This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~ 35 000 images from 50 cell lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 50 timeseries of classes corresponding to the lifespan of the 250 cells. It is related to the trained network doi.org/10.5281/zenodo.5553862 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) Microscopy data type: Brightfield images with 3 stacks Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS Cell type: Budding yeast wild type cell (BY4742) File format: .tif (16-bit RGB, 1 color per z-stack) + .mat Image size: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm) Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

{"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for classification of images from yeast cell lifespans - DetecDiv (id01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553862"]}