@misc{indiciaedaa74a21259c,
  title = {BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology},
  author = {Manubens-Gil, L. and Zhou, Z. and Chen, H. and Ramanathan, A. and Liu, X. and Liu, Y. and Bria, A. and Gillette, T. and Ruan, Z. and Yang, J. and Radojevic, M. and Zhao, T. and Cheng, L. and Qu, L. and Liu, S. and Bouchard, K. E. and Gu, L. and Cai, W. and Ji, S. and Roysam, B. and Wang, C.-W. and Yu, H. and Sironi, A. and Iascone, D. M. and Zhou, J. and Bas, E. and Conde-Sousa, E. and Aguiar, P. and Li, X. and Li, Y. and Nanda, S. and Wang, Y. and Muresan, L. and Fua, P. and Ye, B. and He, H.-y. and Staiger, J. F. and Peter, M. and Cox, D. N. and Simonneau, M. and Oberlaender, M. and Jefferis, G. and Ito, K. and Gonzalez-Bellido, P. and Kim, J. and Rubel, E. and Cline, H. T. and Zeng, H. and Nern, A. and Chiang, A.-S. and Yao, J. and Roskams,},
  year = {2022},
  doi = {10.1101/2022.05.10.491406},
  url = {https://www.biorxiv.org/content/10.1101/2022.05.10.491406v1},
  note = {Source identifier: 10.1101/2022.05.10.491406}
}