The availability of high-fidelity catalysis data is essential for training machine learning models to advance catalyst discovery. Furthermore, the sharing of data is crucial to ensure the comparability of scientific results. In electrocatalysis, where complex experimental conditions and measurement uncertainties pose unique challenges, structured data collection and sharing are critical to improving reproducibility. and enabling robust model development. Addressing these challenges requires standardized approaches to data collection, metadata inclusion, and accessibility. To support this effort, we have developed an extensive data infrastructure that curates and organizes multimodal data from electrocatalysis experiments, making them openly available through the catalysis-hub.org platform. Our datasets, comprising 241 experimental entries, provide detailed information on reaction conditions, material properties, and performance metrics, ensuring transparency and interoperability. By structuring electrocatalysis data in web based as well as machine-readable formats, we aim to bridge the gap between experimental and computational research, allowing for improved benchmarking and predictive modeling. This work highlights the importance of well-structured, accessible data in overcoming reproducibility challenges and advancing machine learning applications in catalysis. The framework we present lays the foundation for future data driven research in electrocatalysis and offers a scalable model for other experimental disciplines.