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Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes. 

Abstract Stable hydrogen and oxygen isotopic compositions (δ²H and δ¹⁸O, respectively) of animal tissues have been used to infer geographical origin or mobility based on the premise that the isotopic composition of tissue is systematically related to that of local water sources. Isotopic data for known‐origin samples are required to quantify these tissue–environment relationships. Although many of such data have been published and could be reused by researchers, differences in the standards used for calibration and analytical procedures for different datasets limit the comparability of these data.We develop an algorithm that uses results from comparative analysis of secondary standards to transform data among reference scales and estimate the uncertainty inherent in these transformations. We apply the algorithm to a compilation of known‐origin keratin data published over the past ~20 years.We show that transformation improves the comparability of data from different laboratories, and that the transformed data suggest ecophysiologically meaningful differences in keratin–water relationships among different animal groups and taxa.The compiled data and algorithms are freely available in the ASSIGNRr‐package to support geographical provenance research, and more generally offer a methodology overcoming several challenges in geochemical data integration and reuse.