@misc{https://doi.org/10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1,
  doi = {10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1},
  url = {},
  author = {Kalikadien, Adarsh V. and Valsecchi, Cecile and van Putten, Robbert and Maes, Tor and Muuronen, Mikko and Dyubankova, Natalia and Lefort, Laurent and Pidko, Evgeny},
  keywords = {Catalysis, Hydrogenation, Organometallics, High-throughput experimentation, Machine learning, Data science},
  title = {Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts},
  publisher = {4TU.ResearchData},
  year = {2024},
  copyright = {CC BY 4.0},
}