Data mapping describes the exchange of variables between different, usually non-matching grids for storing data. As different physics require different physical constraints, so do different simulation require different mesh properties. This makes data mapping a crucial part when coupling single-physics simulations into a multi-physics simulation. However, the tradeoff for the available computationally efficient methods is usually low accuracy order. Such a method is the nearest-neighbor mapping method, which relies on a computationally inexpensive mapping algorithm and shows a first-order accuracy, as it is based on a constant interpolation. A second-order projection-based mapping method nearest-neighbor-gradient aims to improve the accuracy order of the nearest-neighbor mapping, while preserving the low computational costs. This is achieved through the extension of the existing method by considering additional gradient data information and applying a Hermite interpolation, in order to balance out both the computational efficiency and the accuracy of the mapping. In this thesis, we implemented this method by extending the coupling library preCICE, which uses state-of-the-art algorithms for coupling partitioned multi-physics simulations in a black-box manner. We confirmed the theoretical observations of the expected second-order accuracy of the method and we found that the method shows the best convergence order by contrast with the existing mapping methods, including radial basis function mappings. It also performs just as well as the existing projection-based method in terms of computational cost and outperforms the radial basis function mapping in respect of runtime costs.