<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://dhuzard.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="https://dhuzard.github.io/" rel="alternate" type="text/html" /><updated>2026-01-22T14:11:31+01:00</updated><id>https://dhuzard.github.io/feed.xml</id><title type="html">DHuzard’s Website</title><subtitle>Damien Huzard, Neuro-physio-behaviorist.</subtitle><author><name>Damien Huzard</name></author><entry><title type="html">FAIRRR 5/5 — The 30‑Day Playbook to Adopt FAIRRR with MAPP</title><link href="https://dhuzard.github.io/blog/fairrr-playbook-30-days/" rel="alternate" type="text/html" title="FAIRRR 5/5 — The 30‑Day Playbook to Adopt FAIRRR with MAPP" /><published>2025-09-22T00:00:00+02:00</published><updated>2025-09-22T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/fairrr-playbook-30-days</id><content type="html" xml:base="https://dhuzard.github.io/blog/fairrr-playbook-30-days/"><![CDATA[<blockquote class="notice--info">
  <p>This article is part of the FAIRRR series: <a href="/Blog/fairrr-ethics-of-metadata/">1</a> · <a href="/Blog/fairrr-explained-metrics-outcomes/">2</a> · <a href="/Blog/mapp-api-first-fairrr/">3</a> · <a href="/Blog/case-study-hcm-mapp-ontology/">4</a> · <a href="/Blog/fairrr-playbook-30-days/">5</a></p>
</blockquote>

<p>This is Post 5 of 5 in the FAIRRR series. Previous: <a href="/Blog/case-study-hcm-mapp-ontology/">Case Study—HCM with MAPP + Ontology Mapping</a></p>

<h2 id="week-1--scope-and-minimal-descriptors">Week 1 — Scope and minimal descriptors</h2>

<ul>
  <li>Pick one ongoing study; list 10–15 ARRIVE‑aligned descriptors.</li>
  <li>Assign persistent IDs (animals, devices, protocols, reagents).</li>
  <li>Define roles and review cadence.</li>
</ul>

<h2 id="week-2--templates-and-capture-inflow">Week 2 — Templates and capture in‑flow</h2>

<ul>
  <li>Configure MAPP templates for your study.</li>
  <li>Map 10 core ontology terms for behaviors/conditions.</li>
  <li>Start capturing provenance and deviations as you go.</li>
</ul>

<h2 id="week-3--link-a-pipeline-and-export">Week 3 — Link a pipeline and export</h2>

<ul>
  <li>Connect one analysis pipeline; record versions and parameters.</li>
  <li>Export a reusable package (metadata + license) for internal review.</li>
  <li>Dry‑run repository submission if relevant.</li>
</ul>

<h2 id="week-4--review-publish-and-institutionalize">Week 4 — Review, publish, and institutionalize</h2>

<ul>
  <li>FAIRRR scorecard review; close gaps.</li>
  <li>Write an SOP addendum for metadata and provenance.</li>
  <li>Present quick wins to leadership (time saved, joins enabled, duplication avoided).</li>
</ul>

<h2 id="sustaining-practices">Sustaining practices</h2>

<ul>
  <li>Quarterly FAIRRR metrics; template updates.</li>
  <li>Rotating metadata champions; refresher training.</li>
  <li>Incremental integrations (LIMS/ELN, more pipelines, repositories).</li>
</ul>

<h2 id="what-success-looks-like-in-30-days">What success looks like in 30 days</h2>

<ul>
  <li>One study with end‑to‑end FAIRRR metadata in MAPP.</li>
  <li>A working integration with at least one analysis.</li>
  <li>An SOP addendum and a simple FAIRRR dashboard.</li>
</ul>

<p>Series navigation: Previous ← <a href="/Blog/case-study-hcm-mapp-ontology/">Case Study—HCM with MAPP + Ontology Mapping</a> • Start → <a href="/Blog/fairrr-ethics-of-metadata/">The Metadata Gap Is an Ethics Gap</a></p>]]></content><author><name>HD + (Codex)</name></author><category term="Blog" /><category term="FAIRRR" /><category term="MAPP" /><category term="SOP" /><category term="governance" /><category term="rollout" /><category term="training" /><summary type="html"><![CDATA[A pragmatic rollout plan with roles, milestones, and proof points—so you can adopt FAIRRR in a month without boiling the ocean.]]></summary></entry><entry><title type="html">FAIRRR 4/5 — Case Study: Home Cage Monitoring with MAPP + Ontology Mapping</title><link href="https://dhuzard.github.io/blog/case-study-hcm-mapp-ontology/" rel="alternate" type="text/html" title="FAIRRR 4/5 — Case Study: Home Cage Monitoring with MAPP + Ontology Mapping" /><published>2025-09-21T00:00:00+02:00</published><updated>2025-09-21T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/case-study-hcm-mapp-ontology</id><content type="html" xml:base="https://dhuzard.github.io/blog/case-study-hcm-mapp-ontology/"><![CDATA[<blockquote class="notice--info">
  <p>This article is part of the FAIRRR series: <a href="/Blog/fairrr-ethics-of-metadata/">1</a> · <a href="/Blog/fairrr-explained-metrics-outcomes/">2</a> · <a href="/Blog/mapp-api-first-fairrr/">3</a> · <a href="/Blog/case-study-hcm-mapp-ontology/">4</a> · <a href="/Blog/fairrr-playbook-30-days/">5</a></p>
</blockquote>

<p>This is Post 4 of 5 in the FAIRRR series. Previous: <a href="/Blog/mapp-api-first-fairrr/">Inside MAPP—API‑First FAIRRR</a> • Next: <a href="/Blog/fairrr-playbook-30-days/">The 30‑Day FAIRRR Playbook</a></p>

<h2 id="the-hcm-reproducibility-problem">The HCM reproducibility problem</h2>

<p>HCM studies hinge on device details (model, firmware, calibration), environment (light/temperature/noise), and behavior definitions. Without consistent metadata, cross‑study comparison is fragile, and duplication is common.</p>

<h2 id="modeling-hcm-with-mapp">Modeling HCM with MAPP</h2>

<ul>
  <li>Animals ↔ devices ↔ procedures ↔ behaviors/outcomes.</li>
  <li>Capture firmware, calibration, and environment alongside procedures.</li>
  <li>Link analyses and parameters to specific device sessions.</li>
</ul>

<p><img src="/assets/hcmo-mapping/hcm-mapper/HCM-mapper.png" alt="HCM Ontology Mapper" /></p>

<h2 id="ontology-mapping-workflow">Ontology mapping workflow</h2>

<p>Use the HCM Ontology Mapper to align device outputs and behavior labels to shared terms (e.g., MBO for behaviors). That makes results comparable across vendors and pipelines.</p>

<p>Workflow sketch:</p>

<p>1) Extract device mapping sheet → 2) Map to ontology terms → 3) Create MAPP entities → 4) Export a reusable package with provenance and licenses.</p>

<h2 id="analysis-linkage">Analysis linkage</h2>

<p>Tie each behavior/outcome to its generating pipeline version and parameters. That enables auditability and safe reuse in meta‑analyses.</p>

<h2 id="outcomes">Outcomes</h2>

<ul>
  <li>Fewer repeated experiments; more cross‑study joins.</li>
  <li>Faster review and QC via clear provenance and deviations.</li>
  <li>Better welfare through transparent refinement opportunities.</li>
</ul>

<p>Related projects: <a href="/projects/hcm-ontology-mapper/">/projects/hcm-ontology-mapper/</a> • <a href="/projects/hcm-explorer/">/projects/hcm-explorer/</a></p>

<p>Series navigation: Previous ← <a href="/Blog/mapp-api-first-fairrr/">Inside MAPP—API‑First FAIRRR</a> • Next → <a href="/Blog/fairrr-playbook-30-days/">The 30‑Day FAIRRR Playbook</a></p>]]></content><author><name>HD + (Codex)</name></author><category term="Blog" /><category term="HCM" /><category term="ontology" /><category term="MAPP" /><category term="FAIRRR" /><category term="devices" /><category term="reproducibility" /><summary type="html"><![CDATA[A concrete example: modeling devices, behaviors, and provenance in Home Cage Monitoring to unlock reuse, comparability, and fewer animals.]]></summary></entry><entry><title type="html">FAIRRR 3/5 — Inside MAPP: API‑First Metadata for Reproducible Research</title><link href="https://dhuzard.github.io/blog/mapp-api-first-fairrr/" rel="alternate" type="text/html" title="FAIRRR 3/5 — Inside MAPP: API‑First Metadata for Reproducible Research" /><published>2025-09-20T00:00:00+02:00</published><updated>2025-09-20T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/mapp-api-first-fairrr</id><content type="html" xml:base="https://dhuzard.github.io/blog/mapp-api-first-fairrr/"><![CDATA[<blockquote class="notice--info">
  <p>This article is part of the FAIRRR series: <a href="/Blog/fairrr-ethics-of-metadata/">1</a> · <a href="/Blog/fairrr-explained-metrics-outcomes/">2</a> · <a href="/Blog/mapp-api-first-fairrr/">3</a> · <a href="/Blog/case-study-hcm-mapp-ontology/">4</a> · <a href="/Blog/fairrr-playbook-30-days/">5</a></p>
</blockquote>

<p>This is Post 3 of 5 in the FAIRRR series. Previous: <a href="/Blog/fairrr-explained-metrics-outcomes/">FAIRRR, Explained</a> • Next: <a href="/Blog/case-study-hcm-mapp-ontology/">Case Study—HCM with MAPP + Ontology Mapping</a></p>

<h2 id="why-apifirst-beats-formsfirst">Why API‑first beats forms‑first</h2>

<ul>
  <li>Interoperability: integrate ELN/LIMS, devices, and pipelines programmatically.</li>
  <li>Automation: capture in‑flow with fewer manual steps and fewer errors.</li>
  <li>Governance: versioned templates, roles, and validations at the edge.</li>
</ul>

<h2 id="what-mapp-captures">What MAPP captures</h2>

<ul>
  <li>Subjects and cohorts, with identifiers and lineage.</li>
  <li>Procedures, protocols, and deviations.</li>
  <li>Devices, firmware, and environment.</li>
  <li>Behaviors and outcomes.</li>
  <li>Analysis pipelines, inputs/outputs, parameters, and versions.</li>
</ul>

<h2 id="standards-under-the-hood">Standards under the hood</h2>

<ul>
  <li>Persistent identifiers for linkability.</li>
  <li>Community ontologies for semantics and comparability.</li>
  <li>Provenance (who/what/when/where/how) and versioning.</li>
</ul>

<p>Example payload sketch (illustrative):</p>

<div class="language-json highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">{</span><span class="w">
  </span><span class="nl">"study"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"STUDY:123"</span><span class="p">,</span><span class="w"> </span><span class="nl">"title"</span><span class="p">:</span><span class="w"> </span><span class="s2">"Open Field"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"animal"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"ANML:001"</span><span class="p">,</span><span class="w"> </span><span class="nl">"strain"</span><span class="p">:</span><span class="w"> </span><span class="s2">"C57BL/6J"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"device"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"DEV:OF-01"</span><span class="p">,</span><span class="w"> </span><span class="nl">"model"</span><span class="p">:</span><span class="w"> </span><span class="s2">"OF-Cam"</span><span class="p">,</span><span class="w"> </span><span class="nl">"firmware"</span><span class="p">:</span><span class="w"> </span><span class="s2">"1.3.2"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"procedure"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PROC:OF"</span><span class="p">,</span><span class="w"> </span><span class="nl">"ontology"</span><span class="p">:</span><span class="w"> </span><span class="s2">"OAE:0001"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"behavior"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"id"</span><span class="p">:</span><span class="w"> </span><span class="s2">"MBO:000123"</span><span class="p">,</span><span class="w"> </span><span class="nl">"label"</span><span class="p">:</span><span class="w"> </span><span class="s2">"Exploration"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"provenance"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"who"</span><span class="p">:</span><span class="w"> </span><span class="s2">"tech_a"</span><span class="p">,</span><span class="w"> </span><span class="nl">"when"</span><span class="p">:</span><span class="w"> </span><span class="s2">"2025-09-01T10:30:00Z"</span><span class="p">,</span><span class="w"> </span><span class="nl">"where"</span><span class="p">:</span><span class="w"> </span><span class="s2">"Room 3"</span><span class="p">},</span><span class="w">
  </span><span class="nl">"analysis"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"pipeline"</span><span class="p">:</span><span class="w"> </span><span class="s2">"of-v1"</span><span class="p">,</span><span class="w"> </span><span class="nl">"version"</span><span class="p">:</span><span class="w"> </span><span class="s2">"0.9.4"</span><span class="p">,</span><span class="w"> </span><span class="nl">"params"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nl">"fps"</span><span class="p">:</span><span class="w"> </span><span class="mi">30</span><span class="p">}}</span><span class="w">
</span><span class="p">}</span><span class="w">
</span></code></pre></div></div>

<h2 id="integration-patterns">Integration patterns</h2>

<ul>
  <li>ELN/LIMS sync via connectors.</li>
  <li>Pipeline hooks that emit RO‑Crate/PROV‑O flavored exports.</li>
  <li>Repository exports with licenses and DOI metadata.</li>
</ul>

<h2 id="governance-builtin">Governance built‑in</h2>

<ul>
  <li>Role‑based access; review workflows.</li>
  <li>Versioned, reusable templates.</li>
  <li>Validations that keep data FAIR by design.</li>
</ul>

<p>Series navigation: Previous ← <a href="/Blog/fairrr-explained-metrics-outcomes/">FAIRRR, Explained</a> • Next → <a href="/Blog/case-study-hcm-mapp-ontology/">Case Study—HCM with MAPP + Ontology Mapping</a></p>]]></content><author><name>HD + (Codex)</name></author><category term="Blog" /><category term="MAPP" /><category term="FAIRRR" /><category term="metadata" /><category term="APIs" /><category term="provenance" /><category term="ontologies" /><summary type="html"><![CDATA[MAPP turns scattered records into structured, shareable, machine‑readable metadata—operationalizing FAIRRR with standards, identifiers, and provenance.]]></summary></entry><entry><title type="html">FAIRRR 2/5 — FAIRRR, Explained: From Principles to Measurable Outcomes</title><link href="https://dhuzard.github.io/blog/fairrr-explained-metrics-outcomes/" rel="alternate" type="text/html" title="FAIRRR 2/5 — FAIRRR, Explained: From Principles to Measurable Outcomes" /><published>2025-09-19T00:00:00+02:00</published><updated>2025-09-19T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/fairrr-explained-metrics-outcomes</id><content type="html" xml:base="https://dhuzard.github.io/blog/fairrr-explained-metrics-outcomes/"><![CDATA[<blockquote class="notice--info">
  <p>This article is part of the FAIRRR series: <a href="/Blog/fairrr-ethics-of-metadata/">1</a> · <a href="/Blog/fairrr-explained-metrics-outcomes/">2</a> · <a href="/Blog/mapp-api-first-fairrr/">3</a> · <a href="/Blog/case-study-hcm-mapp-ontology/">4</a> · <a href="/Blog/fairrr-playbook-30-days/">5</a></p>
</blockquote>

<p>This is Post 2 of 5 in the FAIRRR series. Previous: <a href="/Blog/fairrr-ethics-of-metadata/">The Metadata Gap Is an Ethics Gap</a> • Next: <a href="/Blog/mapp-api-first-fairrr/">Inside MAPP—API‑First FAIRRR</a></p>

<h2 id="fair-for-animal-research">FAIR for animal research</h2>

<ul>
  <li>Findable: unique IDs for animals, studies, protocols; rich searchable metadata.</li>
  <li>Accessible: standard protocols and clear access rules; durable links.</li>
  <li>Interoperable: shared ontologies and identifiers; qualified references.</li>
  <li>Reusable: provenance, licenses, and community standards.</li>
</ul>

<h2 id="the-3rs-lens-on-metadata">The 3Rs lens on metadata</h2>

<ul>
  <li>Replacement: comparable context reveals viable non‑animal models.</li>
  <li>Reduction: reuse across studies avoids duplication; pooled analyses.</li>
  <li>Refinement: transparent deviations and environment details improve welfare.</li>
</ul>

<h2 id="minimal-viable-standards-practical-set">Minimal viable standards (practical set)</h2>

<ul>
  <li>ARRIVE‑aligned descriptors for study design and reporting.</li>
  <li>Persistent identifiers for animals, devices, reagents, and protocols.</li>
  <li>Ontologies for behaviors, anatomy, conditions, procedures, and reagents.</li>
  <li>Provenance (who/what/when/where/how), versions, and deviations.</li>
  <li>Licenses and reuse terms.</li>
</ul>

<h2 id="metrics-you-can-track">Metrics you can track</h2>

<ul>
  <li>Time‑to‑reuse: first external reuse after publication/export.</li>
  <li>Duplication avoided: studies averted via comparable prior data.</li>
  <li>Cross‑study joins: number of successful joins across cohorts/devices.</li>
  <li>Protocol deviations: detection rate and resolution time.</li>
  <li>Provenance completeness: % of records with full lineage and versions.</li>
  <li>FAIRRR score: a simple rubric (0–100) across F‑A‑I‑R + 3Rs facets.</li>
</ul>

<h2 id="overcoming-the-usual-barriers">Overcoming the usual barriers</h2>

<ul>
  <li>Culture: position metadata as quality and ethics, not bureaucracy.</li>
  <li>UX: capture in‑flow (APIs and templates), not at the end.</li>
  <li>Governance: roles, reviews, and versioned templates.</li>
  <li>Interop: API‑first integration with ELN/LIMS and analysis pipelines.</li>
</ul>

<h2 id="how-this-maps-to-mapp">How this maps to MAPP</h2>

<p>MAPP bakes these practices into templates, identifiers, ontologies, and APIs, and exposes exports for repositories and reviewers. That makes FAIRRR measurable—not aspirational.</p>

<p>Series navigation: Previous ← <a href="/Blog/fairrr-ethics-of-metadata/">The Metadata Gap Is an Ethics Gap</a> • Next → <a href="/Blog/mapp-api-first-fairrr/">Inside MAPP—API‑First FAIRRR</a></p>]]></content><author><name>HD + (Codex)</name></author><category term="Blog" /><category term="FAIRRR" /><category term="FAIR" /><category term="3Rs" /><category term="metrics" /><category term="ARRIVE" /><category term="ontologies" /><summary type="html"><![CDATA[Turn FAIRRR into concrete lab behaviors and outcome metrics you can track—and see how better metadata directly supports the 3Rs.]]></summary></entry><entry><title type="html">FAIRRR 1/5 — The Metadata Gap Is an Ethics Gap</title><link href="https://dhuzard.github.io/blog/fairrr-ethics-of-metadata/" rel="alternate" type="text/html" title="FAIRRR 1/5 — The Metadata Gap Is an Ethics Gap" /><published>2025-09-18T00:00:00+02:00</published><updated>2025-09-18T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/fairrr-ethics-of-metadata</id><content type="html" xml:base="https://dhuzard.github.io/blog/fairrr-ethics-of-metadata/"><![CDATA[<blockquote class="notice--info">
  <p>This article is part of the FAIRRR series: <a href="/Blog/fairrr-ethics-of-metadata/">1</a> · <a href="/Blog/fairrr-explained-metrics-outcomes/">2</a> · <a href="/Blog/mapp-api-first-fairrr/">3</a> · <a href="/Blog/case-study-hcm-mapp-ontology/">4</a> · <a href="/Blog/fairrr-playbook-30-days/">5</a></p>
</blockquote>

<p>This is Post 1 of 5 in the FAIRRR series. Next: <a href="/Blog/fairrr-explained-metrics-outcomes/">FAIRRR, Explained—From Principles to Measurable Outcomes</a></p>

<h2 id="the-hidden-cost-of-good-enough-metadata">The hidden cost of “good enough” metadata</h2>

<p>Most labs still rely on scattered spreadsheets, email attachments, and post‑hoc documentation. The result is irreproducible work, duplicated experiments, and animals used without maximizing knowledge gain. That isn’t just a technical problem—it’s an ethics problem.</p>

<p>FAIRRR reframes metadata management as a scientific and ethical responsibility: data should be Findable, Accessible, Interoperable, and Reusable—and the way we manage it should honor the 3Rs (Replacement, Reduction, Refinement) while enabling innovative, reproducible, and responsible research.</p>

<h2 id="from-fair-to-fairrr">From FAIR to FAIRRR</h2>

<ul>
  <li>FAIR makes data usable beyond a single lab or study.</li>
  <li>FAIRRR adds explicit responsibility for animal welfare and scientific rigor.</li>
  <li>The link: better metadata enables fewer repeated studies, more reuse, and clearer evidence for humane alternatives.</li>
</ul>

<h2 id="where-reproducibility-actually-breaks">Where reproducibility actually breaks</h2>

<ul>
  <li>Planning: no shared identifiers for protocols, animals, devices, reagents.</li>
  <li>Capture: context is lost (who/what/when/where/how), especially deviations.</li>
  <li>Lineage: data aren’t linked to analysis versions and parameters.</li>
  <li>Sharing: metadata are added at publication—too late for quality or reuse.</li>
</ul>

<h2 id="a-simple-lifecycle-you-can-manage">A simple lifecycle you can manage</h2>

<p>Plan → Capture → Link → Publish → Reuse.</p>

<ul>
  <li>Plan: define minimal descriptors and identifiers (ARRIVE‑aligned).</li>
  <li>Capture: log procedures, animals, devices, behaviors as you go.</li>
  <li>Link: connect to pipelines and provenance (inputs, versions, outputs).</li>
  <li>Publish: export a reusable package with licenses and ontology terms.</li>
  <li>Reuse: enable cross‑study joins and meta‑analysis.</li>
</ul>

<h2 id="how-mapp-operationalizes-fairrr">How MAPP operationalizes FAIRRR</h2>

<p>MAPP is an API‑first platform for preclinical metadata. It standardizes capture (procedures, animals, devices, behaviors), enforces identifiers and ontologies, links analyses, and exports structured, machine‑readable packages. In short, it turns scattered records into shareable, computable context.</p>

<p>What that means ethically:</p>

<ul>
  <li>Replacement: discover suitable alternatives faster via findable, comparable studies.</li>
  <li>Reduction: avoid duplication; enable pooled analyses and cross‑study reuse.</li>
  <li>Refinement: expose protocol context and deviations to improve quality and welfare.</li>
</ul>

<h2 id="see-also">See also</h2>

<ul>
  <li>Background: <a href="/Blog/metadata-reproducibility/">Unlocking Reproducibility: Why Metadata Management is the Future of Preclinical Research</a></li>
  <li>Related: <a href="/Blog/mbo-unified-language/">The Mouse Behavior Ontology (MBO) as a Unified Language</a></li>
</ul>

<p>Series navigation: Next → <a href="/Blog/fairrr-explained-metrics-outcomes/">FAIRRR, Explained—From Principles to Measurable Outcomes</a></p>]]></content><author><name>HD + (Codex)</name></author><category term="Blog" /><category term="FAIRRR" /><category term="FAIR" /><category term="3Rs" /><category term="reproducibility" /><category term="metadata" /><category term="MAPP" /><summary type="html"><![CDATA[Poor metadata drives irreproducibility and unnecessary animal use. FAIRRR reframes metadata as a scientific and ethical responsibility—and MAPP makes it operational.]]></summary></entry><entry><title type="html">Unlocking Reproducibility: Why Metadata Management is the Future of Preclinical Research</title><link href="https://dhuzard.github.io/blog/metadata-reproducibility/" rel="alternate" type="text/html" title="Unlocking Reproducibility: Why Metadata Management is the Future of Preclinical Research" /><published>2025-09-11T00:00:00+02:00</published><updated>2025-09-11T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/metadata-reproducibility</id><content type="html" xml:base="https://dhuzard.github.io/blog/metadata-reproducibility/"><![CDATA[<p>Preclinical research stands at a critical juncture, facing mounting pressure from regulators, a persistent reproducibility crisis, and evolving ethical considerations regarding animal welfare. At the heart of these challenges, and indeed their solution, lies <strong>metadata management</strong>. A recent presentation titled “Enhancing Metadata Management for Reproducibility in Preclinical Research” by Damien Huzard of Neuro Nautix, introduces the MAPP platform as a crucial step towards a more transparent, robust, and ethical scientific future.</p>

<h2 id="the-urgent-need-for-change">The Urgent Need for Change</h2>

<h3 id="the-reproducibility-crisis">The Reproducibility Crisis</h3>
<p>The scientific community is grappling with a significant challenge: the <strong>lack of reproducibility</strong> in biomedical studies. A major project designed to validate biomedical research found that <strong>only 21% of experiments were replicable</strong> when using at least half of the applicable criteria. This statistic underscores the profound need for improved research practices and, critically, better data management.</p>

<h3 id="evolving-regulatory-and-ethical-landscapes">Evolving Regulatory and Ethical Landscapes</h3>
<p>There’s a clear global trend towards reducing and eventually phasing out animal testing. For instance:</p>
<ul>
  <li>The <strong>FDA</strong> has announced plans to phase out animal testing requirements for monoclonal antibodies and other drugs.</li>
  <li>The <strong>NIH</strong> is prioritizing human-based research technologies and will no longer provide exclusive funding for animal experiments.</li>
  <li>Even in France, legislation is being considered to recognize the right to conscientious objection to animal experimentation.</li>
</ul>

<p>These shifts highlight that <strong>increasing regulation necessitates better data management</strong>, which in turn requires <strong>better metadata management</strong>.</p>

<h3 id="the-fragmented-ecosystem">The Fragmented Ecosystem</h3>
<p>Currently, preclinical research is characterized by a disjointed ecosystem where researchers navigate numerous disconnected tools and data sources. While existing guidelines (like ARRIVE, EDA, PREPARE) aim for transparency and reproducibility, they are often <strong>“poorly adhered to, Only at publication”</strong>. Similarly, metadata standards (MNMS, IMPreSS, MIATE, SEND), while defining essential metadata, are <strong>“still emerging, No tools, Specific”</strong>. Software like LIMS and ELNs manage workflows but are often <strong>“Siloed, Inconsistent use, Low standardization,”</strong> and data repositories, though enabling open sharing, often involve <strong>“Post-hoc use, Manual metadata”</strong> compilation.</p>

<h2 id="metadata-the-data-of-the-data">Metadata: The “Data of the Data”</h2>

<p>Metadata is fundamentally <strong>“The data of the Data”</strong>. It’s the information that describes and gives context to research data, making it understandable, interpretable, and ultimately usable. Without robust metadata, raw data loses much of its value.</p>

<p>To address these challenges, the <strong>FAIR Principles</strong> and the <strong>FAIRRR framework</strong> are essential:</p>

<h3 id="the-fair-principles-findable-accessible-interoperable-reusable">The FAIR Principles: Findable, Accessible, Interoperable, Reusable</h3>
<p>The FAIR principles provide a foundational guide for data management:</p>
<ul>
  <li><strong>Findable:</strong> Data has unique identifiers, rich metadata, and is registered in a searchable source.</li>
  <li><strong>Accessible:</strong> Data can be retrieved using standard protocols, with clear authentication where necessary.</li>
  <li><strong>Interoperable:</strong> Data uses shared language, FAIR vocabularies, and qualified references, allowing for integration across systems.</li>
  <li><strong>Reusable:</strong> Data has rich attributes, clear licenses, detailed provenance, and meets community standards, enabling future use.</li>
</ul>

<h3 id="the-fairrr-framework-fair-for-the-3rs">The FAIRRR Framework: FAIR for the 3Rs</h3>
<p>The FAIRRR framework extends the FAIR principles to encompass <strong>“FAIR for the Animals, and for Innovative, Reproducible, and Responsible Research.”</strong> It emphasizes that <strong>“Every data-management choice as a welfare decision”</strong>. This framework directly links FAIR principles to the <strong>3Rs of animal welfare</strong>:</p>
<ul>
  <li><strong>Reduction:</strong> Achieved by avoiding redundant experiments, maximizing data reuse, and enabling multi-centre/pooled analyses, stemming from data being Findable and Reusable.</li>
  <li><strong>Replacement:</strong> Supported by discovering and validating alternatives, which is facilitated by Accessible and Interoperable data.</li>
  <li><strong>Refinement:</strong> Fostered through adopting best practices, increasing transparency, and improving protocol quality, enabled by Interoperable and Reusable data.</li>
</ul>

<h2 id="mapp-your-all-in-one-metadata-management-platform">MAPP: Your All-in-one Metadata Management Platform</h2>

<p>While over 20 FAIR initiatives exist, offering various services, they often <strong>“Require extra effort for FAIRification,”</strong> and critically, <strong>“No initiative provides plug-and-play FAIR-by-design tool”</strong>. This is where <strong>MAPP (METADATAPP)</strong> steps in.</p>

<p>MAPP is designed to be <strong>“A central HUB in preclinical research,”</strong> integrating and connecting various existing tools and platforms like eLabFTW, OSF, protocols.io, Fair3R, SoftMouse.NET, and the NC3Rs. It offers <strong>“FAIR-by-design tools to facilitate exchange across apps and improve research practices,”</strong> making research FAIR <strong>“From Study conception to Publication (and beyond!).”</strong></p>

<figure>
  <video width="100%" controls="">
    <source src="/assets/videos/Metadata_for_Reproducibility.mp4" type="video/mp4" />
    Your browser does not support the video tag.
  </video>
  <figcaption>A short presentation on how enhancing metadata management is crucial for reproducibility in preclinical research. (Slideshow created with NotebookLM).</figcaption>
</figure>

<p>Key features and benefits of MAPP include:</p>
<ul>
  <li><strong>Standardized Metadata Schema:</strong> MAPP proposes a comprehensive schema for preclinical research, capturing crucial information about institutes, labs, projects, subjects, experiments, treatments, and environmental housing.</li>
  <li><strong>Stakeholder Integration:</strong> The schema is built to integrate input from all key players in preclinical research, including Facility Managers, Lead Scientists, PhD Students, Animal Caretakers, IT personnel, and Vets, ensuring all relevant metadata is captured.</li>
  <li><strong>Seamless Data Flow:</strong> MAPP facilitates the transfer of metadata, for instance, from LIMS systems like SoftMouse.NET to data repositories such as Fair3R, enabling rich metadata, DOI assignment, and data visualization.</li>
  <li><strong>Generating New Knowledge:</strong> By standardizing and connecting data across different labs and systems, MAPP enables semantic enrichment, ontology mapping, and graph analysis, ultimately accelerating the generation of new scientific knowledge.</li>
</ul>

<h2 id="the-transformative-impact-of-fair-by-design-data-management">The Transformative Impact of FAIR-by-Design Data Management</h2>

<p>Implementing a FAIR-by-design (meta)data management system like MAPP yields significant benefits:</p>
<ul>
  <li><strong>Improved Workflows</strong> for all stakeholders.</li>
  <li>Leads to <strong>Better Data</strong> and enhanced traceability.</li>
  <li>Directly results in <strong>Reproducibility</strong> of research.</li>
  <li>Ensures <strong>Respect for the 3Rs</strong>, addressing ethical considerations in animal research.</li>
  <li>Provides a <strong>Gain of Time</strong> by streamlining processes and reducing redundant efforts.</li>
  <li>Ultimately fosters <strong>Better Science</strong> and accelerates <strong>New Discoveries</strong>.</li>
  <li>Promotes <strong>Open Science</strong> through improved data sharing and collaboration.</li>
</ul>

<p>The journey towards fully reproducible and responsible preclinical research requires a fundamental shift in how we manage our data. MAPP offers a robust, integrated, and FAIR-by-design solution that not only streamlines research processes but also elevates the quality and ethical standing of scientific discovery.</p>]]></content><author><name>HD + (Gemini Code Assist)</name></author><category term="Blog" /><category term="reproducibility" /><category term="metadata" /><category term="FAIR" /><category term="FAIRRR" /><category term="MAPP" /><category term="open-science" /><summary type="html"><![CDATA[Preclinical research stands at a critical juncture, facing mounting pressure from regulators, a persistent reproducibility crisis, and evolving ethical considerations regarding animal welfare. At the heart of these challenges, and indeed their solution, lies metadata management. A recent presentation titled “Enhancing Metadata Management for Reproducibility in Preclinical Research” by Damien Huzard of Neuro Nautix, introduces the MAPP platform as a crucial step towards a more transparent, robust, and ethical scientific future.]]></summary></entry><entry><title type="html">Bridging the Gap: The Mouse Behavior Ontology (MBO) as a Unified Language for Behavioral Research</title><link href="https://dhuzard.github.io/blog/mbo-unified-language/" rel="alternate" type="text/html" title="Bridging the Gap: The Mouse Behavior Ontology (MBO) as a Unified Language for Behavioral Research" /><published>2025-09-10T00:00:00+02:00</published><updated>2025-09-10T10:00:00+02:00</updated><id>https://dhuzard.github.io/blog/mbo-unified-language</id><content type="html" xml:base="https://dhuzard.github.io/blog/mbo-unified-language/"><![CDATA[<p>The field of Human-Computer Measurement (HCM), especially concerning animal behavior, is experiencing rapid growth. This expansion, while promising, has also introduced significant challenges: a <strong>proliferation of systems and definitions</strong> and a resulting <strong>lack of comparability</strong> across different studies and platforms. This fragmentation makes it incredibly difficult to integrate large datasets and draw consistent, reliable conclusions.</p>

<h2 id="the-foundation-ethograms-and-their-limitations">The Foundation: Ethograms and Their Limitations</h2>

<p>At the heart of behavioral analysis lies the <strong>ethogram</strong>, often referred to as “the behavioral dictionary”. An ethogram is a catalog of species-specific behaviors, listing actions like grooming, feeding, nesting, and sniffing. Its primary strength is fostering a <strong>shared understanding</strong> of these behaviors among researchers. It also provides a comprehensive description and structure for behaviors.</p>

<p>However, traditional ethograms come with notable drawbacks:</p>
<ul>
  <li>They are inherently <strong>“Subjective”</strong>.</li>
  <li>They are <strong>“Not machine readable,”</strong> which complicates automated analysis.</li>
  <li>Consequently, they are <strong>“Hard to integrate big data”</strong>.</li>
</ul>

<p>These limitations hinder progress in a data-rich research environment, calling for a more advanced approach.</p>

<h2 id="enter-the-mbo-a-powerful-solution-for-reproducible-behavior-data">Enter the MBO: A Powerful Solution for Reproducible Behavior Data</h2>

<p>The <strong>Mouse Behavior Ontology (MBO)</strong> emerges as a transformative solution, designed to overcome the limitations of traditional ethograms by providing a <strong>“common language for behavior”</strong>. The MBO achieves this by combining the <strong>“ethogram richness + ontology logic”</strong>.</p>

<h3 id="how-ontologies-infer-knowledge">How Ontologies Infer Knowledge</h3>

<p>Ontologies are powerful because they allow for the inference of knowledge. For example, an ontology can logically connect observed micro-behaviors to broader behavioral categories:</p>
<ul>
  <li>“Sniffing nose-to-nose” can be inferred as “Social Investigation”.</li>
  <li>“Sniffing anogenital” can also lead to “Social Investigation”.</li>
  <li>Ultimately, “Social Investigation” can then be inferred as a form of <strong>“Social Interaction”</strong>.</li>
</ul>

<p>This hierarchical structure moves beyond simple observation to provide <strong>machine-understandable conclusions</strong>, such as “Mice are doing socially interacting with each other”.</p>

<h3 id="mbo-from-machine-readable-to-machine-understandable">MBO: From Machine-Readable to Machine-Understandable</h3>

<p>The MBO is essentially an <strong>“Ethogram + Ontology”</strong>. It creates a structured framework of “Behavior Categories” (e.g., Exploratory, Social, Maintenance, with sub-categories like Climbing, Social Investigation, Grooming Self).</p>

<p>This framework provides a <strong>“computable description”</strong> for HCM, integrating various aspects of an experimental setup, including welfare, spatial dimensions, temporal patterns, and measurement of visible movements and physiological processes. Crucially, it links these to computational elements like software, hardware, sensors, and actuators that constrain logical protocols.</p>

<p>The MBO transforms data from merely machine-readable to <strong>“machine understandable”</strong> by linking:</p>
<ul>
  <li><strong>Natural language descriptions</strong> of behaviors.</li>
  <li><strong>Contextual information</strong> about the HCM model (e.g., name, manufacturer).</li>
  <li>Data from various <strong>sensor technologies</strong> (Video, IR, Capacitance, ultrasound).</li>
  <li><strong>Metrics</strong> categorized as Point, Interval, or Continuum events.</li>
</ul>

<p>This allows for the capture of the <strong>“same behavior across HCM systems (sensor-independent)”</strong>. For instance, a behavior like “Circling” can be consistently recognized whether observed via manual annotation, pose estimation, vibration analysis, RFID, or conductance measurements.</p>

<p>By providing a common identifier (MBO:ID), MBO <strong>unifies HCM data streams</strong>, linking raw sensor readings and observations (e.g., a mouse “Digging”) to a standardized definition. This standardization is vital for processing data through <strong>Machine Learning</strong> and <strong>Generative AI</strong>.</p>

<figure>
  <video width="100%" controls="">
    <source src="/assets/videos/A_Common_Language_for_Science.mp4" type="video/mp4" />
    Your browser does not support the video tag.
  </video>
  <figcaption>A short presentation on how structured metadata, like that provided by MBO, is crucial for reproducibility. (Slideshow created with NotebookLM).</figcaption>
</figure>

<h2 id="benefits-of-the-mbo">Benefits of the MBO</h2>

<p>The implementation of MBO offers a multitude of benefits:</p>

<p><strong>For Researchers:</strong></p>
<ul>
  <li><strong>Uniform definitions</strong> for behaviors.</li>
  <li><strong>Consistency across labs</strong>, fostering collaboration.</li>
  <li><strong>Easy sharing &amp; reuse</strong> of behavioral data.</li>
  <li><strong>Simplified data integration</strong> from multiple HCM systems, leading to <strong>enhanced discovery</strong> and <strong>more robust data</strong>.</li>
</ul>

<p><strong>For Stronger Analytics &amp; Models:</strong></p>
<ul>
  <li><strong>Standardized ML training labels</strong>, improving model accuracy.</li>
  <li><strong>Interoperability across systems</strong>, making data globally useful.</li>
  <li><strong>Support for large-scale integration</strong> of complex datasets.</li>
  <li>Data scientists can <strong>train AI and ML models with clear, structured behavioral labels</strong>, resulting in <strong>more accurate and reliable behavior recognition</strong>.</li>
  <li>Commercial providers can offer <strong>“Standardized, interoperable data,”</strong> promoting <strong>broader user adoption</strong> and alignment with community-supported ethograms.</li>
</ul>

<h2 id="building-mbo-together-a-collaborative-effort">Building MBO Together: A Collaborative Effort</h2>

<p>The development of MBO is a <strong>community-driven initiative</strong>, involving various stakeholders:</p>
<ul>
  <li><strong>Researchers</strong> (animal behavior, animal welfare).</li>
  <li><strong>Data scientists</strong> (ML, AI, Bioinformatics).</li>
  <li><strong>Commercial providers</strong> (HCM manufacturers, developers).</li>
</ul>

<p>This collaborative approach ensures that the MBO is refined and supported by the community, integrating input from existing HCM technologies like Cleversys, Noldus, deepOF, and AlphaTracker. The development process, often involving brainstorming and hackathon sessions, follows a structured approach including defining the domain, enumerating terms, and defining classes and properties. It’s recognized that “There is no one correct way to model a domain: there are always viable alternatives,” highlighting the adaptive nature of its development.</p>

<h2 id="the-take-home-message">The Take-Home Message</h2>

<p>The Mouse Behavior Ontology (MBO) is more than just a tool; it’s a <strong>“common language for behavior”</strong>. By combining the richness of ethograms with the logical power of ontologies, MBO significantly <strong>improves reproducibility, sharing, and welfare</strong> in animal research. Ultimately, it <strong>opens new paths for discovery</strong>, pushing the boundaries of what’s possible in behavioral science.</p>]]></content><author><name>HD + (Gemini Code Assist)</name></author><category term="Blog" /><category term="MBO" /><category term="ontology" /><category term="HCM" /><category term="reproducibility" /><category term="open-science" /><summary type="html"><![CDATA[The field of Human-Computer Measurement (HCM), especially concerning animal behavior, is experiencing rapid growth. This expansion, while promising, has also introduced significant challenges: a proliferation of systems and definitions and a resulting lack of comparability across different studies and platforms. This fragmentation makes it incredibly difficult to integrate large datasets and draw consistent, reliable conclusions.]]></summary></entry><entry><title type="html">My First and Last TEATIME- A Glimpse into the Future of Behavioral Research</title><link href="https://dhuzard.github.io/blog/teatime-conference-recap/" rel="alternate" type="text/html" title="My First and Last TEATIME- A Glimpse into the Future of Behavioral Research" /><published>2025-09-05T00:00:00+02:00</published><updated>2025-09-03T12:00:00+02:00</updated><id>https://dhuzard.github.io/blog/teatime-conference-recap</id><content type="html" xml:base="https://dhuzard.github.io/blog/teatime-conference-recap/"><![CDATA[<p>It was my first (and unfortunately last) in-person TEATIME conference, and what a blast it was! The event was a whirlwind of inspiring talks, groundbreaking tools, and a powerful sense of a community united by a common goal: to revolutionize behavioral research. I left feeling energized and more optimistic than ever about the future of our field.</p>

<p>Two major themes dominated the conference: a collective drive towards <strong>standardization</strong> and a celebration of <strong>open-source innovation</strong>.</p>

<h2 id="a-unified-vision-for-hcm">A Unified Vision for HCM</h2>

<p>One of the most influential takeaways was the push for a standardized <strong>HCM (Home Cage Monitoring) system definition</strong>. This isn’t just about technical specs; it’s about creating a shared language that enables true interoperability between different systems. This move towards a homogenized framework is a game-changer for collaborative and reproducible science.</p>

<p><img src="/assets/images/teatime-2025/PXL_20250902_064153323.jpg" alt="The HCM System Definition presentation" /></p>

<p>This spirit of collaboration is also embodied by <a href="https://www.thebehaviourforum.com">The Behaviour Forum</a>, a free, open-access platform for discussing all things animal behavior. The forum continues to grow, and we are always looking for more volunteers to join the community and help us push scientific knowledge forward!</p>

<p><img src="/assets/images/teatime-2025/PXL_20250902_070330906.MP.jpg" alt="The Behaviour Forum presentation" /></p>

<h2 id="the-open-source-revolution-in-full-swing">The Open-Source Revolution in Full Swing</h2>

<p>The conference was a vibrant showcase of what happens when brilliant minds embrace open science. The energy around these community-driven projects was palpable. Here are a few that particularly stood out:</p>

<ul>
  <li>
    <p><strong>MiceCraft</strong>: The incredible LMT duo, Fabrice de Chaumont and Elodie, unveiled the evolution of the Live Mouse Tracker into <strong>MiceCraft</strong>—a fully customizable and controllable playground for neuroscientific and behavioral testing. It’s a testament to how a powerful tool can become a versatile platform for unique applications. It’s all open-source, of course, and you can find out more at <a href="https://micecraft.com">micecraft.com</a>.
<img src="/assets/images/teatime-2025/PXL_20250903_114716217.jpg" alt="MiceCraft presentation" /></p>
  </li>
  <li>
    <p><strong>FED3 and its Ecosystem</strong>: Alexxai Kravitz demonstrated just how much neuroscience research can benefit from the amazing <strong>FED3</strong>, while Hamid presented <strong>RTFED</strong>, a beautiful solution that combines FED3’s power with synchronized photometry recordings.
<img src="/assets/images/teatime-2025/PXL_20250902_133651365.jpg" alt="RTFED presentation" /></p>
  </li>
  <li>
    <p><strong>Innovative Tracking Solutions</strong>: The creativity on display was astounding. Stuart Peirson’s <strong>COMPASS system</strong> for sleep/activity tracking showed how a deep scientific question can drive technological breakthroughs. Michal introduced a brilliant, low-cost system for multi-video tracking of a dam and her pups in their home cages. I’m excited to see how this project evolves!
<img src="/assets/images/teatime-2025/PXL_20250902_125826779.jpg" alt="Dam and Pups tracking presentation" /></p>
  </li>
  <li>
    <p><strong>Comprehensive Monitoring Environments</strong>: We saw glimpses of the next generation of HCM environments. Davor Virag’s <strong>MIROSLAV</strong> is a well-engineered solution for large-scale circadian and environmental tracking. And the <strong>BEATBOX Cafe</strong>, from Daniela Domingues and the Eric Burguière lab, is an elegant, modular environment for cognitive experiments. We’re all eagerly awaiting its official release!
<img src="/assets/images/teatime-2025/PXL_20250902_090430552.jpg" alt="MIROSLAV presentation" /></p>
  </li>
</ul>

<h2 id="building-a-fairer-future">Building a FAIRer Future</h2>

<p>Beyond the tools, the conference reinforced the importance of community-driven infrastructure. The <strong>HCM catalog</strong> continues to be an invaluable resource.</p>

<p>I was particularly excited by the presentation of <strong>Fair3R.fr</strong> by Benoit Petit-Demouliere from Phenomin. It uses the open-source CKAN framework to create a federated platform for sharing preclinical (meta)data. This is exactly the kind of solution we need to build a more FAIR and interconnected research ecosystem. I’ve already included it in <a href="https://www.metadatapp.net">MAPP</a>, and the best part is that you can deploy your own instance today!</p>

<h3 id="bonus-the-hcm-boardgame">Bonus: The HCM Boardgame!</h3>

<p>A fun and creative highlight was the HCM boardgame developed by the incredible Sonia Bains!</p>

<p><img src="/assets/images/teatime-2025/PXL_20250902_144145547.jpg" alt="HCM Boardgame" /></p>

<h2 id="from-ideas-to-implementation">From Ideas to Implementation</h2>

<p>The talks were equally eye-opening. I was truly impressed by Martien Kas, who shared the quality of translational work his team is achieving. His efforts to redefine frameworks that better connect preclinical findings to clinical research are profoundly inspiring.</p>

<p>Seeing this rich ecosystem of initiatives, all centered on standardized knowledge, FAIR-by-design tools, and interoperability, makes it clear that a tremendous future lies ahead. The community has the skills, expertise, and network to turn these ideas into reality.</p>

<p>This is an open call to funders and potential investors: the time is right to support these efforts. If you want to be part of building the next generation of research infrastructure, drop me an email!</p>

<p>The future resides in unity and constant connection. I hope to see an official <strong>HCM society</strong> form soon to help us stay organized and accelerate our development.</p>

<p>Finally, a huge thank you to Vootele and Hilary for making this unforgettable conference happen!</p>]]></content><author><name>HD +(Gemini 2.5 Pro)</name></author><category term="Blog" /><category term="conference" /><category term="open-science" /><category term="HCM" /><summary type="html"><![CDATA[It was my first (and unfortunately last) in-person TEATIME conference, and what a blast it was! The event was a whirlwind of inspiring talks, groundbreaking tools, and a powerful sense of a community united by a common goal: to revolutionize behavioral research. I left feeling energized and more optimistic than ever about the future of our field.]]></summary></entry><entry><title type="html">Aujourd’hui, Maman est morte</title><link href="https://dhuzard.github.io/blog/Aujourdhui-maman-est-morte/" rel="alternate" type="text/html" title="Aujourd’hui, Maman est morte" /><published>2025-08-26T00:00:00+02:00</published><updated>2025-08-27T00:22:11+02:00</updated><id>https://dhuzard.github.io/blog/Aujourdhui-maman-est-morte</id><content type="html" xml:base="https://dhuzard.github.io/blog/Aujourdhui-maman-est-morte/"><![CDATA[<p>“Aujourd’hui, maman est morte” (Today, mom died) *<br />
Exactly one year ago my mum died from glioblastoma.</p>

<p>It took me time and energy to process and decide whether I wanted to share this story or not. But I believe it might resonate with hundreds (if not thousands) of other families suffering from the direct (and side) effects of brain tumors. I also wanted to leverage this dark anniversary to pay homage to my mum while trying to raise awareness about end-of-life treatment in French medical institutions. This piece is personal, but I hope it can help others and describe why better end-of-life support is needed in France.</p>

<p>As I will recount our journey, it’s crucial to remember that the true depth of suffering lay with my mum, a pain far greater than anything we experienced.</p>

<h2 id="the-diagnosis-a-glioblastoma-journey-begins">The Diagnosis: A Glioblastoma Journey Begins</h2>

<p>This is the story of Orev, my 64-year-old mother, whose journey with glioblastoma began quietly but ended tragically. Glioblastoma multiforme (GBM), one of the most aggressive forms of brain cancer, progresses rapidly and carries a devastating prognosis: most people live an average of 12 to 18 months after diagnosis.</p>

<p>In early May 2022, a small tumor was discovered in her corpus callosum—the thick band of nerve fibers connecting the two brain hemispheres. Its critical location made surgical intervention impossible. The first MRI was ambiguous, showing something unusual but inconclusive. Later that month, a biopsy confirmed the devastating diagnosis: GBM.</p>

<p>The news had to be delivered by phone, and I was the one who told her. It was a terrible moment for both of us, filled with confusion, heartbreak, and a profound sense of helplessness. I’ll never forget that conversation; it was a responsibility placed upon me by the neuro-oncologist, a decision I still grapple with, despite my mother’s later appreciation that it came from me, not a ‘cold stranger’.</p>

<p>Early symptoms, leading Orev to ask for an MRI scan, were persistent migraines, headaches, blurred memory, and a sense of “mental fog”. Despite the short survival time she “defied the odds”, surviving two years and three months. Her initial treatment involved six weeks of radiation combined with Temodal (temozolomide, a standard chemotherapy drug), followed by additional chemotherapy cycles. The first chemotherapy began in August 2022. Her MGMT methylation, a clinically relevant prognostic marker, was low (~13%), indicating lower survival and a reduced response to Temodal.</p>

<p>After this initial phase, she had to wait six months before assessing the tumor’s progression, due to difficulties in performing MRI scans after irradiations. In January 2023, the follow-up MRI revealed disease progression, prompting the restart of a new chemotherapy treatment. In March, she began experiencing a steep cognitive decline, and by April-May 2023, her condition required urgent hospitalization and corticosteroid treatment to manage a brain edema.</p>

<p>Beyond the medical complexities, Orev’s diagnosis also plunged our family into an intense emotional landscape, exposing deeply personal coping mechanisms and unforeseen challenges.</p>

<h2 id="navigating-grief-and-family-dynamics">Navigating Grief and Family Dynamics.</h2>

<p>In July 2022, with my help and support, Orev subscribed to Dignitas in Switzerland, contemplating assisted suicide to avoid her biggest fear: a complete loss of cognitive functions and autonomy. However, due to my father’s (Roger) opposition, she ultimately did not pursue this option and suffered that exact situation two years later.</p>

<p>For a woman accustomed to control and independence, the fear of death and loss of autonomy was overwhelming. With a history of depression, Orev sank deeper into despair as her cognitive abilities eroded. Losing the ability to count, reason, and recall memories—the very faculties defining her—was devastating.</p>

<p>The emotional burden on the family was immense, and her decline deeply affected all of us. She had been the family’s anchor, decision-maker, and central figure. Slowly, we began to lose her, and with it, the family unity. Two sides slowly appeared within the family, two coping strategies, and ultimately two non-compatible groups that grew further apart over time.</p>

<p>Her husband, Roger, and daughter, Laure, who lived nearby and interacted daily, met the diagnosis with a form of denial—a defense against the painful reality of losing their beloved wife/mum and family cornerstone. Even though they documented themselves and understood the grim reality of Orev’s impending death, they held onto false hope and refused to accept it. This denial was accompanied by a total lack of communication. To my knowledge, they almost never discussed what was going to happen. They gave all the energy they had, this is obvious, but it did not fully help them face reality, act properly, and, most importantly, plan and accompany her psychologically through this horrific journey.</p>

<p>On the other hand, my brother, Naturel, and I were both totally supportive of her initial end-of-life choices. We understood that she would decline and die in the coming year (or two if lucky). But both of us were living in distant places and were not the first line of helpers. Naturel faced the inevitable in two phases: at first, he offered total support—he even moved to our parents’ place for a month and half. But as tension grew, he shifted into anticipatory grief, eventually withdrawing completely for the six months leading up to her death.</p>

<p>Initially, I found myself in the middle, struggling to mediate and communicate amidst growing tensions. At the beginning, this communication game was okay, translating medical and familial matters to both sides. On one hand, I understood the true meaning of the diagnosis (I might have actually been too pessimistic, reading all the literature I could find on the topic), and on the other hand, I was emotionally protected by the physical distance and my personal context.</p>

<p>However, as Orev’s illness progressed, these emotional and psychological struggles quickly translated into tangible social and practical challenges that further tested our family’s resilience and exposed critical gaps in support systems.</p>

<h2 id="beyond-the-emotional-toll-social-and-practical-realities">Beyond the Emotional Toll: Social and Practical Realities</h2>

<p>As Orev’s condition worsened, the family dynamics shifted dramatically. Roger, the primary caregiver, started to experience some burnout (never officially diagnosed or discussed). The emotional and physical toll, compounded by his reluctance to seek external help, significantly impacted his health. The family isolated themselves from external support, exacerbating their strain.</p>

<p>Fortunately, financial concerns were minimal (in part) due to France’s robust healthcare system, enabling the family to focus solely on Orev’s care. However, as her cognitive abilities declined, social interactions decreased, weakening the family’s once-strong bond.</p>

<p>A pivotal conflict arose in May 2023 when Orev became severely impaired and nearly comatose (a strong edema had formed around the tumor). Roger and Laure refused to listen to Naturel and did not want to call the neuro-oncologist, remaining in denial about her rapidly deteriorating condition. I intervened, calling the hospital with prior approval from all family members. Upon arrival at the hospital, the neuro-oncologist confirmed the urgency, immediately administering corticosteroids to reduce the brain edema caused by the tumor. Only after Orev’s cognitive and physical state stabilized did Laure begin to blame me because the family did not wish to cancel a planned vacation the coming week. Though the hospitalization (which had actually been validated by all two weeks earlier) likely saved Orev’s life, I was blamed for disrupting their plans. This highlights the painful tensions that denial and fear can create.</p>

<p>It is also following that specific event that Orev and Roger decided to abandon the idea of assisted suicide in Switzerland. Then, Orev’s state was stable for a little while. She had a third line of treatment, which required a PICC (peripherally inserted central catheter) line and very recurrent visits to the hospital. A difficult period initiated… more despair, helplessness, and boredom, but mostly a veritable feeling of what was inevitably coming soon.</p>

<p>In May 2024, Orev experienced total loss of speech, accompanied by a fall that resulted in a broken right arm. Surgery was not an option due to her fragile condition.</p>

<p>After May 2024, everything collapsed. Orev was essentially locked inside her body, often in visible pain, yet receiving little more than a Doliprane once or twice per day—only if she “appeared to be suffering,” according to Roger. Until August 2024, they resisted providing her with strong pain relief. In those final days, as she could no longer swallow food and was visibly fading, she was finally administered more potent drugs—though not continuously. Only then was her pain briefly alleviated.</p>

<p>On Monday, August 26, 2024, at 6 am, she passed away in her bedroom, holding the hand of her beloved husband, finally relieved from all the pain.</p>

<p>My mother’s passing, while bringing an end to her suffering, marked the beginning of a profound reflection for me. Her two-year battle not only revealed the devastating nature of Glioblastoma but also illuminated severe shortcomings in how our healthcare system, particularly in France, addresses terminal illness and end-of-life care.</p>

<h2 id="beyond-the-diagnosis-advocating-for-change-in-terminal-care">Beyond the Diagnosis: Advocating for Change in Terminal Care</h2>

<p>In the end, and in my opinion, it could have been different. She could have gone in better conditions. She could have suffered less. If the medical system focused slightly more on the human side. There are several aspects that should be improved in the way the medical system is “treating” patients who face inevitable prominent death:</p>

<ul>
  <li><strong>Inadequate Communication from Medical Professionals</strong>: A significant challenge throughout this ordeal was inadequate communication from medical professionals. Despite my neuroscience background, I found myself bearing the responsibility of informing my mother about her terminal diagnosis—a task that medical professionals should have compassionately handled. This disrupted communication was present until the end. It was often evident that my parents did not understand much of what the neuro-oncologist had been discussing during their interviews. A good solution they found was to record and re-listen later on.</li>
  <li><strong>Lack of Dignified End-of-Life Options in France</strong>: Early in her illness, Orev considered assisted suicide through Dignitas in Switzerland, subscribing to their program. Roger resisted this choice, and eventually, he insisted enough so that the idea was abandoned. As Orev’s disease progressed and she lost autonomy, Roger acknowledged that assisted suicide might have been appropriate, if the logistics were easier. This experience highlights a critical gap in France’s healthcare system: the absence of dignified end-of-life options for terminally ill patients. Relying on an external, unvalidated, unpractical foreign option did not help the decision, leaving Orev without a truly dignified choice.</li>
</ul>

<h2 id="beyond-awareness-a-call-to-action-for-systemic-healthcare-changes">Beyond Awareness: A Call to Action for Systemic Healthcare Changes</h2>

<p>Glioblastoma’s impact is devastating, most profoundly affecting the patient. While I’ve described the immense pain and challenges our family faced, it is the unimaginable depth of my mum’s suffering that truly compels this call to action. Orev’s journey, tragically cut short by GBM and the limitations of our current healthcare system, illustrates the urgent need for systemic change in France’s healthcare approach. But we don’t have to accept the status quo.</p>

<p>Here’s how you can help create a more compassionate and dignified future:</p>

<ul>
  <li><strong>Raise Awareness</strong>: Share Orev’s story and this article with your network. Talk to your friends and family about Glioblastoma and the critical importance of dignified end-of-life options. The more we speak about it, the more visible the need for change becomes.</li>
  <li><strong>Support Research</strong>: Contribute to organizations funding Glioblastoma research. Every donation, no matter the size, fuels the scientific breakthroughs that could lead to better treatments and, one day, a cure.</li>
  <li><strong>Advocate for Policy Reform</strong>: Contact your elected officials in France. Urge them to reconsider current policies surrounding end-of-life care, pushing for legal, dignified options such as assisted suicide or euthanasia for terminally ill patients. Your voice can influence critical legislative changes.</li>
  <li><strong>Improve Medical Communication</strong>: Support initiatives that provide better training for healthcare professionals on delivering difficult news with compassion and clarity. Demand transparency and comprehensive explanations of medical conditions and prognoses.</li>
  <li><strong>Eliminate Pseudoscience</strong>: Insist on evidence-based treatment plans to uphold the integrity of medical care and protect vulnerable patients from false hope and ineffective remedies.</li>
  <li><strong>Preserve Family Bonds</strong>: Recognize that Glioblastoma does not only destroy the patient’s body, but often fractures family relationships under the weight of stress, anticipatory grief, and role reversals. This can be mitigated by the presence of a neutral third party—such as a psychologist, social worker, or trained volunteer—who supports families emotionally, accompanies them to medical appointments, and helps maintain communication and cohesion during the most difficult stages.</li>
</ul>

<p>Glioblastoma is a cruel disease, with profound impacts reaching far beyond the individual diagnosed. Through increased awareness, improved medical communication, and compassionate end-of-life care options, we can provide dignity and meaningful support to those facing this formidable challenge.</p>

<p>I hope these words may have an impact, or at least may help one person who is in a similar situation to feel slightly less alone. Good luck to everyone impacted by this disease and similar conditions.</p>

<p>And most importantly: <strong>Je t’aime Maman!</strong></p>

<hr />

<ul>
  <li>“Aujourd’hui, maman est morte. Ou peut-être hier, je ne sais pas” is the Incipit from the novel <em>The Stranger</em> by Albert Camus (1942). It translates as: “Today, mom died. Or maybe yesterday, I don’t know.”</li>
</ul>

<p><img src="/assets/images/VeroGun.png" alt="VeroGun" /></p>]]></content><author><name>HD (+ ChatGPT o4.5)</name></author><category term="Blog" /><category term="post" /><category term="personal" /><summary type="html"><![CDATA[“Aujourd’hui, maman est morte” (Today, mom died) * Exactly one year ago my mum died from glioblastoma.]]></summary></entry><entry><title type="html">My Personal Journey with the Live Mouse Tracker</title><link href="https://dhuzard.github.io/blog/LMT-Tribute/" rel="alternate" type="text/html" title="My Personal Journey with the Live Mouse Tracker" /><published>2025-07-22T00:00:00+02:00</published><updated>2025-07-22T14:12:33+02:00</updated><id>https://dhuzard.github.io/blog/LMT-Tribute</id><content type="html" xml:base="https://dhuzard.github.io/blog/LMT-Tribute/"><![CDATA[<h1 id="my-personal-journey-with-the-live-mouse-tracker-from-novice-to-home-cage-monitoring-expert">My Personal Journey with the Live Mouse Tracker: From Novice to Home Cage Monitoring Expert</h1>

<h2 id="introduction">Introduction</h2>

<p>Ever since I started my research career, I’ve been fascinated by how organisms adapt to stress and their environments. Understanding the biobehavioral phenotype—how physiological and behavioral aspects intertwine—has always been my primary interest. My journey with Home Cage Monitoring (HCM) systems, specifically the Live Mouse Tracker (LMT), shaped my professional path profoundly, turning me into an expert passionate about integrative animal monitoring.</p>

<h2 id="early-days-from-rats-to-mice-from-ecg-to-lmt-2014-2018">Early Days: From Rats to Mice, from ECG to LMT (2014-2018)</h2>

<p>Back in 2018, at the end of my PhD, my research focused heavily on stress adaptation and coping strategies in rats. Using ECG, heart rate variability, and metabolic cages, I aimed to see the organism as a complete system. Yet, something felt missing—I needed a solution to observe behavior dynamically, continuously, and unobtrusively.</p>

<p>That’s when I stumbled upon Fabrice De Chaumont’s groundbreaking preprint introducing the <strong>Live Mouse Tracker</strong>. The system immediately captivated me. It was affordable, versatile, and combined advanced behavior tracking with accessible technology. Even better, it was open-source, allowing any motivated researcher to build it themselves.</p>

<h2 id="diving-in-building-my-first-lmt-2019">Diving In: Building My First LMT (2019)</h2>

<p>In February 2019, freshly beginning my first postdoc, I eagerly embarked on building my own LMT setup. I remember the excitement of receiving the Kinect sensor, RFID antennas, and computer. The challenge now was creating an independent, self-contained cage system.</p>

<figure>
  <img src="/assets/images/LMT/IMG_20190416_173628.jpg" alt="Soldering the first antennas" width="500" />
  <figcaption>Soldering the first antennas.</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/IMG_20190423_151927.jpg" alt="First LMT floor connected!" width="500" />
  <figcaption>First LMT floor connected!</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/IMG_20190420_112343.jpg" alt="The Pyble!" width="500" />
  <figcaption>The Pyble!</figcaption>
</figure>

<p>With sheer enthusiasm, I soldered circuits, constructed a soundproof and air-controlled environment, and integrated independent lighting. Then came Python—an entirely new language for me at the time. Countless YouTube tutorials, my first Python handbook, and patient troubleshooting sessions later, my LMT system was ready.</p>

<p>Meeting Fabrice and Elodie at at webinar in Marseille was pivotal. Their generosity and expertise boosted my confidence and accelerated my progress dramatically.</p>

<figure>
  <img src="/assets/images/LMT/IMG_20190329_140300.jpg" alt="Second Step, building an enclosure" width="500" />
  <figcaption>Second Step, building an enclosure.</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/IMG_20190703_103714.jpg" alt="Creating a pleasant testing environment" width="500" />
  <figcaption>Step 3: Creating a pleasant testing environment</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/IMG_20190702_130748.jpg" alt="Step 4: Enjoy!" width="500" />
  <figcaption>Step 4: Enjoy!</figcaption>
</figure>

<h2 id="success-challenges-and-covid-2020">Success, Challenges, and COVID (2020)</h2>

<p>The early experiments with LMT were promising, confirming the system’s potential. However, when COVID hit, research slowed dramatically. Combined with challenging lab dynamics, my progress temporarily stalled. Yet, adversity often opens new doors.</p>

<h2 id="a-new-chapter-with-new-hope-and-science-excitement-2020-2023">A New Chapter with New Hope and science excitement (2020-2023)</h2>

<p>During this uncertain period, discussions with Amaury François—another passionate LMT user studying affective touch and sociability—proved transformative. His fascinating research perfectly complemented my interests. Together, we navigated troubleshooting and refined the system further.</p>

<p>Securing personal funding for one year, and subsequently joining Amaury’s ERANET NEURON project (alongside with E. bourinet at IGF of Montpellier) for two more years, allowed me to deeply explore the LMT’s capabilities and push my expertise even further.</p>

<h2 id="creating-the-live-mouse-tracker-widget-toolbox-lwtools-with-paul-carrascosa">Creating the Live Mouse Tracker Widget Toolbox (LWTools) with Paul Carrascosa</h2>

<p>Supervising and working closely with Paul Carrascosa, we recognized a critical barrier for LMT users—the difficulty non-programmers had in analyzing their data. To address this, we developed the LMT Widget toolbox (LWTools)[https://pypi.org/project/LWTools/], a user-friendly tool that simplified data visualization and basic statistics from SQLite databases.</p>

<p>The goal of LWTools is to enhanced the accessibility and practical usability of the Live Mouse Tracker for researchers worldwide, democratizing powerful data analysis tools.</p>

<h2 id="scaling-up-the-collaborative-lmt-shelf">Scaling Up: The Collaborative LMT Shelf</h2>

<p>With the LMT successfully deployed in individual setups, we envisioned scaling its potential further. Collaborating again with Paul, we designed and built an innovative “LMT shelf,” housing four parallel systems. This advanced setup drastically improved experimental throughput and allowed simultaneous monitoring of multiple cohorts under identical conditions—truly an exciting leap forward.</p>

<figure>
  <img src="/assets/images/LMT/PXL_20230828_084215552.MP.jpg" alt="Paul at work" width="500" />
  <figcaption>LMT Shelf, made of Aluminum profiled</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/PXL_20240329_085128867.MP.jpg" alt="LMT Shelf, 4 setups in parallel" width="500" />
  <figcaption>LMT Shelf, 4 setups in parallel</figcaption>
</figure>

<figure>
  <img src="/assets/images/LMT/PXL_20240227_152511536.MP.jpg" alt="LMT v2, LMT-Blocks (MiceCraft beta version)" width="500" />
  <figcaption>LMT v2, LMT-Blocks (MiceCraft beta version)</figcaption>
</figure>

<h2 id="how-lmt-shaped-my-career-and-expertise">How LMT Shaped My Career and Expertise</h2>

<p>Reflecting on this journey, I recognize how profoundly the LMT influenced my career. I acquired not only practical skills—building sophisticated setups, mastering Python, and troubleshooting—but also deep insights into Home Cage Monitoring’s transformative potential in behavioral neuroscience.</p>

<h2 id="conclusion-gratitude-and-looking-ahead">Conclusion: Gratitude and Looking Ahead</h2>

<p>This journey has been rewarding, challenging, and incredibly enriching. I’m deeply grateful to Fabrice for his generosity and vision!
Also Amaury François for invaluable collaboration and mentorship, and Paul Carrascosa for his enthusiasm.</p>

<p>As I continue forward, my passion for integrative behavior monitoring grows stronger, always inspired by the remarkable possibilities of innovative tools like the Live Mouse Tracker.</p>]]></content><author><name>HD (+ ChatGPT o4.5)</name></author><category term="Blog" /><category term="post" /><summary type="html"><![CDATA[My Personal Journey with the Live Mouse Tracker: From Novice to Home Cage Monitoring Expert]]></summary></entry></feed>