Context The KIMIA Path960 is a dataset that was proposed in the following paper: A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam Kalra, and H.R.Tizhoosh; The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Honolulu, Hawaii, USA from Nov. 27 to Dec 1, 2017. This paper introduced a new dataset of histopathology images "KIMIA Path960". From a collection of more than 400 whole slide images (WSIs) of muscle, epithelial and connective tissue etc., we selected 20 scans that "visually" represented different texture/pattern types (purely based on visual clues). We manually selected 48 regions of interest of same size from each WSI and downsampled them to 308x168 patches. Hence, we obtained a dataset of 960(=20x48) images. The images are saved as color TIF files although we do not use the color information (i.e., the effect of staining) in our experiments. Content This paper introduced a new dataset of histopathology images "KIMIA Path960". From a collection of more than 400 whole slide images (WSIs) of muscle, epithelial and connective tissue etc., we selected 20 scans that "visually" represented different texture/pattern types (purely based on visual clues). We manually selected 48 regions of interest of same size from each WSI and downsampled them to 308x168 patches. Hence, we obtained a dataset of 960(=20x48) images. The images are saved as color TIF files although we do not use the color information (i.e., the effect of staining) in our experiments. Acknowledgements Photo by Charles ???? on Unsplash Data is from A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam Kalra, and H.R.Tizhoosh; The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Honolulu, Hawaii, USA from Nov. 27 to Dec 1, 2017. Inspiration Classification problem for scientists