Historical estimates for emissions of greenhouse gases, anthropogenic aerosol (BC, OC) and aerosol and ozone precursor compounds (SO2, NOx, NH3, CH4, CO, NMVOC) are key data needed for Earth System Models, Integrated Assessment Models and research more generally on aerosols, air pollution, and atmospheric chemistry. Current global emissions data sets have a number of shortcomings, including timeliness and transparency. We propose to construct a data system, building on current global efforts, to produce annually updated estimates of global emissions using a data-driven, open source framework. The basic methodologies to be used for this system have been documented in recent journal papers and are designed to complement and extend existing emission inventory efforts. Because energy and other driver data will be incorporated as part of the emissions data system, consistency over time and space can be examined (for example by examining the implied sectoral emissions factors). Emissions estimates can be extended to one year before present, albeit with additional uncertainty. A central component of the proposed system is the estimation of uncertainty. Current global emission estimates are generally not provided with uncertainty values. While uncertainty is challenging to calculate for these emissions, we can use current methodologies to provide transparent and consistent time, and country-specific estimates that will allow uncertainty estimates that are consistent across species. The data system will be constructed using the R open source platform. The use of an open source platform will enable the system to be released to researchers to perform their own research or develop alternative scenarios. This project will allow researchers, analysts, and policy-makers to access data by sector, fuel, and country that will allow an unprecedented community involvement in emissions research and data development.