Along with the recent popularization of live-imaging technologies, various types of microscopy images are becoming available in public databases. Quantitative data such as positions and shapes of nuclei and cells, and their temporal changes obtained from these images are important resources for understanding biological phenomena as dynamical systems. Such resources can be used in various kinds of computational analysis including phenotype screening and mathematical modeling. Here, we develop a new resource of quantitative data on nuclear division dynamics in C. elegans RNAi-treated embryos. Quantitative data were extracted from two-dimensional time-lapse differential interference contrast microscopy images in a public database, Phenobank, using our newly developed automated image processing system. They consist of 1,579 datasets of quantitative data for RNAi-treated embryos. These datasets include three sets of data for each of the genes (i.e. 518/549 of all genes) that exerted defects in embryogenesis without inducing F0 sterility when depleted individually by RNAi. Each data contains the outlines of nuclear regions and their temporal changes. All data were verified through manual error correction, and annotated with cell name and anterior-posterior axis. To demonstrate the usage of such resource, we applied it to computational phenotype screening on female pronuclear migration. In this screening, fifteen candidate genes were selected for faster or slower migration by calculating the maximum migration speed. Seven out of the fifteen genes were confirmed to reproduce each of the phenotype in our independent experiments. Among them, sds-22 and F44B9.8 exhibited significantly faster and slower migration respectively than wild-type. sds-22(RNAi) and F44B9.8(RNAi) embryos expressing GFP::tubulin exhibited markedly larger and smaller asters respectively, consistent with the nuclear tracking along microtubules mechanism. This resource will be openly available in SSBD database (http://ssbd.qbic.riken.jp).