Update README.md

README.md

@@ -21,9 +21,7 @@ _IMPORTANT: SDOML and SDOML-lite datasets are different in structure and data di
 * **Number of Chunks (Days):** 5194
 * **Image Resolution:** 512x512 pixels for all channels
 * **Data Type:** `float32` (NumPy `.npy` files)
-- **Channels per sample**: 6 (separate `.npy` files per channel)
-    * 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`)
+- **Channels per sample**: 6 (separate `.npy` files per channel). 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
@@ -43,7 +41,7 @@ This dataset is provided as a collection of daily TAR files. No predefined train
 
 ## 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.
+The dataset is organized into 5194 TAR files, with one TAR file per day within the temporal coverage period. Each daily TAR file contains multiple observation sets, one for every 15-minute interval of that day (up to 96 observation sets per 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:
@@ -58,8 +56,7 @@ 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).
-
+Each `.npy` file contains a 2D NumPy array of shape `(512, 512)` with `float32` values, representing the solar image. 
 
 ### Features