APA (7th ed.) Citation

Zwolak, J. P., Kalantre, S. S., Wu, X., Ragole, S., & Taylor, J. M. (2018). QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. PloS one, 13(10), e0205844. https://doi.org/10.1371/journal.pone.0205844

Chicago Style (17th ed.) Citation

Zwolak, Justyna P., Sandesh S. Kalantre, Xingyao Wu, Stephen Ragole, and Jacob M. Taylor. "QFlow Lite Dataset: A Machine-learning Approach to the Charge States in Quantum Dot Experiments." PloS One 13, no. 10 (2018): e0205844. https://doi.org/10.1371/journal.pone.0205844.

MLA (9th ed.) Citation

Zwolak, Justyna P., et al. "QFlow Lite Dataset: A Machine-learning Approach to the Charge States in Quantum Dot Experiments." PloS One, vol. 13, no. 10, 2018, p. e0205844, https://doi.org/10.1371/journal.pone.0205844.