1160 lines
44 KiB
HTML
1160 lines
44 KiB
HTML
<!doctype html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
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|
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<!-- Edit me start! -->
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<title>This is where your title goes</title>
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<meta name="description" content=" This is where you put a short description ">
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<meta name="author" content=" Your Name ">
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<!-- Edit me end! -->
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<link rel="stylesheet" href="../reveal.js/dist/reset.css">
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<link rel="stylesheet" href="../reveal.js/dist/reveal.css">
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<link rel="stylesheet" href="../reveal.js/dist/theme/beige.css">
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<!-- Theme used for syntax highlighted code -->
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<link rel="stylesheet" href="../reveal.js/plugin/highlight/monokai.css">
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</head>
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<body>
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<div class="reveal">
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<div class="slides">
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<section>
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<section>
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<script src="https://cdn.logwork.com/widget/countdown.js"></script>
|
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<a href="https://logwork.com/countdown-2zu8" class="countdown-timer"
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||
data-style="columns" data-timezone="Europe/Berlin" data-date="2022-04-22 11:00">
|
||
"Concepts & principles for reproducible science" starts in</a>
|
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</section>
|
||
<section>
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||
<h2>Concepts & principles for reproducible science</h2>
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||
<img height="500" src="../pics/Provenance_alpha.png">
|
||
<imgcredit>CC-BY-SA Scriberia and The Turing Way</imgcredit>
|
||
</section>
|
||
</section>
|
||
|
||
<!--YODA principles-->
|
||
|
||
<section>
|
||
<section>
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||
<h2>DataLad Datasets for data analysis</h2>
|
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|
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<ul style="font-size:30px">
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<li>A DataLad dataset can have <i>any</i> structure, and use as many or few
|
||
features of a dataset as required.</li>
|
||
|
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<li>However, for <b>data analyses</b> it is beneficial to make
|
||
use of DataLad features and structure datasets according to the <b>YODA principles</b>:</li>
|
||
</ul>
|
||
|
||
<img style="" data-src="../pics/yoda.png" height="400">
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<dl style="font-size:30px">
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<dt>P1: One thing, one dataset</dt>
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||
<dt>P2: Record where you got it from, and where it is now</dt>
|
||
<dt>P3: Record what you did to it, and with what</dt>
|
||
</dl>
|
||
<note>Find out more about the YODA principles in
|
||
<a href="http://handbook.datalad.org/en/latest/basics/101-127-yoda.html" target="_blank">
|
||
the handbook</a>, and more about structuring dataset at
|
||
<a href="https://psychoinformatics-de.github.io/rdm-course/02-structuring-data/index.html#example-structure-yoda-principles" target="_blank">
|
||
psychoinformatics-de.github.io/rdm-course/02-structuring-data</a>
|
||
</note>
|
||
</section>
|
||
|
||
<section data-markdown style="font-size:30px">
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## P1: One thing, one dataset
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- Create **modular** datasets: Whenever a particular collection of files could anyhow be useful in more
|
||
than one context (e.g. data), put them in their own dataset, and install it as
|
||
a subdataset.
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- Keep everything **structured**: Bundle all components of one analysis into one superdataset, and
|
||
within this dataset, separate code, data, output, execution environments.
|
||
- Keep a dataset **self-contained**, with relative paths in scripts to subdatasets or files.
|
||
Do not use absolute paths.
|
||
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Why Modularity?</h2>
|
||
|
||
<ul style="font-size:30px">
|
||
<li>1. Reuse and access management</li>
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||
<img src="../pics/ukb_datasets.svg" height="500px">
|
||
|
||
</li>
|
||
<li class="fragment fade-in" data-fragment-index="1">2. Scalability</li>
|
||
<pre class="fragment fade-in" data-fragment-index="1"><code class="fragment fade-in" data-fragment-index="1">adina@bulk1 in /ds/hcp/super on git:master❱ datalad status --annex -r
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||
15530572 annex'd files (77.9 TB recorded total size)
|
||
nothing to save, working tree clean</code></pre>
|
||
<small class="fragment fade-in" data-fragment-index="1"><a href="https://github.com/datalad-datasets/human-connectome-project-openaccess" target="_blank">(github.com/datalad-datasets/human-connectome-project-openaccess)</a></small>
|
||
</ul>
|
||
|
||
</section>
|
||
|
||
<section style="font-size:30px" data-transition="None">
|
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<h2>Why Modularity?</h2>
|
||
<ul>
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||
<li>3. Transparency</li><br>
|
||
|
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Original:
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<pre><code class="sh" style="max-height:none" data-trim>
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||
/dataset
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||
├── sample1
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│ └── a001.dat
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||
├── sample2
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│ └── a001.dat
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...
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</code></pre>
|
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<div class="fragment">
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Without modularity, after applied transform (preprocessing, analysis, ...):
|
||
<pre><code class="sh" style="max-height:none" data-trim>
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||
/dataset
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||
├── sample1
|
||
│ ├── ps34t.dat
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||
│ └── a001.dat
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||
├── sample2
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||
│ ├── ps34t.dat
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||
│ └── a001.dat
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||
...
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||
</code></pre>
|
||
Without expert/domain knowledge, no distinction between original and derived data
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||
possible.
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||
</div>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section style="font-size:30px" data-transition="None">
|
||
<h2>Why Modularity?</h2>
|
||
<ul>
|
||
<li>3. Transparency</li><br>
|
||
|
||
Original:
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||
<pre><code class="sh" style="max-height:none" data-trim>
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/raw_dataset
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||
├── sample1
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||
│ └── a001.dat
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||
├── sample2
|
||
│ └── a001.dat
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||
...
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||
</code></pre>
|
||
<strong>With modularity</strong> after applied transform (preprocessing, analysis, ...)
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||
<pre><code class="sh" style="max-height:none" data-trim>
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||
/derived_dataset
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||
├── sample1
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||
│ └── ps34t.dat
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├── sample2
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||
│ └── ps34t.dat
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||
├── ...
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||
└── inputs
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└── raw
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||
├── sample1
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||
│ └── a001.dat
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||
├── sample2
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||
│ └── a001.dat
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||
...
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</code></pre>
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||
Clearer separation of semantics, through use of pristine version of original dataset within a
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<em>new, additional</em> dataset holding the outputs.</ul>
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</section>
|
||
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||
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<section style="font-size:30px" data-transition="None" data-markdown><script type="text/template">
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||
## When to modularize?
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||
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- Target audience is different
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||
- public vs. private
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- domain specific vs. domain general
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||
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- Pace of evolution is different
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||
- "factual" raw data vs. choices of (pre-)processing
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||
- completed acquisition vs. ongoing study
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||
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||
- Size impacts I/O and logistics
|
||
- Git can struggle with 1M+ files
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||
- filesystems (licensing) can struggle with large numbers of inodes
|
||
- More infos: [Go Big or Go Home chapter](http://handbook.datalad.org/en/latest/beyond_basics/basics-scaling.html)
|
||
|
||
- Legal/Access constraints
|
||
- personal vs. anonymized data
|
||
|
||
<aside class="notes">
|
||
Note to self
|
||
</aside>
|
||
</script>
|
||
</section>
|
||
|
||
<section style="font-size:30px" data-markdown data-transition="None">
|
||
## P2: Record where you got it from, and where it is now
|
||
|
||
|
||
- **Link** individual datasets to declare data-dependencies (e.g. as subdatasets).
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||
- **Record data's origin** with appropriate commands, for example
|
||
to record access URLs for individual files obtained from (unstructured) sources "in the cloud".
|
||
- Share and **publish** datasets for collaboration or back-up.
|
||
|
||
</section>
|
||
|
||
|
||
<section data-transition="None" style="font-size:30px">
|
||
<h2>Dataset linkage</h2>
|
||
<img data-src="../pics/dataset_linkage.png">
|
||
<pre><code class="bash" style="font-size:115%;max-height:none">$ datalad clone --dataset . http://example.com/ds inputs/rawdata
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||
</code></pre>
|
||
|
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<pre><code class="diff" style="max-height:none">$ git diff HEAD~1
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diff --git a/.gitmodules b/.gitmodules
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||
new file mode 100644
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||
index 0000000..c3370ba
|
||
--- /dev/null
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||
+++ b/.gitmodules
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||
@@ -0,0 +1,3 @@
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+[submodule "inputs/rawdata"]
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+ path = inputs/rawdata
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||
+ url = http://example.com/importantds
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diff --git a/inputs/rawdata b/inputs/rawdata
|
||
new file mode 160000
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||
index 0000000..fabf852
|
||
--- /dev/null
|
||
+++ b/inputs/rawdata
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||
@@ -0,0 +1 @@
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||
+Subproject commit fabf8521130a13986bd6493cb33a70e580ce8572
|
||
</code></pre>
|
||
Each (sub)dataset is a separately, but jointly version-controlled entity.
|
||
If none of its data is retrieved, subdatasets are an extremely <strong>lightweight</strong> data dependency
|
||
and yet <strong>actionable</strong> (<strong>datalad get</strong> retrieves contents on demand)
|
||
<aside class="notes">weighs just a few bytes</aside>
|
||
</section>
|
||
|
||
<section data-transition="None" style="font-size:30px">
|
||
<h2>Example dataset structure</h2>
|
||
<img style="" height="750px" data-src="../pics/virtual_dirtree.png">
|
||
<p style="margin-top:-.5em">Link precisely versioned inputs to version-controlled outputs</p>
|
||
<aside class="notes">dataset linkage is pairwise, i.e. cheap</aside>
|
||
</section>
|
||
|
||
|
||
<section data-markdown style="font-size:30px">
|
||
## P3: Record what you did to it, and with what
|
||
|
||
|
||
- Collect and store **provenance** of all contents of a dataset that you create
|
||
- "Which script produced which output?", "From which data?", "In which **software environment**?"
|
||
... Record it in an ideally machine-readable way with **datalad (containers-)run**
|
||
|
||
</section>
|
||
</section>
|
||
|
||
<section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Sharing software environments: Why and how</h3>
|
||
|
||
<p style="font-size:30px"> Science has many different building blocks: Code, software, and data produce research outputs.
|
||
The more you share, the more likely can others reproduce your results <br></p>
|
||
<img height="750px" src="../pics/agoodstart.png">
|
||
</section>
|
||
|
||
|
||
|
||
<section data-transition="None">
|
||
<h3>Sharing software environments: Why and how</h3>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
Software can be difficult or impossible to install (e.g. conflicts with existing software,
|
||
or on HPC) for you or your collaborators
|
||
</li>
|
||
<li>
|
||
Different software versions/operating systems can produce different results:
|
||
<a href="https://doi.org/10.3389/fninf.2015.00012" target="_blank">Glatard et al., doi.org/10.3389/fninf.2015.00012</a>
|
||
</li>
|
||
<iframe width="1200" height="500" src="https://doi.org/10.3389/fninf.2015.00012"></iframe>
|
||
</ul>
|
||
</section>
|
||
|
||
<section>
|
||
<h3>Software containers</h3>
|
||
<ul style="font-size:30px">
|
||
<li class="fragment fade-in">
|
||
Software containers encapsulate a software environment and isolate it from
|
||
a surrounding operating system. Two common solutions: Docker, Singularity
|
||
</li>
|
||
<li class="fragment fade-in">
|
||
How familiar are you with software containers?
|
||
|
||
<iframe src="https://www.directpoll.com/r?XDbzPBd3ixYqg8huKIwKuJ7aj5lQw7fByQ4HgMgN",
|
||
style="border: 0", width="930", height="900"></iframe></li>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section>
|
||
<h2>Software containers</h2>
|
||
<ul style="font-size:30px">
|
||
<table>
|
||
<tr>
|
||
<td>
|
||
<img src="../pics/dockerexplain.png" height="500">
|
||
</td>
|
||
<td><img height="100" src="../pics/blog_docker.png"><br>
|
||
<img height="100" src="../pics/singularitylogo.jpg"> </td>
|
||
</tr>
|
||
|
||
</table>
|
||
</img>
|
||
<li>
|
||
Put simple, a cut-down virtual machine that is a portable and shareable
|
||
bundle of software libraries and their dependencies
|
||
</li>
|
||
<li><strong>Docker</strong> runs on all operating systems, but requires "sudo" (i.e., admin) privileges</li>
|
||
<li><strong>Singularity</strong> can run on computational clusters (no "sudo") but is not (well) on non-Linux</li>
|
||
<li>Their containers are different, but interoperable - e.g., Singularity can use and build Docker Images</li>
|
||
</ul>
|
||
</section>
|
||
|
||
<section>
|
||
<h2>The datalad-container extension</h2>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
The <code>datalad-container</code> extension gives DataLad commands to add, track, retrieve, and
|
||
execute Docker or Singularity containers.
|
||
</li>
|
||
<pre><code>pip/conda install datalad-container</code></pre>
|
||
<li>
|
||
If this extension is installed, DataLad can register software containers as "just another file" to your
|
||
dataset, and <strong>datalad containers-run</strong> analysis inside the container, capturing software as additional
|
||
provenance
|
||
</li>
|
||
</ul>
|
||
<img class="fragment fade-in" src="../pics/containers-run.svg" height="600"> <!-- .element: class="fragment" -->
|
||
</section>
|
||
|
||
</section>
|
||
<section>
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<small>Sadly, handling containerized analysis on the JupyterHub isn't possible
|
||
thus this is only a demonstration.
|
||
<br>
|
||
The code can be found at
|
||
<a href="https://github.com/datalad-handbook/course/blob/master/casts/uke-reproducibility" target="_blank">
|
||
github.com/datalad-handbook/course</a> </small><br><br>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<li>Step 2: Publish the BIDS-structured NIfTI images </li>
|
||
<li>Step 3: Reuse the NIfTI images in an analysis</li>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<pre><code style="max-height:None"># create a superdataset
|
||
$ datalad create -c text2git bids-data
|
||
[INFO ] Creating a new annex repo at /home/adina/bids-data
|
||
[INFO ] scanning for unlocked files (this may take some time)
|
||
[INFO ] Running procedure cfg_text2git
|
||
[INFO ] == Command start (output follows) =====
|
||
[INFO ] == Command exit (modification check follows) =====
|
||
create(ok): /home/adina/bids-data (dataset)
|
||
$ cd bids-data
|
||
# create a README
|
||
$ echo "# A BIDS structured dataset for my input data" > README.md
|
||
$ datalad status
|
||
untracked: README.md (file)
|
||
$ datalad save -m "Add a short README"
|
||
add(ok): README.md (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 1)
|
||
save (ok: 1)
|
||
# add the input data (DICOMs) as a subdataset
|
||
$ datalad clone --dataset . \
|
||
https://github.com/datalad/example-dicom-functional.git \
|
||
inputs/rawdata'
|
||
install(ok): inputs/rawdata (dataset)
|
||
add(ok): inputs/rawdata (file)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 3)
|
||
install (ok: 1)
|
||
save (ok: 2)</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<ul style="font-size:20px"><li><a href="https://github.com/nipy/heudiconv" target="_blank">
|
||
heudiconv</a> is a flexible DICOM converter that can do the job well.
|
||
It is part of a <a href="https://github.com/ReproNim/reproin" target="_blank">reproin</a>,
|
||
a framework for automatic DICOM to BIDS-dataset conversion.
|
||
reproin is part of a public container collection that can be installed as a subdataset:
|
||
</li></ul>
|
||
<pre><code style="max-height:None">$ datalad clone -d . \
|
||
https://github.com/ReproNim/containers.git \
|
||
code/containers
|
||
[INFO ] scanning for unlocked files (this may take some time)
|
||
[INFO ] Remote origin not usable by git-annex; setting annex-ignore
|
||
install(ok): code/containers (dataset)
|
||
add(ok): code/containers (file)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 3)
|
||
install (ok: 1)
|
||
save (ok: 2)
|
||
# list all available containers across the dataset hierarchy
|
||
$ datalad containers-list --recursive
|
||
[...]
|
||
code/containers/repronim-reproin -> code/containers/images/repronim/repronim-reproin--0.9.0.sing
|
||
[...]
|
||
# list the direct subdataset of bids-data
|
||
$ datalad subdatasets
|
||
subdataset(ok): code/containers (dataset)
|
||
subdataset(ok): inputs/rawdata (dataset)
|
||
# use datalad containers-run to run the conversion and save its provenance
|
||
$ datalad containers-run -m "Convert subject 02 to BIDS" \
|
||
--container-name code/containers/repronim-reproin \
|
||
--input inputs/rawdata/dicoms \
|
||
--output sub-02 \
|
||
"-f reproin -s 02 --bids -l "" --minmeta -o . --files inputs/rawdata/dicoms"
|
||
[...]
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 18)
|
||
get (notneeded: 4, ok: 1)
|
||
save (notneeded: 2, ok: 1)
|
||
</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<li>Step 2: Publish the BIDS-structured NIfTI images </li>
|
||
<pre><code style="max-height:None">$ datalad siblings add -d . \
|
||
--name gin \
|
||
--url git@gin.g-node.org:/adswa/bids-data.git'
|
||
$ datalad siblings
|
||
.: here(+) [git]
|
||
[WARNING] Could not detect whether gin carries an annex. If gin is a pure Git remote, this is expected.
|
||
.: gin(-) [git@gin.g-node.org:/adswa/bids-data.git (git)]
|
||
$ datalad push --to gin
|
||
copy(ok): sourcedata/sub-02/func/sub-02_task-oneback_run-01_bold.dicom.tgz (file) [to gin...]
|
||
copy(ok): sub-02/func/sub-02_task-oneback_run-01_bold.nii.gz (file) [to gin...]
|
||
publish(ok): . (dataset) [refs/heads/git-annex->gin:refs/heads/git-annex 68523d8..b4c1ee0]
|
||
publish(ok): . (dataset) [refs/heads/master->gin:refs/heads/master [new branch]]
|
||
action summary:
|
||
copy (ok: 2)
|
||
publish (ok: 2)
|
||
</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<li>Step 2: Publish the BIDS-structured NIfTI images </li>
|
||
<li>Step 3: Reuse the NIfTI images in an analysis</li>
|
||
<pre><code style="max-height:None">$ cd ../
|
||
# create a new dataset for your analysis. The yoda procedure pre-structures it
|
||
# and applies configurations that ensure that scripts are versioned in Git
|
||
$ datalad create -c yoda myanalysis
|
||
[INFO ] Creating a new annex repo at /home/adina/myanalysis
|
||
[INFO ] scanning for unlocked files (this may take some time)
|
||
[INFO ] Running procedure cfg_yoda
|
||
[INFO ] == Command start (output follows) =====
|
||
[INFO ] == Command exit (modification check follows) =====
|
||
create(ok): /home/adina/myanalysis (dataset)
|
||
$ cd myanalysis
|
||
$ tree
|
||
.
|
||
├── CHANGELOG.md
|
||
├── code
|
||
│ └── README.md
|
||
└── README.md
|
||
|
||
1 directory, 3 file
|
||
# add the BIDS-structured data as input - in the form of a subdataset
|
||
$ datalad clone -d . \
|
||
https://gin.g-node.org/adswa/bids-data \
|
||
input
|
||
[INFO ] scanning for unlocked files (this may take some time)
|
||
install(ok): input (dataset)
|
||
add(ok): input (file)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
add(ok): .gitmodules (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 3)
|
||
install (ok: 1)
|
||
save (ok: 2)</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<li>Step 2: Publish the BIDS-structured NIfTI images </li>
|
||
<li>Step 3: Reuse the NIfTI images in an analysis</li>
|
||
<pre><code style="max-height:None"># Get a script for the analysis
|
||
$ datalad download-url -m "Download code for brain masking from Github" \
|
||
-O code/get_brainmask.py \
|
||
https://raw.githubusercontent.com/datalad-handbook/resources/master/get_brainmask.py'
|
||
[INFO ] Downloading 'https://raw.githubusercontent.com/datalad-handbook/...
|
||
https://raw.githubusercontent.com/datalad-handbook/resources/master/get_brainmask.py:
|
||
download_url(ok): /home/adina/myanalysis/code/get_brainmask.py (file)
|
||
add(ok): code/get_brainmask.py (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 1)
|
||
download_url (ok: 1)
|
||
save (ok: 1)
|
||
# Add a container with all relevant Python software
|
||
$ datalad containers-add nilearn \
|
||
--url shub://adswa/nilearn-container:latest \
|
||
--call-fmt "singularity exec {img} {cmd}"
|
||
[INFO ] Initiating special remote datalad
|
||
add(ok): .datalad/config (file)
|
||
save(ok): . (dataset)
|
||
containers_add(ok): /home/adina/myanalysis/.datalad/environments/nilearn/image (file)
|
||
action summary:
|
||
add (ok: 1)
|
||
containers_add (ok: 1)
|
||
save (ok: 1)
|
||
# run your containerized analysis reproducibly
|
||
$ datalad containers-run -m "Compute brain mask" \
|
||
-n nilearn \
|
||
--input input/sub-02/func/sub-02_task-oneback_run-01_bold.nii.gz \
|
||
--output figures/ \
|
||
--output "sub-02*" \
|
||
"python code/get_brainmask.py"'
|
||
[INFO ] Making sure inputs are available (this may take some time)
|
||
get(ok): input/sub-02/func/sub-02_task-oneback_run-01_bold.nii.gz (file) [from origin...]
|
||
[INFO ] == Command start (output follows) =====
|
||
[INFO ] == Command exit (modification check follows) =====
|
||
add(ok): figures/sub-02_brainmask.png (file)
|
||
add(ok): figures/sub-02_mean-epi.png (file)
|
||
add(ok): sub-02_brain-mask.nii.gz (file)
|
||
save(ok): . (dataset)
|
||
action summary:
|
||
add (ok: 3)
|
||
get (notneeded: 2, ok: 1)
|
||
save (notneeded: 1, ok: 1)</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h3>Reproducible analysis: From DICOMs to brain masks</h3>
|
||
<ul style="font-size: 30px">
|
||
<li>Step 1: Convert DICOMs to BIDS-structured NIfTI images</li>
|
||
<li>Step 2: Publish the BIDS-structured NIfTI images </li>
|
||
<li>Step 3: Reuse the NIfTI images in an analysis</li>
|
||
<pre><code style="max-height:None"># Ask your results how they came to be
|
||
$ git log sub-02_brain-mask.nii.gz
|
||
commit d2d35eb31a93a0a82163835de0e3c14946504811 (HEAD -> master)
|
||
Author: Adina Wagner <adina.wagner@t-online.de>
|
||
Date: Wed Apr 20 16:05:40 2022 +0200
|
||
|
||
[DATALAD RUNCMD] Compute brain mask
|
||
|
||
=== Do not change lines below ===
|
||
{
|
||
"chain": [],
|
||
"cmd": "singularity exec .datalad/environments/nilearn/image python code/get_brainmask.py",
|
||
"dsid": "421d677c-2873-49f0-a1a9-9c7bb0100e69",
|
||
"exit": 0,
|
||
"extra_inputs": [
|
||
".datalad/environments/nilearn/image"
|
||
],
|
||
"inputs": [
|
||
"input/sub-02/func/sub-02_task-oneback_run-01_bold.nii.gz"
|
||
],
|
||
"outputs": [
|
||
"figures/",
|
||
"sub-02*"
|
||
],
|
||
"pwd": "."
|
||
}
|
||
^^^ Do not change lines above ^^^
|
||
# ... or recompute them
|
||
$ datalad rerun
|
||
</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section>
|
||
<h3>Summary - Reproducible analysis</h3>
|
||
|
||
<ul style="font-size:30px">
|
||
<dt class="fragment fade-in"><code>datalad run</code> records a command and
|
||
its impact on the dataset.</dt>
|
||
<dd class="fragment fade-in">Data/directories specified as <code>--input</code>
|
||
are retrieved prior to command execution, Data/directories specified as <code>--output</code>
|
||
will be unlocked for modifications prior to a rerun of the command.</dd>
|
||
<br>
|
||
<dt class="fragment fade-in"><code>datalad containers-run</code> from the datalad-container
|
||
extends <code>datalad run</code> & can be used
|
||
to capture the software environment as provenance.</dt>
|
||
<dd class="fragment fade-in">Its ensures computations are ran in the desired software set up
|
||
and supports Docker and Singularity containers</dd>
|
||
<br>
|
||
<dt class="fragment fade-in">Modular dataset hierarchies ensure transparency, easier access management, and reusability</dt>
|
||
<dd class="fragment fade-in">To install a dataset into an existing dataset as a subdataset, use <strong>datalad clone -d . [URL]</strong></dd>
|
||
<br>
|
||
<dt class="fragment fade-in">The YODA procedure pre-structures and configures datasets in a way that aids reproducibility</dt>
|
||
<dd class="fragment fade-in"><strong>datalad create -c yoda newdataset</strong> applies it directory during creation</dd>
|
||
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>General reproducibility checklist (Hinsen, 2020)</h2>
|
||
<small><a href="https://www.nature.com/articles/d41586-020-02462-7" target="_blank">
|
||
https://www.nature.com/articles/d41586-020-02462-7
|
||
</a> </small>
|
||
<dl style="font-size:30px">
|
||
<dt>
|
||
Use code/scripts
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Workflows based on point-and-click interfaces (e.g. Excel), are
|
||
not reproducible. Enshrine computations and data manipulation in code.
|
||
</dl>
|
||
<dt>
|
||
Document
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Use comments, computational notebooks and README files to explain
|
||
how your code works, and to define the expected parameters and the
|
||
computational environment required.
|
||
|
||
</dl>
|
||
<dt>
|
||
Record
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Make a note of key parameters, e.g. ‘seed’ values used to start a
|
||
random-number generator.
|
||
</dl>
|
||
<dt>
|
||
Test
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Create a suite of test functions. Use positive and negative control
|
||
data sets to ensure you get the expected results, and run those tests
|
||
throughout development to squash bugs as they arise.
|
||
</dl>
|
||
<dt>
|
||
Guide
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Create a master script (for example, a ‘run.sh’ file or a Makefile) that downloads
|
||
required data sets and variables, executes your workflow and provides
|
||
an obvious entry point to the code.
|
||
</dl>
|
||
</dl>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>General reproducibility checklist (Hinsen, 2020)</h2>
|
||
<small><a href="https://www.nature.com/articles/d41586-020-02462-7" target="_blank">
|
||
https://www.nature.com/articles/d41586-020-02462-7
|
||
</a> </small>
|
||
<dl style="font-size:30px">
|
||
<dt>
|
||
Archive
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
GitHub is a popular but impermanent online repository. Archiving
|
||
services such as Zenodo, Figshare and Software Heritage promise
|
||
long-term stability.
|
||
</dl>
|
||
<dt>
|
||
Track
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Use version-control tools such as Git to record your project’s history.
|
||
Note which version you used to create each result.
|
||
</dl>
|
||
<dt>
|
||
Package
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Create ready-to-use computational environments using containerization
|
||
tools (for example, Docker, Singularity), web services (Code Ocean,
|
||
Gigantum, Binder) or virtual-environment managers (Conda).
|
||
</dl>
|
||
<dt>
|
||
Automate
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Use continuous-integration services (for example, Travis CI) to
|
||
automatically test your code over time, and in various computational environments
|
||
</dl>
|
||
<dt>
|
||
Simplify
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Avoid niche or hard-to-install third-party code libraries that can complicate reuse.
|
||
</dl>
|
||
<dt>
|
||
Verify
|
||
</dt>
|
||
<dl class="fragment fade-in">
|
||
Check your code’s portability by running it in a range of computing environments.
|
||
</dl>
|
||
</dl>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<dl>
|
||
<small>
|
||
<dl>
|
||
<dt>
|
||
Use code/scripts
|
||
</dt>
|
||
<dl>
|
||
Workflows based on point-and-click interfaces (e.g. Excel), are
|
||
not reproducible. Enshrine computations and data manipulation in code.
|
||
</dl>
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<ul style="font-size:30px">
|
||
<li>First: YES! Very much so!</li>
|
||
<li class="fragment fade-in">But if your workflow includes interactive
|
||
code sessions, and you want to at least save the results, you could do
|
||
<pre><code>datalad run ipython/R/matlab/...</code></pre></li>
|
||
<li class="fragment fade-in">Once you close the interactive session,
|
||
every result you created would be saved (although with crappy provenance)</li>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<dl>
|
||
<small>
|
||
<dt>
|
||
Document
|
||
</dt>
|
||
<dl>
|
||
Use comments, computational notebooks and README files to explain
|
||
how your code works, and to define the expected parameters and the
|
||
computational environment required.
|
||
|
||
</dl>
|
||
<dt>
|
||
Record
|
||
</dt>
|
||
<dl>
|
||
Make a note of key parameters, e.g. ‘seed’ values used to start a
|
||
random-number generator.
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
Commit messages and run records can do this for you, and are a useful basis
|
||
to extend upon with "documentation for humans" such as READMEs
|
||
</li>
|
||
<li>
|
||
If you create datasets using <strong>datalad create -c yoda ... </strong>
|
||
the YODA procedure automatically populates your repository with README
|
||
files to nudge you into using them (and makes sure that code is versioned with Git).
|
||
</li>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<dl>
|
||
<small>
|
||
<dt>
|
||
Test
|
||
</dt>
|
||
<dl>
|
||
Create a suite of test functions. Use positive and negative control
|
||
data sets to ensure you get the expected results, and run those tests
|
||
throughout development to squash bugs as they arise.
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<ul style="font-size:30px">
|
||
<li>There is an excellent
|
||
<a href="https://the-turing-way.netlify.app/reproducible-research/testing.html" target="_blank">
|
||
Turing Way chapter about it</a>
|
||
</li>
|
||
<li class="fragment fade-in">
|
||
Because annexed files are stored by their content identity hash,
|
||
if any change in your pipeline/workflow produces a changed results,
|
||
the version control software will be able to tell you
|
||
</li>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<dl>
|
||
<small>
|
||
<dt>
|
||
Guide
|
||
</dt>
|
||
<dl>
|
||
Create a master script (for example, a ‘run.sh’ file) that downloads
|
||
required data sets and variables, executes your workflow and provides
|
||
an obvious entry point to the code.
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<ul style="font-size:30px">
|
||
<li class="fragment fade-in">
|
||
A well-made run record can do this, or at least help
|
||
</li>
|
||
<li class="fragment fade-in">
|
||
Makefiles are also great. A tutorial for a reproducible paper
|
||
using Makefiles is in
|
||
<a href="https://github.com/datalad-handbook/repro-paper-sketch/" target="_blank">
|
||
github.com/datalad-handbook/repro-paper-sketch/</a>
|
||
</li>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<small>
|
||
<dl>
|
||
<dt>
|
||
Archive
|
||
</dt>
|
||
<dl>
|
||
Archiving services such as Zenodo, Figshare and Software Heritage promise
|
||
long-term stability.
|
||
</dl>
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<p style="font-size:30px">You can archive a dataset to figshare? <br>
|
||
If you have a Figshare account, you can do the following:
|
||
<pre><code class="bash" style="max-height:none">$ datalad export-to-figshare
|
||
[INFO ] Exporting current tree as an archive under /tmp/comics since figshare does not support directories
|
||
[INFO ] Uploading /tmp/comics/datalad_ce82ff1f-e2b3-4a84-9e56-87d8eb6e5b27.zip to figshare
|
||
Article
|
||
Would you like to create a new article to upload to? If not - we will list existing articles (choices: yes, no): yes
|
||
|
||
New article
|
||
Please enter the title (must be at least 3 characters long). [comics#ce82ff1f-e2b3-4a84-9e56-87d8eb6e5b27]: acomictest
|
||
|
||
[INFO ] Created a new (private) article 13247186 at https://figshare.com/account/articles/13247186. Please visit it, enter additional meta-data and make public
|
||
[INFO ] 'Registering' /tmp/comics/datalad_ce82ff1f-e2b3-4a84-9e56-87d8eb6e5b27.zip within annex
|
||
[INFO ] Adding URL https://ndownloader.figshare.com/files/25509824 for it
|
||
[INFO ] Registering links back for the content of the archive
|
||
[INFO ] Adding content of the archive /tmp/comics/datalad_ce82ff1f-e2b3-4a84-9e56-87d8eb6e5b27.zip into annex AnnexRepo(/tmp/comics)
|
||
[INFO ] Initiating special remote datalad-archives
|
||
[INFO ] Finished adding /tmp/comics/datalad_ce82ff1f-e2b3-4a84-9e56-87d8eb6e5b27.zip: Files processed: 4, removed: 4, +git: 2, +annex: 2
|
||
[INFO ] Removing generated and now registered in annex archive
|
||
export_to_figshare(ok): Dataset(/tmp/comics) [Published archive https://ndownloader.figshare.com/files/25509824]
|
||
</code></pre></p>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know ...</h2>
|
||
|
||
<img src="../pics/figshare.png">
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>Did you know...</h2>
|
||
<dl>
|
||
<small>
|
||
<dt>
|
||
Package
|
||
</dt>
|
||
<dl>
|
||
Create ready-to-use computational environments using containerization
|
||
tools (for example, Docker, Singularity), web services (Code Ocean,
|
||
Gigantum, Binder) or virtual-environment managers (Conda).
|
||
</dl>
|
||
</small>
|
||
<br><br>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
The <code>datalad-container</code> extension can help to use and share software
|
||
environments in your dataset
|
||
</li>
|
||
<li><a href="https://github.com/repronim/containers" target="_blank">
|
||
github.com/repronim/containers</a> is a public DataLad dataset with access to dozens of commonly used
|
||
containerized neuroimaging software
|
||
</li>
|
||
</ul>
|
||
</dl>
|
||
</section>
|
||
|
||
<section>
|
||
<h2>Did you know...</h2>
|
||
<ul style="font-size:30px">
|
||
Helpful resources for working with software containers:
|
||
<li>
|
||
<a href="https://github.com/jupyterhub/repo2docker" target="_blank">
|
||
repo2docker</a> can fetch a Git repository/DataLad dataset and builds
|
||
a container image from configuration files
|
||
</li>
|
||
<li>
|
||
<a href="https://github.com/ReproNim/neurodocker" target="_blank">
|
||
neurodocker</a> can generate custom Dockerfiles and Singularity recipes
|
||
for neuroimaging.
|
||
</a>
|
||
</li>
|
||
<li>
|
||
<a href="https://github.com/repronim/containers" target="_blank">
|
||
The ReproNim container collection</a>, a DataLad dataset that
|
||
includes common neuroimaging software as configured singularity containers.
|
||
</li>
|
||
<li>
|
||
<a href="https://github.com/rocker-org/rocker" target="_blank">
|
||
rocker</a> - Docker container for R users
|
||
</li>
|
||
</ul>
|
||
</section>
|
||
|
||
<section style="font-size:30px">
|
||
<h2>Summary</h2>
|
||
Where can DataLad help?
|
||
<table>
|
||
<tr>
|
||
<td>
|
||
<img src="../pics/turingway/ReproducibleDefinitionGrid.png">
|
||
<imgcredit>Illustration by Scriberia and The Turing Way</imgcredit>
|
||
</td>
|
||
<td>
|
||
<table style="font-size:30px">
|
||
<tr >
|
||
<b>Reproducible</b><br>
|
||
automatic recompute <br>
|
||
and identity checks<br>
|
||
<b>Replicable</b><br>
|
||
Easily exchange <br>
|
||
input data<br>
|
||
<b>Robust</b><br>
|
||
Reuse data & change<br>
|
||
code, update paper <br>
|
||
<b>Generalisable</b><br>
|
||
Share analysis in an<br>
|
||
easily reusable and<br>
|
||
adaptable framework
|
||
</tr>
|
||
</table>
|
||
|
||
</td>
|
||
</tr>
|
||
</table>
|
||
|
||
</section>
|
||
|
||
<section>
|
||
<h2>Questions!</h2>
|
||
<iframe src="https://www.directpoll.com/r?XDbzPBd3ixYqg8huKIwKuJ7aj5lQw7fByQ4HgMgN",
|
||
style="border: 0", width="930", height="900"></iframe>
|
||
</section>
|
||
</section>
|
||
|
||
|
||
|
||
<section>
|
||
<section>Backup</section>
|
||
<section data-transition="None">
|
||
<h2>Adding a Singularity Image from a path</h2>
|
||
<ul style="font-size:30px">
|
||
<li>You can get Singularity images by "pulling" them from Singularity or
|
||
Dockerhub:</li>
|
||
<pre><code class="bash">$ singularity pull docker://nipy/heudiconv:0.5.4
|
||
$ singularity pull shub://adswa/python-ml:1
|
||
INFO: Downloading shub image
|
||
265.56 MiB / 265.56 MiB [==================================================] 100.00% 10.23 MiB/s 25s</code></pre>
|
||
<li>You can also take/write a recipe file and build a container on your computer:
|
||
<pre><code class="bash">$ sudo singularity build myimage Singularity.2
|
||
INFO: Starting build...
|
||
Getting image source signatures
|
||
Copying blob 831751213a61 done
|
||
[...]
|
||
INFO: Creating SIF file...
|
||
INFO: Build complete: myimage
|
||
</code></pre></li>
|
||
<li>pulled or build images lie around as <i>.sif</i> or <i>.simg</i> files, and can be
|
||
added to the dataset with their path and <strong>datalad containers-add</strong>:
|
||
<pre><code class="bash">$ ls
|
||
heudiconv_0.5.4.sif
|
||
python-ml_1.sif</code></pre></li>
|
||
<pre><code class="bash">$ datalad containers-add software --url /home/me/singularity/myimage
|
||
[INFO ] Copying local file myimage to /home/adina/repos/resources/.datalad/environments/software/image
|
||
add(ok): .datalad/environments/software/image (file)
|
||
add(ok): .datalad/config (file)
|
||
save(ok): . (dataset)
|
||
containers_add(ok): /home/adina/repos/resources/.datalad/environments/software/image (file)
|
||
action summary:
|
||
add (ok: 2)
|
||
containers_add (ok: 1)
|
||
save (ok: 1)
|
||
</code></pre>
|
||
<pre><code class="bash">$ datalad containers-list
|
||
software -> .datalad/environments/software/image</code></pre>
|
||
</ul>
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Adding a Singularity Image from a URL</h2>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
Tip: If you add Images from public URLs (e.g., Dockerhub or Singularity Hub),
|
||
others can retrieve your Image easily
|
||
</li>
|
||
<pre><code>$ datalad containers-add software --url shub://adswa/python-ml:1
|
||
add(ok): .datalad/config (file)
|
||
save(ok): . (dataset)
|
||
containers_add(ok): /tmp/bla/.datalad/environments/software/image (file)
|
||
action summary:
|
||
add (ok: 1)
|
||
containers_add (ok: 1)
|
||
save (ok: 1)
|
||
</code></pre>
|
||
</ul>
|
||
|
||
</section>
|
||
|
||
<section data-transition="None">
|
||
<h2>Adding a Docker Image from a path</h2>
|
||
<ul style="font-size:30px">
|
||
<li>You can get Docker images by "pulling" them from Dockerhub:</li>
|
||
<pre><code class="bash">$ docker pull repronim/neurodocker:latest 1 !
|
||
latest: Pulling from repronim/neurodocker</code></pre>
|
||
<li>You can also take/write a Dockerfile and build a container on your computer:
|
||
<pre><code class="bash">$ sudo docker build -t adwagner/somedockercontainer .
|
||
Sending build context to Docker daemon 6.656kB
|
||
Step 1/4 : FROM python:3.6
|
||
[...]
|
||
Successfully built 31d6acc37184
|
||
Successfully tagged adwagner/somedockercontainer:latest
|
||
</code></pre></li>
|
||
<li>Show docker images:
|
||
<pre><code class="bash">$ docker images
|
||
REPOSITORY TAG IMAGE ID CREATED SIZE
|
||
repronim/neurodocker latest 84b9023f0019 7 months ago 81.5MB
|
||
adwagner/min_preproc latest fca4a144b61f 8 months ago 5.96GB
|
||
[...]</code></pre></li>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section data-transition="None">
|
||
<h2>Adding a Docker image from a URL</h2>
|
||
<ul style="font-size:30px">
|
||
<li>
|
||
<pre><code>$ datalad containers-add --url dhub://busybox:1.30 bb
|
||
[INFO] Saved busybox:1.30 to C:\Users\datalad\testing\blablablabla\.datalad\environments\bb\image
|
||
add(ok): .datalad\environments\bb\image\64f5d945efcc0f39ab11b3cd4ba403cc9fefe1fa3613123ca016cf3708e8cafb.json (file)
|
||
add(ok): .datalad\environments\bb\image\a57c26390d4b78fd575fac72ed31f16a7a2fa3ebdccae4598513e8964dace9b2\VERSION (file)
|
||
add(ok): .datalad\environments\bb\image\a57c26390d4b78fd575fac72ed31f16a7a2fa3ebdccae4598513e8964dace9b2\json (file)
|
||
add(ok): .datalad\environments\bb\image\a57c26390d4b78fd575fac72ed31f16a7a2fa3ebdccae4598513e8964dace9b2\layer.tar (file)
|
||
add(ok): .datalad\environments\bb\image\manifest.json (file)
|
||
add(ok): .datalad\environments\bb\image\repositories (file)
|
||
add(ok): .datalad\config (file)
|
||
save(ok): . (dataset)
|
||
containers_add(ok): C:\Users\datalad\testing\blablablabla\.datalad\environments\bb\image (file)
|
||
action summary:
|
||
add (ok: 7)
|
||
containers_add (ok: 1)
|
||
save (ok: 1)</code></pre>
|
||
</li>
|
||
</ul>
|
||
</section>
|
||
|
||
|
||
<section>
|
||
<h2>Configure containers</h2>
|
||
<ul>
|
||
<li>
|
||
<code>datalad containers-run</code> executes any command inside of the
|
||
specified container. How does it work?
|
||
</li>
|
||
<pre><code>$ cat .datalad/config
|
||
[datalad "containers.midterm-software"]
|
||
updateurl = shub://adswa/resources:1
|
||
image = .datalad/environments/midterm-software/image
|
||
cmdexec = singularity exec {img} {cmd}</code></pre>
|
||
<li class="fragment fade-in">
|
||
You can configure the command execution however you like:
|
||
<pre><code>$ datalad containers-add fmriprep \
|
||
--url shub://ReproNim/containers:bids-fmriprep--20.1.1 \
|
||
--call-fmt 'singurity run --cleanenv -B $PWD,$PWD/.tools/license.txt {img} {cmd}'</code></pre><br>
|
||
<small>workflow demonstration fMRIprep: <a href="https://youtu.be/xlb_moXe48E?t=200" target="_blank">
|
||
OHBM 2020 Open Science Room presentation
|
||
</a> </small></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
|
||
|
||
|
||
</div>
|
||
</div>
|
||
|
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<script src="../reveal.js/dist/reveal.js"></script>
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<script src="../reveal.js/plugin/notes/notes.js"></script>
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<script src="../reveal.js/plugin/markdown/markdown.js"></script>
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<script src="../reveal.js/plugin/highlight/highlight.js"></script>
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