Update dataset card with paper link, GitHub repository, and citation

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by nielsr HF Staff - opened
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  1. README.md +16 -14
README.md CHANGED
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  ---
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- license: cc-by-4.0
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  language:
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  - en
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- pretty_name: CGM-JEPA Pretraining Corpus
 
 
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  task_categories:
 
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  - feature-extraction
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- - other
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- modalities:
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- - Time Series
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- - Tabular
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  tags:
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  - continuous-glucose-monitor
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  - cgm
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  - time-series
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  - pretraining
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  - jepa
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- size_categories:
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- - 100K<n<1M
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  configs:
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  - config_name: default
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  data_files:
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  # CGM-JEPA Pretraining Corpus
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- 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*.
 
 
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  > 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`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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  ## Citation
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- > _Citation block to be filled once the CGM-JEPA paper has a stable venue / arXiv link._
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-
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- ## Code repository
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-
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- [`https://github.com/cruiseresearchgroup/CGM-JEPA`](https://github.com/cruiseresearchgroup/CGM-JEPA)
 
 
 
 
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  ---
 
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  language:
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  - en
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+ license: cc-by-4.0
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+ size_categories:
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+ - 100K<n<1M
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  task_categories:
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+ - time-series-forecasting
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  - feature-extraction
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+ pretty_name: CGM-JEPA Pretraining Corpus
 
 
 
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  tags:
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  - continuous-glucose-monitor
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  - cgm
 
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  - time-series
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  - pretraining
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  - jepa
 
 
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  configs:
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  - config_name: default
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  data_files:
 
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  # CGM-JEPA Pretraining Corpus
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+ 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](https://huggingface.co/papers/2605.00933).
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+
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+ **Code:** [https://github.com/cruiseresearchgroup/CGM-JEPA](https://github.com/cruiseresearchgroup/CGM-JEPA)
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  > 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`](https://huggingface.co/CRUISEResearchGroup/CGM-JEPA).
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  ## Citation
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+ ```bibtex
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+ @article{muhammad2026cgm,
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+ title = {CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining},
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+ author = {Muhammad, Hada Melino and Li, Zechen and Salim, Flora and Metwally, Ahmed A},
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+ journal = {arXiv preprint arXiv:2605.00933},
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+ year = {2026}
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+ }
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+ ```