Technical Abstract: A typical agricultural experiment produces vastly more data than can be reproduced in a research paper. Thus, data provided in a paper often are not detailed enough to allow the analysis to be checked, nor can the data be re-analyzed, perhaps together with data from other experiments or using techniques developed long after the original paper was published. Limitations to re-analysis are particularly unfortunate because agricultural research increasingly deals with issues that require examining large or detailed sets of data. Journals increasingly favor publishing data in digital supplements this improves the likelihood that papers will be cited while reducing the length of journal articles. In this paper we argue that formal methods for publishing datasets from agricultural research should be established, fully analogous to publishing research findings. Criteria for determining whether a dataset merits publication include originality, utility, significance, completeness, quality, and usability. Mechanisms for distributing datasets also merit attention. Ensuring long-term accessibility, respecting intellectual property rights, and managing possible corrections or modifications to datasets are among issues that require consideration.