Quantitative sensory testing and classical pain model dataset in 127 healthy volunteers

Experimental pain models are integral to human pain research and the development of analgesic drugs. Of many options available, it is possible to identify pain models that reliably predict clinical analgesic efficacy in cost-effective laboratory settings. However, translating experimental findings to clinical practice is imperfect, and laboratory models can only partially replicate specific clinical pain conditions. The present dataset originates from a study that addressed this gap between experimental and clinical pain. The broader study investigated whether patterns of clinical neuropathic pain could be induced, at least to some degree, in healthy human subjects using topical capsaicin application to induce hypersensitization. This dataset provides the baseline control measurements from untreated skin that were obtained as part of this research. The study enrolled 127 healthy volunteers who underwent comprehensive sensory testing. A quantitative sensory testing (QST) battery developed for clinical diagnosis by the German Research Network on Neuropathic Pain was utilized to characterize baseline sensory function and pain sensitivity. The dataset is provided in both its original (raw) and transformed formats. Key pain data are presented as a 127×23 matrix (127 cases and 23 QST variables). Transformations include sign inversion for certain variables to align pain sensitivity, directionality, unit conversions, and log transformations based on psychophysical principles. A separate metadata file documents participant characteristics, such as age, sex, test site allocation (hand or foot), and body side tested. This dataset has significant potential for reuse. Researchers can use it to benchmark new pain assessment tools, validate experimental pain models, and develop or test statistical and computational methods for analyzing sensory and pain data. The inclusion of demographic variables allows for the investigation of how age and sex influence pain perception. The structure of the dataset supports secondary analyses, integration into machine learning workflows as independent validation data, and use as a reference in future pain research. All data are anonymized and formatted to facilitate reproducibility and secondary use.