Problem Definition: Bikeshare systems are expanding worldwide, but empirical research is hindered by the lack of high-frequency data on system status. Public bikeshare datasets typically include only trip start and end records, with no information on real-time station conditions (e.g., how many bikes or docks are available). This gap in data limits the analysis of operational performance and user service levels in bikeshare systems. Methodology/Results: We address this gap by collecting nearly minute-by-minute status data from Washington, D.C.'s Capital Bikeshare system over a three-year period (April 2019-September 2022). For each station and minute in this dataset, we record the number of available bikes and open docks, along with station location details. We also include data on dockless bikes introduced during the collection period. The raw data were cleaned to correct errors and can be connected to official trip records data. This high resolution dataset enables direct measurement of system performance. For example, analysis of August 2022 shows that bikes were completely unavailable (bike stockout) for approximately 1.94% of station-minutes, while all docks were occupied (dock stockout) for about 0.78% of station-minutes. We also propose a method for using the data to identify potential rebalance events. Managerial Implications: The availability of this granular status data opens new avenues for empirical research and decision-making in operations management. Researchers and practitioners can leverage it to improve demand forecasting models, design more effective rebalancing strategies for bike distribution, and rigorously measure system performance (e.g., availability and stockouts) over time. By providing previously unavailable insights into full system status, this dataset supports more informed operational planning and real-time management of bikeshare services.