New Releases Boost Performance and Flexibility for Dataverse Integration in Python

Effective and sustainable Research Data Management (RDM) is a cornerstone of modern, reproducible science. At the Cluster of Excellence SimTech, we are committed to providing researchers with tools that make data publication and sharing both robust and user-friendly. As part of this effort, two key Python libraries developed at SimTech - EasyDataverse and Python DVUploader - have recently been updated to offer improved performance, usability, and security.

With increasing volumes of complex simulation data, the need for structured, transparent, and accessible data workflows has never been greater. SimTech supports researchers throughout the entire data lifecycle - from planning and documentation to long-term preservation and open sharing. Tools like EasyDataverse and Python DVUploader are central to this infrastructure, enabling direct integration with Dataverse, a widely used repository platform for research data.

Designed to simplify the management of Dataverse datasets in Python, EasyDataverse now includes several enhancements:

• Dynamic license retrieval: Licenses are now pulled directly from the connected Dataverse instance, supporting both standard and customized terms. This enables researchers to better align their datasets with institutional or project-specific licensing requirements.
• Improved stability and performance: Under-the-hood improvements, including tighter integration with DVUploader, result in faster and more secure data handling.
• Growing community adoption: With over 20 stars on GitHub, EasyDataverse is gaining momentum and is increasingly recognized by the wider Dataverse community.

Python DVUploader: Optimized for Large-Scale Uploads
Built for high-volume data transfer, particularly relevant for simulation-based research, DVUploader now features:

• Optimized upload process: Uploads are now faster and more reliable, even for large datasets.
• Proxy support: Users can optionally route uploads via a proxy server, enhancing security and meeting the IT requirements of institutions like the University of Stuttgart.

Check https://github.com/gdcc/easyDataverse and https://github.com/gdcc/python-dvuploader for more.

These improvements are the result of ongoing development contributions by Jan Range, SimTech’s Research Data Software Engineer. He is responsible for Research Data Management within SimTech and plays a key role in the continuous advancement of Dataverse-related tools. As a PhD researcher, he is also involved in the development of EnzymeML and contributes to various web applications and client libraries (primarily in JavaScript, Python, and Rust). In addition, he leads the pyDataverse Working Group, which fosters collaboration and best practices within the Dataverse developer community.

These updates are part of SimTech’s broader strategy to promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles. By improving the tools researchers rely on, SimTech aims to lower the barriers to data sharing and ensure that high-quality research data can be preserved and reused across disciplines.

Find out more about SimTech’s Research Data Management services and tools here: https://www.simtech.uni-stuttgart.de/rdsm/.