Data curation is vital in catalysis for several reasons. Firstly, it facilitates data sharing among researchers, promoting collaboration and accelerating scientific discovery. Secondly, it ensures the reproducibility of experimental results, which is a cornerstone of scientific integrity. Thirdly, curated data can be used to develop machine learning models and perform computational simulations, leading to new insights and innovations in catalytic processes.
