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README.md

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----
-license: other
-license_name: other
-license_link: LICENSE
----
+---
+pretty_name: Extreme Environment Generator
+dataset_name: Extreme_Environment_Generator
+annotations_creators:
+- no-annotation
+language_creators:
+- no-annotation
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- generated
+task_categories:
+- time-series-forecasting
+- reinforcement-learning
+- other
+task_ids:
+- multivariate-time-series-forecasting
+paperswithcode_id: null
+---
+
+# Dataset Card for **DBbun / Extreme_Environment_Generator**
+
+## Table of Contents
+
+- [**Dataset Summary**](#dataset-summary)  
+- [**Supported Tasks and Leaderboards**](#supported-tasks-and-leaderboards)  
+- [**Languages**](#languages)  
+- [**Dataset Structure**](#dataset-structure)  
+  - [**Data Instances**](#data-instances)  
+  - [**Data Fields**](#data-fields)  
+  - [**Data Splits**](#data-splits)  
+- [**Dataset Creation**](#dataset-creation)  
+  - [**Curation Rationale**](#curation-rationale)  
+  - [**Source Data**](#source-data)  
+  - [**Simulated Procedure**](#simulated-procedure)  
+- [**Bias, Risks, and Limitations**](#bias-risks-and-limitations)  
+- [**Additional Information**](#additional-information)  
+  - [**Usage**](#usage)  
+  - [**Citation Information**](#citation-information)  
+  - [**License**](#license)  
+  - [**Contact**](#contact)  
+
+---
+
+## Dataset Summary
+
+**Extreme_Environment_Generator** is a family of three synthetic datasets created by DBbun.  
+All three datasets are generated from a single physics-inspired simulator and share the **same schema**, making them directly comparable for cross-environment benchmarking.
+
+The three environments are:
+
+- **Deep Subsurface** – a hot, high-pressure interior with crust, mantle, and core-like layers  
+- **Subsea** – an oceanic and seafloor environment with water/sediment upper layers and crust–mantle transition  
+- **Ice World** – a cold, ice-dominated environment inspired by icy planets and deep terrestrial ice layers  
+
+For each environment, the simulator produces:
+
+- A “true” physical state over time (`state_true`)  
+- Noisy, drifting, dropout-prone sensor readings (`sensors`)  
+- Layer definitions (`layer_profile`)  
+- Scenario configuration (`environment_config`)  
+- Rare catastrophic failure events (`failures`)  
+- Scenario-level aggregates (`summary`)  
+
+The dataset is designed for research in:
+
+- Multivariate time-series forecasting  
+- Sensor fusion and multimodal learning  
+- Anomaly and rare-event detection  
+- Robustness and uncertainty under noise/drift  
+- Reinforcement learning in synthetic physics  
+- Educational and demonstrative use in synthetic data generation  
+
+---
+
+## Supported Tasks and Leaderboards
+
+**Supported tasks:**
+
+- **Time-series forecasting**  
+  - Predict future physical states or sensor readings given historical sequences.  
+- **Anomaly / rare-event detection**  
+  - Identify unstable states, catastrophic events, or abnormal patterns in the time series.  
+- **Sensor fusion**  
+  - Learn from multiple correlated sensor channels (including fictional sensors) to infer hidden state or stability.  
+- **Robust ML and stress-testing**  
+  - Evaluate model behavior under noise, drift, and missing data.  
+- **Reinforcement learning and control (sim2sim)**  
+  - Use the sequences as environment rollouts or offline RL training data.  
+- **Foundation model pretraining (time-series)**  
+  - Pretrain sequence models on rich synthetic physical dynamics.  
+
+---
+
+