Initial release: 228-subject CGM pretraining corpus

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README.md

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----
-license: mit
----
+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: CGM-JEPA Pretraining Corpus
+task_categories:
+- time-series-forecasting
+- feature-extraction
+task_ids:
+- multivariate-time-series-forecasting
+tags:
+- continuous-glucose-monitor
+- cgm
+- self-supervised-learning
+- biosignal
+- healthcare
+- time-series
+- pretraining
+- jepa
+size_categories:
+- 100K<n<1M
+---
+
+# CGM-JEPA Pretraining Corpus
+
+Continuous glucose monitor (CGM) time-series corpus used for self-supervised pretraining of **CGM-JEPA**, **X-CGM-JEPA**, **GluFormer**, and **TS2Vec** encoders in the paper *CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining*.
+
+> Pretraining-only corpus. For the labeled downstream-evaluation cohorts (insulin resistance and β-cell dysfunction classification), see [`CRUISEResearchGroup/CGM-JEPA-Downstream`](https://huggingface.co/datasets/CRUISEResearchGroup/CGM-JEPA-Downstream). For pretrained model weights, see [`CRUISEResearchGroup/CGM-JEPA-Weights`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA-Weights).
+
+## Quick start
+
+```bash
+huggingface-cli download CRUISEResearchGroup/CGM-JEPA-Pretraining \
+  --repo-type dataset --local-dir Dataset_Open
+```
+
+Then from the [code repository](https://github.com/cruiseresearchgroup/CGM-JEPA):
+
+```bash
+# (Optional, only for X-CGM-JEPA) Precompute the Glucodensity cache once.
+python -m utils.precompute_glucodensity \
+  --data_path Dataset_Open/cgm_initial_cohort.csv \
+  --output_path Dataset_Open/gluco_cache_stride144.pkl \
+  --stride 144
+
+# Pretrain
+python -m pretrain.pretrain_x_cgm_jepa     # X-CGM-JEPA (cross-view)
+python -m pretrain.pretrain_cgm_jepa       # CGM-JEPA (temporal only)
+python -m pretrain.pretrain_gluformer      # GluFormer baseline
+python -m pretrain.pretrain_ts2vec         # TS2Vec baseline
+```
+
+## Files
+
+| File | Size | Purpose |
+|---|---|---|
+| `cgm_initial_cohort.csv` | ~12 MB | Raw CGM readings (5-min sampling) — primary pretraining input |
+
+X-CGM-JEPA additionally needs a **Glucodensity cache** (~54 MB) which is *not* shipped here; it's derived deterministically from the CSV by [`utils/precompute_glucodensity.py`](https://github.com/cruiseresearchgroup/CGM-JEPA/blob/main/utils/precompute_glucodensity.py) in the code repository (one-time, ~few minutes on CPU).
+
+## Schema
+
+### `cgm_initial_cohort.csv`
+
+| Column | Type | Description |
+|---|---|---|
+| `timestamp` | datetime | Reading time at 5-min resolution |
+| `glucose_value` | float | Glucose value in mg/dL |
+| `subject` | str | Subject identifier (`S01..S22` for Stanford; `colas_<subject>_<day>` for Colas subject-days) |
+
+- **413 unique subject identifiers** (22 Stanford + 391 Colas subject-days = 206 unique Colas subjects across multiple days).
+- **389,365 total readings**.
+- Median 288 readings per subject (= one 24-hour window at 5-min sampling); max 18,527 readings for the longest Stanford continuous-CGM record.
+
+## Cohort composition
+
+| Source | Subjects | Rows | Share | Use |
+|---|---|---|---|---|
+| Stanford CGM Study | 22 | 276,757 | 71.1 % | CGM-JEPA / X-CGM-JEPA pretraining |
+| Colas et al. (2019) | 206 (391 subject-days) | 112,608 | 28.9 % | CGM-JEPA / X-CGM-JEPA pretraining |
+| **Total** | **228 unique subjects** | **389,365** | 100 % | |
+
+This matches the paper's stated pretraining corpus of "~389k readings from 228 subjects (22 Stanford + 206 Colas)".
+
+## How this corpus was built
+
+The corpus was assembled by [`scripts/preprocess_dataset.py`](https://github.com/cruiseresearchgroup/CGM-JEPA/blob/main/scripts/preprocess_dataset.py) in the code repository, from two upstream sources:
+
+1. **Stanford CGM Study** (Metwally et al. 2025, *Nature Biomedical Engineering*) — data distributed through the [`Metabolic_Subphenotype_Predictor`](https://github.com/aametwally/Metabolic_Subphenotype_Predictor) repository under MIT license (files `filtered_cgm_03222026.csv`, `filtered_ogtt_…csv`, `filtered_metabolic_tests.csv`).
+2. **Colas et al. (2019)** — Open-access CGM time series from *Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk*, PLOS ONE 14(12):e0225817, distributed in the paper's Supporting Information files under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+Stanford rows were smoothed onto a 5-min grid via cubic smoothing splines (`scipy.interpolate.make_smoothing_spline(lam=0.35)`). Sensor `"Low"`/`"High"` markers were replaced with the empirical numeric min/max. Colas daily windows were concatenated with prefixed subject IDs (`colas_<subject>_<day>`) to disambiguate them from Stanford subjects.
+
+## Intended use
+
+- Self-supervised pretraining of CGM encoders (masked-patch prediction, contrastive, autoencoding objectives).
+- Comparison of CGM representation learning methods.
+
+## License & attribution
+
+This corpus is released under **[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)** to match the licenses of its two upstream sources:
+
+- **Stanford CGM Study data** — released through [`Metabolic_Subphenotype_Predictor`](https://github.com/aametwally/Metabolic_Subphenotype_Predictor) under the MIT license, accompanying Metwally et al. 2025 (*Nature Biomedical Engineering*).
+- **Colas et al. (2019)** — CGM time series from the PLOS ONE Supporting Information files, licensed under CC BY 4.0.
+
+When using this dataset, please cite **all three sources**: the Stanford CGM Study (via `Metabolic_Subphenotype_Predictor`), Colas et al. 2019, and our CGM-JEPA paper.
+
+## Citation
+
+> _Citation block to be filled once the CGM-JEPA paper has a stable venue / arXiv link._
+
+## Code repository
+
+[`https://github.com/cruiseresearchgroup/CGM-JEPA`](https://github.com/cruiseresearchgroup/CGM-JEPA)

cgm_initial_cohort.csv

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