Update README.md

README.md

@@ -8,13 +8,30 @@ license: mit
 
 This dataset contains daily solar and meteorological data downloaded from the [NASA Power API](https://power.larc.nasa.gov/) 
 
-## Dataset Details
+## Overview
 
 The dataset includes solar and meteorological variables collected from January 1st, 1984, to December 31st, 2022. 
 We downloaded 28 variables directly and estimated an additional 3 from the collected data.  The data spans a 5 x 8 grid covering 
 the United States, Central America, and South America. Each grid rectangle contains 160 data points spaced 0.5 degrees apart in 
 latitude and longitude.
 
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```
+@misc{hasan2025vita,
+      title={VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting}, 
+      author={Adib Hasan and Mardavij Roozbehani and Munther Dahleh},
+      year={2025},
+      eprint={2508.03589},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2508.03589}, 
+}
+```
 
 
 ### Dataset Description
@@ -228,21 +245,3 @@ The `raw` data is in the JSON format and unprocessed. The `csvs` and the `pytorc
 - Leap year extra day was omitted. So, each year of the daily dataset has 365 days. Similarly, each year of the weekly dataset has 52 weeks, and the monthly dataset has 12 columns. 
 - Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
 - `pytorch` data was standardized using the mean and standard deviation of the weather over the continental United States.
-
-## Citation
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-```
-@misc{hasan2024weatherformerpretrainedencodermodel,
-      title={WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets}, 
-      author={Adib Hasan and Mardavij Roozbehani and Munther Dahleh},
-      year={2024},
-      eprint={2405.17455},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2405.17455}, 
-}
-```