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Soil moisture and soil temperature (SMST) are important state variables for quantifying exchange of heat and water between land and atmosphere. Yet, long-term regional-scale in-situ SMST measurements are scarce on the Tibetan Plateau (TP), even fewer are available for multiple soil depths. "Tibet-Obs" is such a long-term regional-scale SMST observatory in the TP established 10 years ago that includes three SMST monitoring networks, i.e., Maqu, Naqu, and Ngari (including Ali and Shiquanhe), located in the cold humid area covered by short grasses, the cold semiarid area dominated by tundra, and the cold arid area dominated by desert, respectively. This paper presents a long-term (~10 years) SMST profile dataset collected from the Tibet-Obs, which includes the original in-situ measurements at a 15-min interval collected between 2008 and 2019 from all the three networks and the spatially upscaled data (SMups and STups) for the Maqu and Shiquanhe networks. The quality of the upscaled data is proved to be good with errors that are generally better than the measured accuracy of adopted SMST sensors. Long term analysis of the upscaled SMST profile data shows that the amplitudes of SMST variations decrease with increasing soil depth, and the deeper soil layers present later onset of freezing and earlier start of thawing and thus shorter freeze-thaw duration in both Maqu and Shiquanhe networks. In addition, there are notably differences noted between the relationships of SMups and STups under freezing conditions for the Maqu and Shiquanhe networks. No significant trend can be found for the SMups profile in the warm season (from May to October) for both networks that is consistent with the tendency of precipitation. Similar finding is also found for the STups profile and air temperature in the Shiquanhe network during the warm season. For the cold season (from November to April), a drying trend is noted for the SMups above 20 cm in the Maqu network, while no significant trend is found for those in the Shiquanhe network. Comparisons between the long-term upscaled data and five reanalysis datasets indicate that none of current model-based products can reproduce the seasonal variations and inter-annual trend changes of measured SMST profile dynamics in both networks. All the products underestimate the STups at every depth, leading to earlier onset of freezing and later onset of thawing, which essentially demonstrates the current model are not able to adequately simulate winter conditions on the TP. In short, the presented dataset would be valuable for evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change. The dataset is available in the 4TU.ResearchData repository at https://doi.org/10.4121/20141567.v1. [ABSTRACT FROM AUTHOR]