This repository provides the supplementary code and data associated with the paper: "Data-Driven Sparse Sensor Selection for Observing-Network Optimization and Its Impact on Data Assimilation". The repository contains a demonstration implementation of the data-driven sparse sensor selection method described in the paper. The provided Jupyter notebook illustrates the procedure for selecting observation locations and reconstructing spatial fields from sparse observations. Due to data usage restrictions, the original dataset used in the study cannot be redistributed. Instead, a sample dataset and precomputed matrices are provided to allow users to reproduce the workflow demonstrated in the notebook. The repository includes: • Python implementation of the sparse sensor selection algorithm • A Jupyter notebook demonstrating the workflow • A sample dataset for demonstration purposes • Precomputed matrices required for the algorithm The code was tested with the following environment: Python 3.11.14scikit-learn 1.8.0 Users can reproduce the example by creating the conda environment and running the notebook provided in the repository. This repository serves as supplementary material accompanying the paper.