipe?
I was told that my Lasagna would be as great if I just follow the recipe... liers
<!-- .element: style="font-size:0.6em" -->
----
## Recipe of a Study
- **Ingredients**:
- humans (100% of *effort*)
- computers (0% of *effort*)
- language(s) (100% of the *result*)
- English/... (human -to- human)
- programming/scripting languages (human -to- computer)
- standards (human/computer -to- computer/human)
<!--
- **Steps**:
- **humans**: plan the study ahead
- **humans and computers**: do data collection/processing
-->
note:
- automate: use language(s) to make computers (not humans) do boring stuff
- human-to-human languages aren't good for automation
----
## ❮Languages-Rant❯
Part of the prior webinar "Reproducible Execution of Data Collection/Processing"
<small>
- Video: https://youtu.be/dwBtrpI2iS0
- Slides: https://datasets.datalad.org/repronim/artwork/talks/webinar-2020-reprocomp/#/4/2
</small>
### Main take away: Embrace standards
## ❮/Languages-Rant❯
----
## Recipe of a Study
- **Ingredients**:
- humans (100% of *effort*)
- computers (0% of *effort*)
- language(s) (100% of the *result*)
- English/... (human -to- human)
- programming/scripting languages (human -to- computer)
- standards (human/computer -to- computer/human)
- <!-- .element: class="fragment" data-fragment-index="2" -->
**Steps**:
- **humans**: plan the study ahead (**Science!**)
- **humans and computers**: do data collection/processing (**SciOps!**)
note:
- automate: use language(s) to make computers (not humans) do boring stuff
- human-to-human languages aren't good for automation
----
### Currently Dominant Recipe Effort Proportions
<!-- .slide: data-transition="fade" -->
<div id="pareto" style="position: absolute;
top: -300px;
right: -200px;
width: 400px;
height: 400px;">
<img src="pics/Paretos-80-20-rule.png"></img>
</div>
- **Steps**:
- **humans**: plan the study ahead (<20% effort)
- **(a good number of) humans and (some) computers**:<br/> do data collection/processing (>80% effort)
----
### Target Recipe Effort Proportions
<!-- .slide: data-transition="fade" -->
**Prove Pareto to be *wrong*** <!-- .element: style="color:#ff0000" -->
<br/>**and that we can avoid wasting our effort** <!-- .element: style="color:#ff0000" -->
- **Steps**:
- **humans**: plan the study ahead (>80% effort)
- **(some) humans and (many) computers**: <br>
automated data collection/processing (<20% effort)
<!-- .element: class="fragment" data-fragment-index="2" -->
----
### Recipe Steps&Ingredients for Planing Ahead (>80% effort)
----
### Humans: Plan Ahead (>80% effort)
- Plan to be ~~greedy~~ thorough
- plan for **all** [5 ReproNim steps](http://5steps.repronim.org)
(including *do-ing* analyses etc.)
- prepare to be (ab)used ([Halchenko&Hanke, 2015](http://dx.doi.org/10.1186/s13742-015-0072-7))
- be ~~lazy~~ **efficient** and learn from/(re)use work of others
- improve your study workflow SciOps
- check webinar from <a href="https://youtu.be/zFr8DyTGhxQ">Dec 2, 2022 by Keith Bush</a>
- **choose an RDM (Research Data Management) platform/approach**
- decide how to *log* what you will have done
- **aim to collect rich(er) datasets**
- Pre-register
- treat it as a checklist (now) and a "regression-test" (later)
- Prepare/train humans to "talk" to computers
- [ReproNim Training](https://www.repronim.org/teach.html)
- Listen to and/or participate in
[BrainHacks](https://brainhack.org/tutorials.html)
- [DataLad Handbook](http://handbook.datalad.org)
notes:
- nothing is "final" until ... virtually never
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
# Plan Ahead: Choose an RDM
----
----
### Plan Ahead: YODA
https://github.com/myyoda/poster/ <br/>by Michael Hanke (CP7, DataLad) et al.
----
### Plan Ahead: YODA's Layout
----
### Plan Ahead: YODA's Hierarchy
https://github.com/ReproNim/containers/
<!--
----
### Example: YODA's DataLad Reproducible Paper
http://handbook.datalad.org/en/latest/usecases/reproducible-paper.html <br/>by
ReproNim YODA master Adina Wagner, Michael Hanke, et al.
-->
----
### Fact: ~~No~~Everybody should care about YODA
https://fmriprep.org by Russ Poldrack (CP5*, OpenNeuro), et al.
----
### Plan Ahead: More on YODA via DataLad
http://handbook.datalad.org/en/latest/basics/basics-yoda.html
----
### Plan Ahead: More on DataLad and Its Automations
[](https://www.youtube.com/watch?v=Mkb7qpYaL7o)
<small>
[distribits 2024](https://distribits.live). Yaroslav O. Halchenko. "What's in the DataLad sandwich" AKA DataLad "ecosystem":
- Video: https://www.youtube.com/watch?v=Mkb7qpYaL7o
- Slides: https://datasets.datalad.org/centerforopenneuroscience/talks/2024-distribits-datalad.html
</small>
----
### Plan Ahead: DataLad or not but ...
- become friends with YODA and its principles
- choose an RDM platform/approach which
- tells you what you have done to obtain result X
- knows where you have or can get data Y (of exact version Z)
- **minimizes %effort** to re-run desired steps as-is or modified
- works well with your *analytics* platforms
- extra: allows you to search for data, results, etc
- to find other *nominally similar*
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
# Plan Ahead:<br/> aim to collect rich(er) datasets
----
## Plan Ahead: automate collection of any relevant (meta)data in standard form
Re *automate*:
- manual data entry/wrangling = hard to trace/fix data bugs
- we must be efficient:
- facilitate human/computer -to- computer/human interactions
- we are not unique: (ab)use existing solutions
- seek for longer term low %effort solutions
Re *any relevant*:
- of cause there is a trade-off
- prepare to be (ab)used
- ~~others~~ you could find (meta)data relevant to their study missing
- more of explained variance = higher power
- new explanations of "noise" regularly emerge
- have data ≠ have to analyze all data
Re *standard form*:
- without it - **high %effort** for a human/computer to understand it
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Plan Ahead: Collect DICOMs (not NIfTI, PAR/REC, ...)
- DICOMs contain lots of relevant metadata
- most of the contained metadata is not relevant to your study
- Conversion from DICOMs to BIDS could be automated
- You should be able to
- extract additional metadata happen you need it
- reconvert if the conversion tool was buggy
----
### Plan Ahead: HeuDiConv/ReproIn
- **HeuDiConv** (https://github.com/nipy/heudiconv)
- A flexible scriptable (Python) framework for conversion from
DICOMs into an arbitrary layout
- Uses [dcm2niix](https://github.com/rordenlab/dcm2niix/) by
ReproNim Guru Chris Rorden for basic DICOM -> NIfTIs conversion
- BIDS-aware and comes with a collection of conversion heuristics
----
### Plan Ahead: HeuDiConv/ReproIn
- **ReproIn** (https://github.com/repronim/reproin)
- A convention for organizing and naming sequences on the scanner
console
- BIDS-like
- **very low %effort to "adopt"**
- HeuDiConv heuristic to convert from such convention to BIDS
- Can be used to retroactively "map" into ReproIn (see <a
href="https://github.com/ReproNim/reproin/issues/18">reproin/issues/18</a>)
- Prototype <a
href="https://github.com/ReproNim/reproin/blob/master/bin/reproin">reproin</a>
script provides higher level functionality: study-create,
study-convert, study-show, ...
----
### CRON Job: reproin lists-update-study-shows
<small>
More about our setup at
https://github.com/ReproNim/reproin/tree/master?tab=readme-ov-file#complete-setup-at-dbic
</small>
----
### Plan Ahead: use ReproIn Convention
----
### ReproIn Convention: Use ReproIn-namer by Will Foran
<iframe src="https://npacore.github.io/reproin-namer/" style="width:100%; height:600px;"></iframe>
<small>
Use/contribute back: https://github.com/NPACore/reproin-namer
</small>
----
### Plan Ahead: ReproIn or not but automate conversion to BIDS!
- *Taking Control of your DICOM Data: ReproIn/Heudiconv Tools*
webinar https://www.repronim.org/webinar-series.html
- Consider using reproin helper to streamline handling of multiple studies
- If not ReproIn, consider
<!-- - https://github.com/psychoinformatics-de/datalad-hirni (Hanke, CP7) -->
- https://github.com/brainlife/ezbids (Pestilli, Brainlife, CP6*)
- https://bids.neuroimaging.io/benefits.html#converters ...
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Plan Ahead: Get your ~~greedy~~ thorough hands on Phantom QA Data
### Surprise: Phantom QA data can explain (some)<br/> variance in your data!<br/>
(Operation code-name: ReproPhantom (?))
----
### FYI: Study "Nuisance"
Cheng CP and Halchenko YO. A new virtue of phantom MRI data:
explaining variance in human participant data (v1; under peer
review). F1000Research 2020, 9:1131 <!-- .element: style="font-size:0.6em" -->
https://doi.org/10.12688/f1000research.24544.1
Full slide stack: <!-- .element: style="font-size:0.6em" -->
http://datasets.datalad.org/centerforopenneuroscience/nuisance/presentations/2020-NNL/
GitHub: <!-- .element: style="font-size:0.6em" --> https://github.com/proj-nuisance/nuisance
<!--
----
### Planned Ahead: (somewhat)
- Phantom Data: DBIC QA ([///dbic/QA](http://datasets.datalad.org/?dir=/dbic/QA))
- Human Data: 206 participants from studies of 3 PIs at DBIC
- DICOM-to-BIDS: [HeuDiConv](https://github.com/nipy/heudiconv/) with [ReproIn](https://github.com/repronim/reproin/) heuristic
- Base OS: [Debian GNU/Linux](http://debian.org) + [NeuroDebian](http://neuro.debian.net)
- QA: [MRIQC](https://github.com/poldracklab/mriqc) (BIDS-App)
- Morphometrics: ReproNim's ["Simple Workflow"](https://github.com/ReproNim/simple_workflow)
- [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki): BET, FAST, FIRST
- code/data/details: [10.12688/f1000research.10783.2](http://dx.doi.org/10.12688/f1000research.10783.2)
- Data wrangling and analyses:
- [Python](http://python.org/), [pandas](https://pandas.pydata.org/), [statsmodels](https://www.statsmodels.org/stable/index.html), [Jupyter notebooks](https://jupyter.org/)
- Containerization: [Singularity](https://singularity.lbl.gov)
- Version control/distribution:
- [DataLad](http://datalad.org), [datalad-container](http://handbook.datalad.org/en/latest/basics/101-133-containersrun.html?), [///ReproNim/containers](https://github.com/ReproNim/containers)
- Organization: follows [YODA principles](https://github.com/myyoda/poster/blob/master/ohbm2018.pdf)
- https://github.com/proj-nuisance/nuisance
-->
----
### What we know: Phantoms are good for scanner QA
<!-- .element: style="height: 500pt" -->
https://www.dartmouth.edu/dbic/research_infrastructure/qualityassurance.html
<!-- .element: style="font-size: 16pt" -->
and the variance is largely "noise", right?
----
### Model: Phantom SNR "explained"
----
### Model: Phantom SNR (variables)
----
### But why do we care about "noise?"
----
### Plan Ahead: Talk to your MR physicist/technician
- they better be doing QA
- do not discard phantom data - can come handy
- improve you power
- possibly help to harmonize across sites
- **ultra low %effort** to keep phantom data around
- **∞ %effort** to recover when deleted
- **small %effort** to make use of it
- concern: dates are "sensitive data"
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Plan Ahead: Physiological data
### Let's streamline acquisition of physiological data<br/>
(Operation code-name: ReproPhys)
----
### Others Planned it already: [phys2bids](https://github.com/physiopy/phys2bids/)
A nice overview: [OHBM 2020 poster](https://cdn-akamai.6connex.com/645/1827//phys2bids_OHBM_15922384856589877.pdf)
----
### Others Planned it already: [bidsphysio](https://github.com/cbinyu/bidsphysio)
HeuDiConv support PR: https://github.com/nipy/heudiconv/pull/446
----
### Plan Ahead: Consider collecting physiological data
- benefits are known
- improve power of your studies
- tools for conversion/slicing are available
- relatively high **%effort** at the moment to setup/do
- pales in comparison to other **%effort**s spent
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Plan Ahead: Automate collection of all related metadata (audio/video stimuli, events)
(Operation code-name: ReproFlow)
----
### ReproFlow: MRI Experiment *Flow
<!-- .slide: data-transition="fade" -->
----
### ReproFlow: Used Standards/Technologies
<!-- .slide: data-transition="fade" -->
----
### ReproFlow: Projects
<!-- .slide: data-transition="fade" -->
----
### ReproFlow: Projects
<!-- .slide: data-transition="fade" -->
<div id="birch" style="position: absolute;
top: -200px;
right: -200px;
width: 400px;
height: 400px;">
<img src="pics/reproflow-projects1.png"></img>
</div>
- **ReproStim** - capture audio and video stimuli
- **ReproEvents** - capture behavioral events from participants
- **Con/noisseur** - capture and QA operator input at the scanner console
- **ReproMon** - online operator feedback and alerts in case of incidents or anomalous metadata input
----
### Let's collect all video and audio stimuli as presented to the participants<br/>
(Operation code-name: ReproStim)
----
### But WHY/What For?
- QA (was there a jitter/dropped stimuli/randomization...)?
- make it possible to forward model **any** collected dataset
- *resting state folks - see previous sections*
- explain low level signal features (/confounds?)
- post-hoc salience features analysis
- **100% reproduce experiment stimulation at ≈0% effort**
----
### Plowing Ahead: https://github.com/ReproNim/reprostim
- Goal: **0% effort** for "clients"
- Minimal **%effort** to setup
- Fully seamless and automated after that
- HOW:
- [Video](https://www.amazon.com/gp/product/B00BLZDY6A/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&psc=1)/Audio splitters
- Video/Audio grabber, e.g. [Magewell USB Capture DVI Plus](https://www.amazon.com/gp/product/B01MSDFAO5/)
- A new video file upon connect/change of resolution<br>
(e.g., `2020.11.24.12.57.08_2020.11.24.15.51.23.mkv` <!-- .element: style="font-size:0.8em" -->)
- Synchronization:
- NTP where possible <br/>(stimuli delivery computer, video grabber, ...)
- video stream QR time-stamping and detection/decoding
- Automated data synchronization/transfer using <a href="">git-annex assistant</a>
- Target: Automated "slicing" of audio-video into BIDS datasets
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
### ReproFlow: Projects
<!-- .slide: data-transition="fade" -->
----
## Plan Ahead: Events description
### Automate collection of all events information in consistent machine-readable form<br/>
(Operation code-name: ReproEvents)
----
### ReproEvents: Two approaches for Current Design hardware
 <!-- .element: style="width:40%" -->
 <!-- .element: style="width:40%" -->
- MicroPython board+code: collect events on 932 or Birch via AUX Port
- Native Birch device "tune up"
<small>
**Disclaimer**: we have no affiliation or commercial interest in the
curdes business
</small>
----
### ReproEvents: Birch Response collector
<div id="birch" style="position: absolute;
top: -200px;
right: -200px;
width: 400px;
height: 400px;">
<img src="pics/borrowed/curdes-birch-1.png"></img>
</div>
- https://www.curdes.com/birch-optical-interface-unit.html
- Contains Raspberry PI running [RaspberryPi](https://wiki.debian.org/RaspberryPi)
- we installed NTP client to sync the clock
- Uses Python to run the GUI and record responses
- we patched to record events as JSON records:
 <!-- .element: style="width:130%" -->
----
### ReproFlow: Projects
<!-- .slide: data-transition="fade" -->
----
### Let's inform Experimenter about ongoing Repro-activities<br/>
(Operation code-name: ReproMon)
<!-- .slide: data-transition="fade" -->
----
### ReproMon: Monitor the humans and hardware
<!-- .slide: data-transition="fade" -->
Information/alert feedback for Researcher/MRI tech.
----
### ReproFlow: Take home summary
<div id="birch" style="position: absolute;
top: -200px;
right: -200px;
width: 400px;
height: 400px;">
<img src="pics/reproflow-projects1.png"></img>
</div>
- We are working on automation of collection of audio/video stimuli
and responses/events timing and alignment them into BIDS datasets
- Sample collation of data are available from https://github.com/ReproNim/reproflow-data-sync
- Many components are ready for deployment by enthusiasts
- Coming: simplified installation on (Neuro)Debian-based systems
- Visit our exhibit table and poster on ReproFlow at OHBM 2024
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Plan Ahead (the 80%): Summary
- Plan to be ~~greedy~~ thorough
- plan for **all** [5 ReproNim steps](http://5steps.repronim.org)
(including *do-ing* analyses etc.)
- be ~~lazy~~ efficient and (re)use work of others
- **choose an RDM for your SciOps**
- decide how to *log* what you will have done
- **aim to automate collection of rich(er) datasets**
- collect phantom data
- consider adopting elements of ReproFlow
- Be ~~not special~~ ~~academic~~ formal
- **learn about and reuse existing standards and study workflows**
</textarea></section></section>
<section><section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
# Reproducible Execution of Data Processing (the 20%)
Becomes more feasible with increased automation, <br/>
good RDM, and good (human- AND machine-) readable *logging*.
----
## The ultimate do it (the 20%)
- **(some) humans and (many) computers**: <br>
automated data collection/processing (<20% effort)
- data collection
- QA
- pre-processing
- analysis
- publication composition <!-- .element: class="fragment" data-fragment-index="2" -->
- also automate as much as possible by tuning prior stages
- keep in mind/plan for: <!-- .element: class="fragment" data-fragment-index="2" -->
- pipelines are great, but could be tricky. Use pre-crafted:
- MRIQC, fMRIPrep, C-PAC, ...
- having a small number of modular steps allow for "ad-hoc" pipelining (minimal script
etc)
- containers can help to collaborate/scale/debug/...
----
### SciOP: ReproYODA with [ReproNim/containers](https://github.com/ReproNim/containers/#a-typical-workflow)
<!-- .slide: data-transition="fade" -->
----
### SciOP: ReproYODA with [ReproNim/containers](https://github.com/ReproNim/containers/#a-typical-workflow)
<!-- .slide: data-transition="fade" -->
----
### ReproYODA Log
----
### Ultimately - many computers
*Version control your data and computation using containers, DataLad and ReproMan, and reproducible they be!* <br/> https://www.repronim.org/webinar-series.html
----
### More on ReproMan
[](https://www.youtube.com/watch?v=P0SOGt3xWmw)
<small>
[distribits 2024](https://distribits.live). Joe Wexler: fMRI Pipelines on HPC with DataLad and ReproMan
- Video: https://www.youtube.com/watch?v=P0SOGt3xWmw
</small>
</textarea>
<!-- forgot that iframes to github do not work!
----
### Welcome the ultimate YODA style OpenNeuroDerivatives from CP5* OpenNeuro
<iframe src="https://github.com/OpenNeuroDerivatives/"
style="width:100%; height:600px;"></iframe>
-->
</section>
<section data-transition="None">
<h3>
YODA style OpenNeuro Derivatives from CP5* OpenNeuro
</h3>
<small>
<center><p>Explore/obtain yourself from <a href="https://github.com/OpenNeuroDerivatives">https://github.com/OpenNeuroDerivatives</a></p></center>
</small>
<div style="position: relative; width: 100%; height: 80vh;">
<img src="pics/webshot-onderivs-listing.png" class="" width="80%" style="position: absolute; top: 0%; left: 0%" />
<img src="pics/webshot-onderivs-gitmodules.png" class="fragment fade-in" width="80%" style="position: absolute; top: 5%; left: 5%" />
<img src="pics/webshot-onderivs-repromanfolder.png" class="fragment fade-in" width="80%" style="position: absolute; top: 10%; left: 10%" />
<img src="pics/webshot-onderivs-commandarray.png" class="fragment fade-in" width="80%" style="position: absolute; top: 15%; left: 15%" />
<img src="pics/webshot-onderivs-stdout.png" class="fragment fade-in" width="80%" style="position: absolute; top: 20%; left: 20%" />
</div>
</section>
</section>
<section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Welcome https://github.com/con/duct
----
## duct as a runner within datalad containers-run
<div style="position: relative; width: 100%; height: 90vh;">
<img src="pics/screenshot-duct-diff.png" class="" width="80%" style="position: absolute; top: 0%; left: 0%" />
<img src="pics/screenshot-duct-1.png" class="fragment fade-in" width="100%" style="position: absolute; top: 0%; left: 0%" />
<img src="pics/screenshot-duct-2.png" class="fragment fade-in" width="100%" style="position: absolute; top: 25%; left: 0%" />
<img src="pics/screenshot-duct-3.png" class="fragment fade-in" width="100%" style="position: absolute; top: 15%; left: 10%" />
<img src="pics/screenshot-duct-4.png" class="fragment fade-in" width="100%" style="position: absolute; top: 60%; left: 10%" />
----
## Try duct for your SciOps!
</div>
</textarea>
</section>
<section data-markdown data-separator="^\n----\n" data-vertical="^\n---\n"><textarea data-template>
## Final Take Home Message
<!-- .slide: data-transition="fade" -->
### Embrace SciOps
- Be ~~greedy~~ thorough
- get as much as possible (even if you think you don't need it)
- know what you are going to do and what you have done:
- **automate provenance information collection**
- Be ~~lazy~~ efficient
- manually do as little as necessary
- **automate as much as feasible**
- achieve more than originally planned
- Be ~~not special~~ ~~academic~~ formal
- do not come up with *your ways*
- **use standardized approaches**
- become efficient collaborator
----
# Thank you!
## Establish your SciOps and
### Let the reproducibility be with you
<a href="https://github.com/myyoda/poster/blob/master/ohbm2018.pdf" target="_blank"> <img style="height:450px;margin:20px" data-src="pics/yoda.svg"/></a>
Slides: https://github.com/ReproNim/webinar-2024-reproflow
<!-- .element: style="font-size:0.6em" -->
</textarea></section>
</section>
</div> <!-- /.slides -->