Despite the success of DFT simulations in computational materials science, existing data may not fully meet the requirements for advancing the design of ionic conductors. These data bases predominantly emphasize information generated at zero temperature, overlooking anharmonicity and temperature-induced effects. Furthermore, current high-throughput and machine-learning studies focus predominantly on Li and Na, overlooking other cations such as Mg, Ca, K, and even neglecting anions such as O and halides. Achieving a comprehensive understanding of ion transport phenomena requires a balanced and thorough analysis of these diverse ionic classes that hold technological relevance.