Recent technological advances in digital microscopy and image analysis have produced a large amount of quantitative data of spatiotemporal dynamics of biological objects such as molecules, cells, and organisms. These quantitative data can be used to reveal the nature of dynamical behaviors of biological phenomena. However, these data are difficult to reuse for further analysis because they are often scattered over the Internet in different formats. There is a crucial need in bringing these data together in a coherent and systematic manner. We developed Systems Science of Biological Dynamics database (SSBD) as an open repository for quantitative data and microscopy images (http://ssbd.qbic.riken.jp). SSBD provides quantitative data in a unified format (BDML: Kyoda et al., 2015) and through REST API (Representational State Transfer Application Programming Interface). SSBD also provides image data in the original format by using OMERO platform. SSBD currently provides 313 sets of quantitative data and 194 sets of microscopy images for single molecules, nuclei, and whole organisms in a wide variety of model organisms from E. coli to mouse. For example, SSBD includes 186 sets of nuclear division dynamics data of wild-type and RNAi-treated C. elegans embryos during the first three rounds of cell division and the corresponding 4D differential interference contrast microscopy images. One set of nuclear division dynamics data of wild-type C. elegans embryo from 4- to 350-cell stage, and 11 sets of behavioral dynamics data of goa-1 C. elegans adults are also included. A web-based viewer allows users to visualize quantitative data in time and 3D space on-demand without any additional plugin. In addition, SSBD provides software tools for data visualization and analysis such as Phenochar and BDML4DViewer. Phenochar is a software tool for extracting phenotypic characteristics from the data in BDML format. BDML4DViewer is an ImageJ plugin to read and visualize quantitative data in BDML format. The open-source version of SSBD for managing quantitative data is also available at http://github.com/openssbd/. It enables other research groups to setup their own databases independently for storing and sharing their data. SSBD will support data-driven biology by accelerating the reuse of quantitative data and microscopy images, and development of software tools for data analysis.