This dataset contains annotations and annotated images for the "AgarCyto" samples used in this manuscript: A Segmentation-Free Machine Learning Architecture for Immune Land-scape Phenotyping in Solid Tumors by Multichannel Imaging. Shabaz Sultan, Mark A. J. Gorris, Lieke L. van der Woude, Franka Buytenhuijs, EvgeniaMartynova, Sandra van Wilpe, Kiek Verrijp, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor bioRxiv 2021.10.22.464548; doi: https://doi.org/10.1101/2021.10.22.464548 The .tar.gz file contains several multichannel images stored as TIFF files, and arranged in a folder structure that is convenient for matching the files to the annotations provided in the .json.gz file. We also provide an .h5 file that contains the final trained network that was used to generate the figures in this manuscript. Further information on the data can be found in the manuscript cited above. Instructions on how to use the annotations and the code can be found on our GitHub page at: https://github.com/jtextor/immunet

Additional funding information: JT and SS were supported by the Dutch Cancer Society - Alpe d'HuZes foundation (grant 10620). JT and FB were supported by NWO grant VI.Vidi.192.084. MG, LvdW, KV, and CF were supported by Dutch Cancer Society grants 10673 and 2017-8244. CF was also supported by a grant from Oncode Institute. EM was supported by a grant from the Hanarth Fonds (to JT).