This chapter provides an overview of current and past efforts in data sharing and data standardization in metabolomics. Reproducibility for metabolomics results is challenging and the topic is vast. Here we focus on best practices and existing community efforts in data handling. Several elements are needed to increase reproducibility. Firstly, a framework for reporting experimental details containing minimum information guidelines is needed. Secondly, the community needs to adhere to such guidelines (and encouraged) for sharing experimental data publically. The reporting framework should also be supported by the journals, reinforced by the reviewers and implemented by the data repositories. Data sharing should follow the standards framework, i.e., use an open access data format (when possible), enriched with metadata and containing the complete set of study files—not just the raw files, but also QC and QA samples, blanks, the run order, etcetera. It is equally important to include data processing parameters for the individual data analysis steps.