Name: ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset (see our Wiki for details) Data type: 3D paired microscopy images (fluorescence and transmitted light) Microscopy data type: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594) Microscope: Leica SP8, HC PL APO 63x 1.40 NA oil objective Cell type: HeLa (fixed using an organelle-preserving protocol) File format: .tif (8-bit) Image size: 512 x 512 x 32 (Pixel size: x and y: 90 nm pixel size, z: 150 nm) Author(s): Christoph Spahn Contact email: c.spahn@chemie.uni-frankfurt.de, heilemann@chemie.uni-frankfurt.de Affiliation: Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany Associated publications: Unpublished Funding body(ies): M.H. and C.S.: German Science Foundation (grant nr. SFB1177). C.S.: European Molecular Biology Organization (short term fellowship 8589)

This v2 version of the fnet example dataset is a replicate of the v1 dataset where the data has been split in smaller regions of interest (512x512x32 from the 1024x1024x32 in v1).