Repository Description This repository presents an extensive collection of published studies compiling non-destructive test (NDT) results on the identification of reinforcement properties embedded with reinforced concrete members. The NDT methods include the covermeter (CM) and ground-penetrating radar (GPR). The datasets collectively comprise 3,463 test results from 26 published studies. Covermeter results are organised into two separate datasets for rebar diameter measurements and concrete cover depth measurements. The datasets are distributed as follows: CM (Rebar Diameter): 835 tests from 10 studies. CM (Cover Depth): 1,844 tests from 15 studies. GPR: 784 tests from 15 studies. Across each NDT, the measured outputs include the estimated rebar spacing, number of bars, rebar diameter, concrete cover depth, and effective depth. Database Structure Each database contains a mixture of numerical and categorical variables, with the following characteristics: CM (Rebar Diameter): 17 independent variables and 3 dependent variables. CM (Cover Depth): 18 independent variables and 3 dependent variables. GPR: 18 independent variables and 4 dependent variables. These variables cover the material age, location, geometry, NDT parameters, and specimen design. Of the total datasets, 681 CM rebar diameter measurements, 1,284 CM cover depth measurements, and 574 GPR measurements were conducted on in-situ on in-situ structures, while the remainder were performed on laboratory-cast specimens. The datasets include numerical results from reported directly in the relevant publication, with no interpretation performed on radar or scan maps. Numerical data from visualized results (e.g., scatter plots) were extracted using an open-source computer vision-assisted software for high precision. Project and Documentation These databases were compiled as part of the EU-funded ReCreate project. The ReCreate project researches the process of reusing precast concrete elements through four real-life deconstruction and reuse pilots in Europe. The project aims to assess and improve the technical feasibility of a circular life cycle for structural precast elements across the entire value chain from disassembly, quality control, design for reuse and reconstruction. A manuscript applying this dataset to empirical and machine learning models is under review and will be linked here upon publication. For detailed descriptions of nomenclature, abbreviations, and assumptions, refer to the Database Guide PDF included in this repository.