update to readme, standard format + image

README.md

@@ -1,32 +1,62 @@
----
 license: mit
 size_categories:
-- 100B<n<1T
+- 100K<n<1M
+tags:
+- solar-wind
+- omni
+- wind-speed
+- space-weather
+pretty_name: Solar Wind Forecasting Dataset
 ---
-# Surya-Bench Solar wind prediction Task
 
-This foundation model task uses the foundation model to predict the solar wind measurements at L1 from NASA OMNI dataset. The dataset includes Speed, ("V"), Bx (GSE), By (GSM), Bz (GSM) and number density (N). For the foundation model prediction task, we only consider the wind speed from the dataset.
-In this dataset, we have a buffer set (Jan 1 - Jan 14 (inclusive) and Jan 29 - Feb 11 (inclusive)), the validation set (Jan 15 - Jan 28(inclusive)), and training set (other days) from 2010 -05 - 01 to 2019 - 12-31 (inclusive), for every year. 
-Our testing set is from Jan 1, 2020 -- Dec 31, 2024. 
+# Solar Wind Forecasting Dataset
 
-The data are stored as csv files, and can be accesed through pandas, for instance. 
+## Dataset Summary
 
-Scripts to generate/download the dataset may be found on [Github](https://github.com/NASA-IMPACT/SuryaBench/tree/solarwind_split/solarwind)
+This dataset provides hourly solar wind plasma and interplanetary magnetic field (IMF) parameters at L1, derived from NASA’s OMNI dataset. The primary forecasting target is the solar wind speed (`V`), while additional parameters are included for completeness:  
+- Solar wind speed (`V`)  
+- IMF Bx (GSE)  
+- IMF By (GSM)  
+- IMF Bz (GSM)  
+- Proton number density (`N`)  
 
-## Dataset Details
+The dataset is structured for machine learning experiments, particularly in this collection for evaluating foundation model efficiency for space weather forecasting tasks.  Temporal coverage spans from **May 1, 2010 to December 31, 2024**, with splits designed for consistent training, validation, and testing across years.
+
+
+## Supported Tasks and Applications
+- `time-series forecasting`: Predicting future solar wind speed from past observations.  
+- `regression`: Using solar wind parameters for supervised forecasting tasks. 
+
+
+## Dataset Structure
 
-| Field    | Description |
-| -------- | ------- |
-| Temporal Coverage | 2010-05-01 to 2024-12-31 |
-| File Format  | `.csv` |
-| Data Shape | (1, 5) |
-| Number of Time Steps | 119478 |
-| Cadence    | hourly  |
-| Parameter to be considered | `V`  |
+### Data Files
+
+- `train.csv`: Instances from all other days in each year between 2010–2019  
+- `validation.csv`: Instances from Jan 15–28 of each year between 2010–2019  
+- `test.csv`: All instances from each year between 2020–2024  
+- `leaky_validation.csv`: Instances from Jan 1–14 and Jan 29–Feb 11 of each year between 2010–2019  
+
+### Features
+Each row contains hourly measurements with the following fields:  
+- `V`: Solar wind speed (km/s) [primary target]  
+- `Bx_gse`, `By_gsm`, `Bz_gsm`: IMF components (nT)  
+- `N`: Proton number density (cm⁻³)  
+
+
+## Dataset Details
 
+| Field                  | Description                          |
+|-------------------------|--------------------------------------|
+| **Temporal Coverage**   | May 13, 2010 – Dec 31, 2024           |
+| **File Format**         | CSV (.csv)                          |
+| **Data Shape**          | (1, 5) per timestep                 |
+| **Number of Time Steps**| 119,478                             |
+| **Cadence**             | 1 hour                              |
+| **Forecast Target**     | Solar wind speed (`V`)              |
 
 
 ## Authors
-Vishal Upendran, [[email protected]]([email protected])
+- Vishal Upendran, [[email protected]](mailto:[email protected])
 
-![Solar wind time series](solar_wind_data.png "Solar wind")
+![Solar wind time series](solar_wind_data.png "Solar wind")