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Agriculture contributes approximately 80% of ammonia (NH3) emissions globally and in the United States, with major loss pathways including animal housing, manure storage, and land application of manure and synthetic fertilizers. Although NH3 is not itself a greenhouse gas, it is a key precursor of nitrous oxide (N2O), a potent greenhouse gas. Current housing NH3 emission models, including those implemented in the open-source Ruminant Farm Systems (RuFaS) model, rely on generalized parameters that may not adequately represent region- and management-specific variability, particularly in naturally ventilated barns. The objective of this study was to generate high-temporal resolution housing NH3 concentration data using IoT-based sensors to inform refinement of housing NH3 modeling in RuFaS toward more context-specific simulations. Measurements were conducted in a naturally ventilated free-stall dairy barn housing approximately 600 lactating cows with solid flooring and sand bedding in Harford, New York, USA. Manure was mechanically scraped 3 times per day. A total of 7 electrochemical NH3 sensors (Cynomys, Arenzano, Italy) were deployed evenly throughout the barn at a height of 2 m. Ammonia concentrations were continuously monitored from April 2025 to January 2026 at 10-min intervals. Hourly averages were used to assess diurnal patterns, and monthly averages were calculated to evaluate seasonal trends. Indoor temperature was monitored concurrently. Indoor temperature increased from 12.04±1.52 °C in April to 24.67±0.46 °C in July, before declining to 4.32±0.89 °C in January. Hourly NH3 concentrations ranged from 0.447±0.497 ppm to 0.714±0.369 ppm, with an overall mean of 0.554±0.088 ppm. Minimum concentrations occurred around 12:00, while maximum concentrations were observed at 23:00. Monthly mean NH3 concentrations ranged from 0.413±0.090 ppm to 0.752±0.618 ppm, with an overall mean of 0.594±0.133 ppm; the lowest and highest monthly averages occurred in April and August, respectively. These concentration levels are generally consistent with ranges reported in the literature for dairy housing. These measurements provide a high-throughput dataset capturing diurnal and seasonal variability of housing NH3 concentrations in a naturally ventilated dairy barn. While concentration data alone are insufficient to directly calibrate housing NH3 emission models, the observed temporal patterns establish essential boundary conditions for subsequent emission estimation when combined with ventilation rates. In this context, the dataset supports future derivation of NH3 emission fluxes needed to evaluate and calibrate housing NH3 submodules in whole-farm simulation frameworks such as RuFaS. Ongoing work will integrate ventilation estimates to quantify emission fluxes.