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The development and application of an algorithm to compute K€ oppen-Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid-Holocene (6k yBP) and the Pre-Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach. an easily reproducible method for deriving K€ oppen-Geiger climate classifications from CMIP/ PMIP climate model simulation outputs using accessible open source tools, the computations and single GIS processing steps are described in detail, to facilitate the reapplication of the presented method discussed. Thus, the aim of this work is not to compare the results of different climate models, for which the presented method would be suitable as well, but to present a reproducible method, application, or tool, to derive the K€ oppen-Geiger classifications as basis or input for further applications and research. Because there is clearly an increasing momentum towards open science (Hey and Payne 2015) in which the authors of this study want to participate, close attention is paid to the reproducibility of the presented method and its application. To support this, in addition to using open source software for the computations, all resulting and source datasets are provided by the authors.This study proceeds by first looking at related research of applying climate classifications to climate model simulations in Section 2. As described and applied in some of these studies, the here presented classification method can also be used to cross-validate and evaluate climate models. In the following sections of this article, we describe the data and methods applied in this study in Section 3. The CMIP/PMIP models and experiments are introduced in Section 3.1, the input variables for the classifications are described in Section 3.1.2 and the exact data sources for the classification maps presented in this article are given in Section 3.1.3. This is followed by a description of the process of creating the land masks needed for time slices with different sea levels in Section 3.2 and the interpolation procedure applied to the input data in Section 3.3. In Section 3.4, we describe the updated K€ oppen-Geiger classification scheme after Peel et al. (2007) and Kottek et al. (2006), which we implemented in this contribution. Section 4 concerns the actual practical implementation details of how the classifications are computed. The resulting classifications...

The implementation of a research data management infrastructure for a large interdisciplinary research project is presented here, based on well-established Free and Open Source Software for Geospatial (FOSS4G) products such as MapServer, MapProxy, GeoExt and pyCSW, as well as the (not primarily geospatial) open source technologies Typo3 and CKAN. The presented implementation depends primarily on the demands for research data management infrastructure by the funding research agency. It also aligns to theory and practice in Research Data Management (RDM) and e-Science. After the research project and related work in the field of RDM are introduced, a detailed description of the architecture and its implementation is given. The article discusses why Open Source and open standards are chosen to implement the infrastructure and provides some suggestions and examples on how to make it easier and more attractive for researchers to upload and publish their primary research data. Acknowledgments: First we want to thank and acknowledge the student staff contributors, Kai Volland, Daria Grafova and Yasa Yener, who helped to implement the presented system. We are grateful for all the comments, advice, ideas, inspiration and support from all members of the CRC806 and especially from the members of the Z2 project: Olaf Bubenzer, Andreas Bolten and Dirk Hoffmeister. We are also grateful to peers and co-workers within the GIS & RS working group at the Institute of Geography of the University of Cologne, especially Sebastian Brocks and Christoph Hütt, who helped to implement several GIS Data Services, Juliane Bendig for proofreading this article, and Constanze Curdt for her valuable work in RDM within our research group. Then we want to thank all the contributors of the open source software projects we apply to build the presented system; and finally we would like to thank the German Research Foundation (DFG) for its support and funding of the CRC806 research project.