Understanding and improving global mobility has gained increased interest during the last decades. However, studies on the railway network are spatially limited so far, mostly investigating the domestic network of a country. Data availability is a major limiting factor for the analysis of these networks. Despite the increased open data movement, network operators are often reluctant to publish their infrastructure and passenger data. Existing large-scale studies usually make use of hand-collected data, for instance, based on historical cartographies. In this paper, we develop and implement a methodology to extract the worldwide railway skeleton network from the open data repository OpenStreetMap, where nodes are stations/waypoints and links are weights with information such as spatial distance, gauge, and maximum speed. We describe how we solved several data cleansing and scalability issues and developed network simplification techniques, in order to obtain an adequate representation of the network. We show that the network breaks down into few large and many small components. Furthermore, we show that this public data set can be used for efficient minimum travel time estimation between stations or cities. This paper leads to the development of a new research data set and contributes toward the ability of analyzing global mobility patterns, particularly regarding multimodality and cross-country transportation.