From Complexity to Simplicity: DVCLI Streamlines Dataverse Use for Researchers

Managing research data can be complex - especially when dealing with large datasets, multiple contributors, or automated workflows. To address these challenges, the open-source Dataverse Command Line Interface (DVCLI) was presented. Developed by SimTech’s Research Data & Software Engineer Jan Range, DVCLI provides a fast, safe, and user-friendly way to interact with Dataverse instances via the command line - no programming experience required.

DVCLI is built on top of the Rust-Dataverse library, also created at SimTech, and uses the Rust programming language to ensure high performance, memory safety, and robust error handling. It is designed to streamline typical research data management workflows, especially in automated or large-scale settings.

Until now, users who wanted to programmatically interact with Dataverse had two main options: writing custom scripts using Python or JavaScript libraries, or manually sending HTTP requests via shell scripts. Both approaches require substantial technical expertise and are prone to errors.

DVCLI eliminates this barrier by bundling complex API interactions into a single, easy-to-use binary. Each command is purpose-built and often encapsulates entire workflows - such as uploading files, publishing datasets, or linking metadata - into one concise instruction. This greatly reduces the time and effort required to manage research data, and ensures consistency across systems and projects.

DVCLI offers:

• Authentication Management via the operating system’s keychain
• Collection and Dataset Operations (create, edit, publish, link)
• Flexible File Uploads, including direct S3 uploads for datasets >10GB
• Resumable Uploads, supporting interruptions and large transfers
• Cross-platform Support (works on any OS with no dependencies)

This makes DVCLI especially valuable in high-performance computing (HPC) environments, automated data pipelines, and collaborative research projects where reproducibility and efficiency are critical.

To help users get started, Jan Range has collaborated with Harvard’s Institute for Quantitative Social Science (IQSS) to expand the Dataverse Recipes repository. This GitHub collection now includes a dedicated section for DVCLI, featuring real-world examples and reusable snippets that demonstrate how to perform common tasks with the new tool: DVCLI Recipes on GitHub

With DVCLI, SimTech – and especially Jan Range – contribute a practical and well-designed tool to the research community. By removing technical hurdles and making Dataverse easier to use in everyday workflows, DVCLI helps researchers spend less time on data handling and more time on their actual research.