This seminar, hosted by the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS), was held on 27 March 2025. Agenda: Improving Research Transparency: An experiment at a NASA SMD data repository, Rebecca Ringuette Slides Leading Journal Practices for Data and Software Citations, AGU Case Study, Kristina Vrouwenvelder Examples of Journal Challenges making Data and Software Citations Machine Actionable, Shelley Stall Helping Researchers Navigate: ESIP’s Open Science and Data Help Desk, Brian Sedora Recording: https://youtu.be/kalFEG7jUVc This seminar provides insight into the inner workings of data and software citation through the journal production process to ensure that your role in publishing peer-reviewed papers supports the proper linkage of data and software citations to the publication. We took a deep-dive on the steps necessary for proper machine-actionable data and software citations that result in data and software creators receiving automated attribution when a new peer-reviewed paper is published. AGU has made significant progress on this challenge by working with our authors, staff, editors, and reviewers – as well as the journal infrastructure with our publishing partner Wiley, and our many collaborators in the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS). AGU journal’s data citations have increased from 1% in 2019 to 72% in 2024 (and rising). Software citations have increased from 0.2% to 25% in the same time period. Data citations are mandatory for AGU journals, where software citations are only required by some journal editors depending on the research. This year there has been greater interest from researchers to ensure that data and software citations are properly included, formatted, and machine actionable. As institutional promotion and tenure policies shift to include data and software sharing, the importance of reliable machine-actionable linkage between research data and software to the research outcomes is accelerating. Funder requirements have also shifted to require data sharing and citation in papers. The interlinking of these research objects is becoming a critical component of assessing impact, funding prospects, and career advancement for authors. This 90-min seminar introduced leading practices for ensuring authors include properly formatted data and software citations, journal staff actions to ensure submitted papers include the proper information, guidance to editors and reviewers on techniques for engaging their authors on including and improving the needed data and software citations, and requirements in publisher production workflows and down-stream infrastructure that enables the automated attribution and linkages of digital objects to publications. We will use real-world examples to demonstrate properly prepared data and software citations as well as what happens when things go awry. Objectives For journals: editors, staff, reviewers, and the journal production team will find the recommended guidance immediately applicable for all disciplines. For researchers: leading practices in preparing your data and software citations such that they are managed properly by your journal, machine-actionable, and properly linked to the paper. We will also demonstrate ways to check our published papers to validate proper processing. The materials for the session were developed from two primary resources: Stall, S., Bilder, G., Cannon, M. et al. Journal Production Guidance for Software and Data Citations. Sci Data 10, 656 (2023). https://doi.org/10.1038/s41597-023-02491-7 AGU’s Data and Software Citation Pilot project which concluded in 2023, educating editors, reviewers, and staff on data and software citations and how to support authors to ensure their manuscripts include proper data and software citations. In 2024, over 72% of AGU published papers included a data citation, and 25% had a software citation (Vrouwenvelder, 2024). This is an incredible increase from 2019 where only 1% of papers include a data citation, and 0.2% a software citation. The pilot project was partially funded by an NSF grant.