The ongoing digitization of medicine is opening up new perspectives for patient-specific modeling, simulation, and prognosis of orthopedic healing processes. This study presents a unique database built up over five years that includes patients with fractures of the long bones of the upper and lower extremities (tibia, femur, radius, and humerus). The database currently contains 100 fully documented cases, based on a standardized, specially developed workflow [1]. Each data set includes multimodal information, including imaging data (Computed Tomography, X-ray), motion analyses using the XsensTM motion capture system, and musculoskeletal simulations using AnyBodyTM software. For patients with lower-extremity fractures, the team also recorded ground reaction forces using instrumented soles. Andres et al. [2] describe the methodological principles and technical capabilities of these combined measurement systems. Based on this data structure, the creation of digital twins for orthopedic-traumatological applications was established [3]. This human digital twin approach enables patient-specific mapping of biomechanical analyses and the simulation of individual stress scenarios. In addition, these models can be used to predict healing processes and provide quantitative support for therapy decisions. For example, the workflow enabled prediction of the effects of individual partial loads on mechanical stability and healing progress in tibial head fractures [4]. The database presented here thus provides a basis for developing adaptive and predictive digital twins. By linking imaging, motion data, and biomechanical simulation, patient-specific factors influencing fracture healing can be systematically analyzed. In addition, the database opens new possibilities for machine learning, particularly for automated classification, pattern recognition, and the prognosis of individual healing processes. This unique data basis represents an important step toward data-driven, personalized orthopedics and trauma surgery. In the future, it will be continuously expanded and supplemented with additional measurement methods to further improve the precision of digital twins and open new clinical fields of application.