Update README.md

README.md

@@ -6,11 +6,14 @@ pretty_name: 'Deep learning four decades of human migration: datasets'
 tags:
 - arXiv:2506.22821
 ---
+Deep learning four decades of human migration: datasets
+---
 
 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.
+The repository contains three folders:
 
-# 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.
+# Estimates
+This folder contains all the 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.
 
 Each dataset uses the following coordinate conventions:
 
@@ -44,7 +47,7 @@ Each dataset includes a `mean` variable (mean estimate) and a `std` variable (st
 
 An ISO3 conversion table is also provided.
 
-# Input covariates
+# Data
 The `Data` contains all the data used to train, evaluate, and test the neural network.
 It is stored thematically in different folders, and most folders again contains its own `README` file to further
 explain the specific sources and imputation methods. All data is given *both* as a `.csv` file and a `.nc` file, and