Advancements in Research Data Management at SimTech

May 31, 2024

Effective Research Data Management (RDM) is essential in today's data-driven scientific environment. It ensures data integrity, facilitates data sharing, and enhances the reproducibility of research findings. At SimTech, RDM is more than just managing data; it is about fostering innovative research and enabling cross-disciplinary collaboration. Our tools and practices are designed to support researchers in efficiently managing their data, thereby accelerating scientific discovery and technological progress. Our RDM team ensures that our practices are robust, efficient, and continually evolving.

Introducing EasyDataverse 0.4.1

We are thrilled to announce the release of EasyDataverse 0.4.1, a significant upgrade to the Python software designed for creating datasets and uploading files to Dataverse (DaRUS). This latest version brings a host of improvements that make data management more seamless and flexible than ever. With the ability to connect to any Dataverse installation and dynamically generate metadata configurations, EasyDataverse 0.4.1 simplifies the process of creating datasets and uploading files. The new parallelized uploads, compatible with S3, now allow for the efficient handling of larger files, over 100GB, ensuring that even the most substantial datasets can be managed effortlessly.

Enhanced Performance with DVUploader 0.2.3

Our commitment to improving RDM tools is further exemplified by the release of DVUploader 0.2.3. This updated version focuses on enhanced stability and performance, making bulk data uploads more efficient. Its S3 compatibility means that uploading large data files, exceeding 100GB, is now more straightforward and effective. These enhancements make DVUploader an indispensable tool for researchers dealing with extensive datasets.

Launch of pyDataverse 0.3.2

In another stride forward, the pyDataverse Working Group, led by Jan Range, has launched pyDataverse 0.3.2. This general-purpose library is designed to facilitate the administration and interaction with Dataverse instances. The new version introduces asynchronous functionality, allowing for the concurrent handling of multiple requests. This feature is particularly beneficial for tasks such as parallel dataset harvesting, which is crucial for gathering training data from various Dataverse instances. More information about the working group can be found on their website.

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