Add dataset description to README

README.md

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 ### Chaotic Time Series Dataset
 
-Each time series is a drawn from one system over an extended duration, making this dataset suitable for long-horizon forecasting tasks.
++ Each multivariate time series is a drawn from one chaotic dynamical system over an extended duration, making this dataset suitable for long-horizon forecasting tasks.
+
++ There are 4 million total multivariate observations, grouped into 135 systems and three granularities
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++ The subdirectories `coarse`, `medium`, and `fine` each contain 135 `.csv` files, each of which contains a single multivariate time series of length 10,000
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++ The number of channels varies depending on the specific dynamical system.
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++ The time series are stationary due to the ergodic property of chaotic systems.
+
+## Reference
+
+For more information, or if using this code for published work, please cite the accompanying papers.
+
+> William Gilpin. "Chaos as an interpretable benchmark for forecasting and data-driven modelling" Advances in Neural Information Processing Systems (NeurIPS) 2021 https://arxiv.org/abs/2110.05266
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+> William Gilpin. "Model scale versus domain knowledge in statistical forecasting of chaotic systems" Physical Review Research 2023 https://arxiv.org/abs/2303.08011
+
+## Code
+
+For executable code, or to simulate new trajectories, please see the [dysts repository on GitHub](https://github.com/williamgilpin/dysts)
+