This dataset presents daily / hourly raw data on atmospheric pollen count and identification in Sierra Nevada (Granada, Spain) during the period June-September of the years 2016-2019. The sampling was carried out using a Hirst-type suction volumetric collector (model Lanzoni 2000, Lanzoni srl, Bologna, Italy), installed in the University Hostel, Hoya de la Mora (37º, 05'N, 3º 23'W, 2500 m a.s.l.), in Sierra Nevada Natural and National Park, southeastern of the Iberian Peninsula. The sampler works permanently, 24h / day, sucking in a constant volume of air of 10 l / min., and depositing all the particular material they contain on a filter impregnating with diluted silicone. The subsequent optical microscopy analysis of the samples, following the standardized protocol of the Spanish Aerobiology Network (Galán et al., 2007), enables the quali-quantitative identification of the different pollen types at the taxonomic level of family, genus and in some cases, even species. The resulting dataset contains records of the pollen types from the most representative wind-pollinated plants of the natural vegetation of the surroundings: juniper groves, pine groves, oak groves and psychroxerophilic pasture. Data on pollen transported from other nearby or remote locations of a radius of up to 30 km around the sampler are also included, the latter in the frequent episodes of Saharan dust intrusion in the southeast of the peninsula. In order to offer greater practicality to potential users, the data set is provided in both a wide and long format. In both formats 0 values indicate not pollen count for a taxon that was being sampled that year (identified at least once time). Regarding null values, in the long format indicate technical errors, while in the wide format indicate also that no information is available for a taxon over a whole sampling year. Date/time data correspond to CET and CEST time zone. This data set provides valuable information on atmospheric pollen in a recognized plant biodiversity hotspot within the Mediterranean context, which is essential for assessing the status of endemic species highly dependent on stable environmental conditions, and their response to the impact of climate change. In this context, data can help to establish conservation and recovery plans for the species whose survival is most threatened.
