Update README.md

README.md

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 license: mit
+tags:
+- extreme
+- instabilities
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-This dataset is accompanying the NeurIPS'26 paper: Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events.
+# ExtremeEventSensor
 
-TLDR: We use a small ensemble of intentionally perturbed, low-cost simulations to identify the locally most unstable directions of the dynamics. That information is then used to guide learned corrections to coarse trajectories.
+This dataset accompanies the NeurIPS 2026 paper *Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events*.
 
-For a light-weight demonstration, see my blog post: [blog](https://www.mit.edu/~thieli/writing/neurips/).
+**TLDR:** Small ensembles of perturbed, low-cost simulations reveal locally unstable directions in the dynamics. We use that information to guide learned corrections to coarse trajectories.
 
-Full details can be found in the accompanying paper: [arXiv:2609.30746](https://arxiv.org/abs/2609.30746).
+## What’s included
 
+- **LowDim:** Trajectories from a three-dimensional nonlinear system exhibiting bursts, including reference, coarse, nudged, and perturbed ensemble simulations.
+- **QG:** Data from a two-layer quasigeostrophic model, including upper and lower streamfunction with additional topography.  Includes fields from reference, coarse, nudged, and ensemble simulations.
 
-The codebase can be found on the GitHub: [GitHub: thisisisi](https://github.com/thisisisi/ExtremeEventSensor)
-The code is organized into two subdirectories, `QG` and `LowDim`, which follow the same overall interface.
+
+For a short demonstration, see the [blog post](https://www.mit.edu/~thieli/writing/neurips/). Full details about methods and examples are in the [paper](https://arxiv.org/abs/2609.30746), and the accompanying code is on [GitHub](https://github.com/thisisisi/ExtremeEventSensor).