Update README.md

README.md

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 # SDOML-lite
 
-SDOML-lite is a lightweight alternative to the SDOML dataset specifically designed for machine learning applications in solar physics, providing continuous full-disk images of the Sun with magnetic field and extreme ultraviolet data in several wavelengths. The data source is the [Solar Dynamics Observatory (SDO)](https://sdo.gsfc.nasa.gov/) space telescope, a NASA mission that has been in operation since 2010.
+SDOML-lite is a lightweight alternative to the [SDOML](https://registry.opendata.aws/sdoml-fdl/) dataset specifically designed for machine learning applications in solar physics, providing continuous full-disk images of the Sun with magnetic field and extreme ultraviolet data in several wavelengths. The data source is the [Solar Dynamics Observatory (SDO)](https://sdo.gsfc.nasa.gov/) space telescope, a NASA mission that has been in operation since 2010.
 
 NASA's SDO mission has produced more than 20 petabytes of high-resolution solar image data since its launch in 2010. This data holds significant potential for a range of scientific and machine learning applications, including space weather prediction, climate and heliophysics modeling, spatiotemporal forecasting of solar activity, image-to-image translation, analysis of active region evolution, and flare prediction. However, the scale and format of the original archive have posed barriers to entry for ML practitioners. SDOML-lite addresses this by providing a curated and normalized subset of SDO data in a format suitable for large-scale machine learning workflows.
 
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     * AIA: 131 Å, 171 Å, 193 Å, 211 Å, 1600 Å (`aia_0131`, `aia_0171`, `aia_0193`, `aia_211`, `aia_1600`)
     * HMI: Line-of-sight magnetogram (`hmi_m`)
 * **Cadence:** Images are provided every 15 minutes within each daily TAR file. Each TAR file contains up to 96 observation sets.
+
+## Intended Uses
+
+This dataset is designed for machine learning research involving phenomena influenced by solar physics. Potential applications include:
+
+* Generative modeling of solar phenomena.
+* Spatiotemporal forecasting of solar activity.
+* Data-driven models in climate science and weather forecasting.
+* Developing and testing models for solar flare prediction.
+* Studying the evolution of solar active regions.
+* Image-to-image translation tasks between different solar channels.
+
+## Data Splits
+
+This dataset is provided as a collection of daily TAR files. No predefined training, validation, or test splits are provided. Users are encouraged to define their own splits according to their specific research requirements, such as by date ranges (e.g., specific years or months of year for training/validation/testing) or by solar events.
+
+## Dataset Structure
+
+The dataset is organized into 5194 TAR files, with each TAR file named according to the day it represents (e.g., `20100513.tar` for the data files on Hugging Face Hub which are named `sdoml-lite-xxxx.tar` where `xxxx` is the shard number from `0000` to `5193`). Each daily TAR file contains multiple observation sets, one for every 15-minute interval of that day. An observation set consists of 6 NumPy (`.npy`) files, one for each specified channel.
+
+**Inside each daily TAR file:**
+Files are organized with a path structure that WebDataset uses as the `__key__`. For example, files for May 13, 2010, at 00:00 UTC would be:
+* `2010/05/13/0000.aia_0131.npy`
+* `2010/05/13/0000.aia_0171.npy`
+* `2010/05/13/0000.aia_0193.npy`
+* `2010/05/13/0000.aia_0211.npy`
+* `2010/05/13/0000.aia_1600.npy`
+* `2010/05/13/0000.hmi_m.npy`
+
+And for 00:15 UTC on the same day:
+* `2010/05/13/0015.aia_0131.npy`
+* ...and so on.
+
+Each `.npy` file contains a 2D NumPy array of shape `(512, 512)` with `float32` values, representing the solar image. The preprocessing and normalization for these values are detailed in the "Data Collection and Preprocessing" section and the [GitHub repository's README](https://github.com/oxai4science/sdoml-lite/blob/main/README.md#data-preprocessing).
+
+
+### Features
+
+Each sample obtained by iterating through the WebDataset corresponds to one solar observation (with 15-minute cadence) and contains:
+
+* `__key__`: (string) The base name YYYY/MM/DD/HHMM (e.g., 2010/05/13/0000).
+* `aia_0131.npy`: (numpy.ndarray) Image data for AIA 131 Å. Shape: (512, 512). Dtype: float32.
+* `aia_0171.npy`: (numpy.ndarray) Image data for AIA 171 Å. Shape: (512, 512). Dtype: float32.
+* `aia_0193.npy`: (numpy.ndarray) Image data for AIA 193 Å. Shape: (512, 512). Dtype: float32.
+* `aia_0211.npy`: (numpy.ndarray) Image data for AIA 211 Å. Shape: (512, 512). Dtype: float32.
+* `aia_1600.npy`: (numpy.ndarray) Image data for AIA 1600 Å. Shape: (512, 512). Dtype: float32.
+* `hmi_m.npy`: (numpy.ndarray) Line-of-sight magnetogram data from HMI. Shape: (512, 512). Dtype: float32.