Description
As research across disciplines becomes increasingly data-intensive, effective and user-friendly data management platforms are essential. Among the most widely adopted tools are Dataverse and SEEK, both designed to support FAIR (Findable, Accessible, Interoperable, Reusable) data practices. While each platform offers robust capabilities for data curation, sharing, and metadata management, there is a growing need to understand how they perform from a usability perspective, especially for researchers, data stewards, and clinicians who may not have specialized technical backgrounds.
This poster outlines the design and goals of an ongoing usability study comparing Dataverse and SEEK. Despite their widespread adoption in academic and clinical settings, little comparative research exists on how actual users interact with these platforms in everyday data workflows. Our aim is to fill this gap by evaluating how intuitive, efficient, and user-friendly these systems are for diverse user groups.
We are conducting a mixed-methods usability evaluation involving task-based testing, user surveys, and structured interviews. Participants will include researchers, data stewards, and clinicians with varying levels of experience in research data management. The study focuses on key user interactions such as dataset deposition, metadata annotation, access management, stress testing, and integration with external tools like ORCID and Git repositories.
The evaluation is guided by the following research questions:
How do users perceive the usability and learning curve of Dataverse versus SEEK?
What specific tasks or workflows present the greatest friction for different user roles?
How well do the platforms support users with limited technical backgrounds, such as clinicians or early-career researchers?
What usability improvements could enhance the adoption and effectiveness of each platform?
While results are still forthcoming, this poster will present our preliminary observations from early testing. By sharing our process, we aim to encourage similar usability-driven assessments in the broader research infrastructure community.
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