TEAM AND ROLE
I worked as the UX Designer & Researcher at Correlate AS, collaborating with the Chief Design Officer and UX Researcher. My responsibilities included conducting API research, testing technical integration capabilities, user research coordination, cross-functional collaboration, and end-to-end design execution. I leveraged my computer science background to personally test Zenodo's API and create comprehensive technical documentation that informed our design constraints.
TIME FRAME
8 weeks (MVP design and development)
OVERVIEW
Building a time-critical archival system for European research partners while maintaining regulatory compliance.
Zenodo Integration is a comprehensive archival solution that enables our consortium partners to seamlessly transfer their collaborative work from Correlate to Zenodo while meeting strict EU Horizon 2020 compliance requirements. Our solution eliminates the need for manual data recreation, reducing archival time from hours to minutes while maintaining full regulatory compliance and preserving organizational structure.
CONTEXT
European research consortiums faced strict compliance requirements without seamless integration tools.
European consortium partners (FeMaLe and Cities2030) were required by EU Horizon 2020 regulations to archive their research data in approved repositories like Zenodo. However, the lack of integration with their existing Correlate workflows meant researchers spent hours manually recreating their work in new platforms. This administrative burden created resistance to compliance and decreased productivity. Our challenge was to design a solution that would make compliance effortless while respecting technical constraints and user workflows.
RESEARCH
API Technical Investigation
With our development team fully occupied, I leveraged my computer science background to personally test Zenodo's API capabilities. I set up integration endpoints, documented mandatory fields, and identified technical limitations—creating comprehensive documentation that informed our design constraints.
User Research with Consortium Members
Our UX researcher conducted interviews with FeMaLe and Cities2030 project managers & research members to understand archival workflows, compliance requirements, and pain points. We identified opportunities to reduce administrative overhead while ensuring regulatory compliance.
Competitive Analysis & Systems Research
I researched existing archival integrations and Zenodo's interface patterns to understand user expectations. This revealed that most solutions required users to completely reconstruct their work in new platforms—highlighting our opportunity for seamless data transfer.
INSIGHT #1
Zenodo's API supports both draft and direct publication modes with different metadata requirements. This technical constraint needed careful balance with user workflow preferences to avoid overwhelming researchers. We needed to provide flexibility while maintaining simplicity in the interface design.
INSIGHT #2
Consortium members preferred bulk archival for project deliverables rather than individual files. They needed to archive entire project phases while maintaining organizational structure, which required us to design for both individual and batch operations efficiently.
INSIGHT #3
The biggest pain point was data recreation—researchers spent hours manually uploading files and re-entering metadata that already existed in Correlate. This duplication of effort created strong resistance to compliance requirements and reduced overall research productivity.
PROBLEM STATEMENT
"How might we enable European research consortium members to seamlessly archive their collaborative work from Correlate to Zenodo while meeting strict compliance requirements and minimizing workflow disruption?"
IDEATION & DESIGN
Addressing Correlates Technical Challenge
Understanding Zenodo's technical requirements and challenges was one thing, but the same went for Correlate. The biggest issue I ran into when I hit the design board was that Correlate doesn't have its own storage since we only integrate with other applications for our users. This meant I had to figure out a way to work around this technical limitation. My solution was to leverage our existing server infrastructure to temporarily download files from integrations like Google Drive or OneDrive (the files users want to archive), upload them to Zenodo via their API, then immediately delete them from our servers.
Simplified Integration Authentication
The integration itself was made seamlessly into Correlate where users can easily access the Zenodo integration via the existing integration tab and easily connect their Zenodo account, and even multiple accounts if they wanted. This ensures there is no external website or extra steps or configuration for the user to go through. A seamless experience that removes the typical authentication friction that often prevents users from adopting new integrations, keeping everything right within Correlate's familiar interface.
Collaborative Design Process
This project required a lot of reiterations from initial stages like wireframes to low fidelity prototypes to high fidelity prototypes. Especially during the high fidelity prototype design and iteration stage, I worked together with the Chief Design Officer to make improvements to the design. We were also in active communication with the project managers and researchers from the consortiums who were a huge help to us in making sure we hit all the targets they would need for an MVP. Multiple stakeholder review cycles ensured alignment between design vision, technical feasibility, and business requirements.
Flexible Archival Options
Based on user research, I created two distinct archival pathways: individual file archival for quick uploads and bulk project archival for comprehensive deliverable packages. Users can save drafts for later completion or publish immediately, accommodating different workflow preferences and compliance timelines while clearly communicating the permanence of published archives.
Smart Metadata Pre-population
The design leverages both already existing and ready to fetch data as well as using AI to help populate all the metadata fields required to publish or save a draft. This includes automatically pulling document titles, creation dates, author information, and file descriptions directly from Correlate's existing data. The AI assistance can suggest relevant keywords, research categories, and even help generate abstracts based on document content. This helps reduce completion time by approximately 62%.
RESULTS AND IMPACT
KEY LEARNINGS
TECHNICAL RESEARCH IS CRUCIAL
Conducting hands-on API research early prevented design decisions that would have been technically impossible to implement. This technical foundation informed every subsequent design choice and enabled more realistic and achievable solutions.
CONSTRAINT-DRIVEN DESIGN
Working within multiple constraint layers—regulatory, technical, and user—required creative problem-solving. The most elegant solutions emerged from embracing constraints rather than fighting them, leading to focused and purposeful design decisions.
COLLABORATION UNDER PRESSURE
Tight deadlines demand clear communication and defined ownership. Strategic division of responsibilities between myself and the Chief Design Officer accelerated delivery without compromising quality, demonstrating the power of effective teamwork.
FUTURE WORK
IN-PLATFORM DRAFT MANAGEMENT
Currently, users can publish or send drafts to Zenodo directly from Correlate. However, editing published drafts requires navigating to Zenodo's platform. Building comprehensive draft management functionality within Correlate would enable researchers to edit, update, and publish drafts without leaving their workflow, creating a truly seamless archival experience.
NATIVE STORAGE ARCHITECTURE
Implementing Correlate's own storage system would significantly simplify the technical architecture and improve performance. This would eliminate the dependency on temporary file downloads from external sources, reduce latency, and enable more sophisticated file handling capabilities while maintaining our commitment to data privacy and security.
AI-ENHANCED METADATA GENERATION
Improving our AI processes and prompts could deliver more accurate and comprehensive metadata auto-population. By leveraging advanced natural language processing and machine learning, we could extract richer contextual information from documents, suggest appropriate keywords, and even recommend relevant funding acknowledgments, further reducing the manual effort required for compliance.