This demonstration will show participants a new data-gathering tool. While the tool is being developed primarily to expand the breadth and depth of comparative international education research, a demonstration of the tool may be useful for those working in other related areas, such as public policy and survey research, who are interested in developing data-gathering tools. This web-based tool is free and publicly available, developed with funding from the International Activities Program (IAP) at the National Center for Education Statistics (NCES), U.S. Department of Education, and in collaboration with the American Institutes for Research (AIR) and other contractors. This data-gathering tool, the International Cross-Time, Cross-System (XTXS) database (see http://intledstatsdatabase.org/default.aspx), contains information relevant to quantitative analyses, including the empirical modeling of international data. It places information collected from multiple sources and multiple points in time in one database for use by researchers. The XTXS database currently includes assessment data from three international surveys—PIRLS, PISA, and TIMSS—together with relevant background and contextual variables from these and other data sources as well as additional economic and demographic data from the World Bank and UNESCO. New data (both quantitative and qualitative) to be added over the next several months include participation measures, outcome measures, school contexts, student characteristics, education system characteristics, and more detailed national/subnational characteristics. Researchers can submit their own data for review by the XTXS steering committee for possible inclusion in the database. Data from the XTXS database gets outputted to a flat file, with rows corresponding to nations or subnational units and columns corresponding to variables. It is available both in a wide format (years nested in columns/variables) and in a long format (years nested in rows/countries) to meet the specific needs of the researcher and can be imported into SAS, SPSS, or Stata for statistical analysis.