{
 "id": "jiawensi/PortPy_Dataset",
 "owner": "jiawensi",
 "name": "PortPy_Dataset",
 "downloads": 122,
 "likes": 0,
 "last_modified": "2026-09-22T07:45:15.000Z",
 "stated_licence": "cc-by-nc-4.0",
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 "citation": "This dataset is derived from the publicly available NSCLC-Radiomics dataset hosted on [The Cancer Imaging Archive (TCIA)](https://www.cancerimagingarchive.net/collection/nsclc-radiomics/).\n\nOriginal Data Citation:\nAerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2014). Data From NSCLC-Radiomics (version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI \n\nThe original imaging data was processed by the authors of this repository by:\n- Adding beam configurations.\n- Creating influence matrices suitable for radiotherapy treatment planning optimization.\n- Adding benchmark plans using in-house automated treatment planning system **ECHO**.\n- Storing the data in the standardized [PortPy](https://github.com/PortPy/PortPy) data format for research and educational purposes.",
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 "reason": "The declared licence (cc-by-nc-4.0) is classified as having use restrictions.",
 "revision": "4528ff6c185f4a6f15441b16bd00144ef1dc51a4",
 "stated_evidence": "https://huggingface.co/datasets/jiawensi/PortPy_Dataset/blob/4528ff6c185f4a6f15441b16bd00144ef1dc51a4/README.md",
 "file_evidence": "https://huggingface.co/datasets/jiawensi/PortPy_Dataset/tree/4528ff6c185f4a6f15441b16bd00144ef1dc51a4",
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}