Update README.md

README.md

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 ---
+language: en
 license: mit
+task_categories:
+- tabular-classification
+tags:
+- long-covid
+- multi-omics
+- recovery
+- systems-biology
+- sios
+- interaction-geometry
+size_categories:
+- n<1K
+pretty_name: Long Covid Recovery Interaction Geometry
 ---
+
+# What this dataset does
+
+This dataset tests whether failed Long Covid recovery is better predicted by interaction geometry than by any single biological burden signal.
+
+The target is not disease diagnosis.
+
+The target is failed recovery.
+
+# Core stability idea
+
+A patient may show high biological burden and still recover if repair capacity remains strong and omics layers stay coherent.
+
+A patient may show moderate burden and fail to recover if repair capacity weakens and biological layers begin to diverge.
+
+The central hypothesis is:
+
+```text
+failed recovery = burden × disagreement × weak repair
+
+Prediction target
+label = 1
+
+means failed or stuck recovery trajectory.
+
+label = 0
+
+means recovered or stable recovery trajectory.
+
+Row structure
+
+Each row represents a synthetic patient recovery state.
+
+Columns:
+
+scenario_id
+ifn_signature
+cytokine_activation
+acute_phase_proteins
+mitochondrial_stress
+lipid_disruption
+microbiome_instability
+oxidative_stress
+repair_response
+symptom_score
+label
+Biological interpretation
+
+The dataset is biology-shaped but synthetic.
+
+It is designed to test whether models can detect interaction structure across immune, metabolic, microbiome, oxidative, and repair signals.
+
+It does not claim patient-level validation.
+
+Files
+data/train.csv
+data/test.csv
+scorer.py
+README.md
+Evaluation
+
+Submit predictions as:
+
+scenario_id,prediction
+LC101,0
+LC102,0
+LC103,1
+
+Run:
+
+python scorer.py predictions.csv data/test.csv
+
+The scorer reports:
+
+accuracy
+precision
+recall
+f1
+confusion matrix
+Structural Note
+
+This dataset is part of the Clarus / SIOS synthetic benchmark series.
+
+It is designed to test whether models can identify latent stability geometry rather than rely on single-variable correlation.
+
+The hidden structure is not intended to represent a clinical rule.
+
+The purpose is to evaluate whether a model can reason across interacting biological signals.
+
+Production Deployment
+
+This dataset should not be used for clinical decision-making.
+
+A production version would require validated patient-level Long Covid multi-omics data, longitudinal outcomes, and independent external testing.
+
+Enterprise & Research Collaboration
+
+This benchmark can be extended into real-data research using public or partner datasets containing transcriptomics, proteomics, metabolomics, immune profiling, symptom trajectories, and recovery outcomes.
+
+License
+
+MIT