Research Atlas: Designing a visual search tool for interdisciplinary research

Context

A researcher entering an unfamiliar field often starts with only a rough question. Conventional search immediately asks for keywords, but the useful keywords, authors and concepts are often exactly what the researcher is trying to discover.

I built Research Atlas to start from that rough question, retrieve relevant papers, and show the topics and relationships between them. The point is to help a researcher learn what to search for next, not just rank a list of papers.

Problems

• Search assumes you already know the language of the field. Effective queries depend on terms users may still be trying to discover.
• A ranked list hides the shape of the literature. Long result lists make it hard to see clusters, relationships or how papers connect.
• Useful work can sit outside the discipline where the search starts. Relevant papers may use different terminology or belong to another field.
• The reason a result appears is hard to see. Conventional ranking gives users little visibility into the relationships behind a result.
• Understanding builds across sessions. Researchers need to keep useful papers, topics and maps rather than restart each time.

Constraints

The product also has three hard limits:

• It was a solo capstone. The scope had to fit research, product design, engineering and evaluation into one project.
• Research Atlas depends on OpenAlex. It can organise and relate the works and topics OpenAlex exposes, but cannot control the source's coverage.
• The graph has to stay readable. Adding works can reveal useful connections and add noise at the same time, so broad maps are capped at about 200 works and Analysis at about 100 per topic.

Working hypothesis

If the tool turns a rough question into a visible map of papers and topics, reveals relationships progressively, and saves useful connections, users should find unfamiliar fields easier to navigate and discover work they would not have thought to search for.

Decisions and delivery

I tested the relevance of OpenAlex search results using 30 queries across different subject areas. Those checks helped me decide how much material the maps could show before useful connections were lost in noise: about 200 works for broad exploration and 100 per topic in the more focused Analysis flow.

I built the application with Next.js. Neo4j stores the network of papers and topics; Supabase handles accounts and saved content; D3 draws the maps; and Vercel hosts the application.

Reading the product

The product has three workflows:

Evaluation

I evaluated the tool with an academically oriented group and recorded usage and performance over a 30-day period.

• Structured feedback repeatedly identified unexpected discovery as the strongest source of value.
• The application recorded 115 visits.
• Vercel Speed Insights reported a 96/100 Real Experience Score.

Users described finding connected research areas and papers they would not have thought to explore.

The evaluation also highlighted areas to improve: map readability, relevance controls, source coverage, exports and the explanation of the tool's exploratory purpose.

I did not test willingness to pay or institutional demand, so commercial viability remains an open question.