Advances in imaging of developing embryos in model organisms such as the fruitfly, zebrafish, and mouse are producing massive data sets that contain 3D images with every cell and readouts of signaling activity in every cell of an embryo. In Zebrafish embryos, determining the locations of nuclei is crucial for the study of the spatial-temporal behavior of these cells and the control of gene expression during the developmental process. Traditional image processing techniques suffer from bad generalizations, often relying on heuristic measurements that narrowly applies to specific data types, microscope settings, or other image characteristics. Machine learning techniques, and more specifically convolutional neural networks, have recently revolutionized image processing and computer vision. A well-known challenge in developing theses algorithms is the lack of curated training data. We developed a new, manually-curated nuclei segmentation data set for four complete embryos containing over 8,000 cells each. The whole-mount zebrafish embryos at different development stages were hand-labeled with 3D volumetric segmentation of nuclei. Two full embryo data sets were used for training the 3D nuclei instance segmentation network NISNet3D, and the other two embryos were used to validate the training results. We provide both qualitative and quantitative evaluation results for each of the volumes using multiple evaluation metrics. We also provide the fully curated and manually segmented embryo data sets, along with raw images, for the image processing community.