Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - arXiv:2506.22821
 ---
 
-This Zenodo repository contains all migration flow estimates associated with the paper [_"Deep learning four decades of human migration."_](https://arxiv.org/abs/2506.22821) Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the [main GitHub repository](https://github.com/ThGaskin/Migration_flows), which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here.
+This repository contains all migration flow estimates associated with the paper [_"Deep learning four decades of human migration."_](https://arxiv.org/abs/2506.22821) Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the [main GitHub repository](https://github.com/ThGaskin/Migration_flows), which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here.
 
 # Migration estimates
 Data is available in both NetCDF (`.nc`) and CSV (`.csv`) formats. The NetCDF format is more compact and pre-indexed, making it suitable for large files. In Python, datasets can be opened as [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) objects, enabling coordinate-based data selection.
@@ -50,11 +50,6 @@ It is stored thematically in different folders, and most folders again contains
 explain the specific sources and imputation methods. All data is given *both* as a `.csv` file and a `.nc` file, and 
 follows the ISO3-naming convention outlined in the main README.
 
-> [!NOTE] 
-> This is a reminder that all data is stored in this repository using git LFS (large file
-> storage); if you wish to clone the repository with the data, you should follow the instructions
-> from the main README. You can still download the files manually from the webpage.
-
 ## Training_data
 This folder contains all the tensors used to train the neural network. All data is given as a PyTorch
 tensor (`.pt`) and can be loaded using `torch.load()`. The folder contains targets, weights, masks, input covariates (scaled