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The rise of new experimental techniques, such as high-throughput combinatorial methods, and the availability of large data sets by means of the Internet have greatly increased the amount of data that must be managed by relatively small projects. Scientific data management systems developed for large projects are often not available, suitable, nor affordable for projects with lesser resources. Increasing numbers of open-source frameworks have made available numerous options for smaller facilities to build for themselves effective and robust data management solutions. We will present considerations of these options and a case study.