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Geochemistry is a data-driven discipline. Modern laboratories produce highly diverse data, and the recent exponential increase in data volumes is challenging established practices and capabilities for organizing, analyzing, preserving, and accessing these data. At the same time, sophisticated computational techniques, including machine learning, are increasingly applied to geochemical research questions, which require easy access to large volumes of high-quality, well-organized, and standardized data. Data management has been important since the beginning of geochemistry but has recently become a necessity for the discipline to thrive in the age of digitalization and artificial intelligence. This paper summarizes the landscape of geochemical databases, distinguishing different types of data systems based on their purpose, and their evolution in a historic context. We apply the life cycle model of geochemical data; explain the relevance of current standards, practices, and policies that determine the design of modern geochemical databases and data management; the ethics of data reuse such as data ownership, data attribution, and data citation; and finally create a vision for the future of geochemical databases: data being born digital, connected to agreed community standards, and contributing to global democratization of geochemical data. 

Abstract Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains. This article is categorized under:Science of Water > Methods