# MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management

arXiv: https://arxiv.org/abs/2601.11505 · HTML: https://mlsanity.com/paper/2601.11505/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Benchmarks for Deep Off-Policy Evaluation](https://mlsanity.com/paper/2103.16596/) (2021)
- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions](https://mlsanity.com/paper/2304.06506/) (2023)
- [AZT1D: A Real-World Dataset for Type 1 Diabetes](https://mlsanity.com/paper/2506.14789/) (2025)
- [Presenting DiaData for Research on Type 1 Diabetes](https://mlsanity.com/paper/2508.09160/) (2025)
- [Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData](https://mlsanity.com/paper/2511.02849/) (2025)
- [Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors](https://mlsanity.com/paper/2307.12015/) (2023)

## Built on by (most cited first, 5 of 5)

- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [A unified data format for managing diabetes time-series data: DIAbetes eXchange (DIAX)](https://mlsanity.com/paper/2604.11944/) (2026)
- [MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2606.18640/) (2026)
- [HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care](https://mlsanity.com/paper/2609.06976/) (2026)
- [Sensor-Language-Action Models](https://mlsanity.com/paper/2610.08244/) (2026)

## Abstract (arXiv)

Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development. Multiple publicly available T1D datasets were consolidated into a unified resource, termed the MetaboNet dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet dataset comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/ , and with a Data Use Agreement (DUA)-restricted subset accessible through their respective application processes. For the datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual datasets.