The ResearchOps Toolbox + Graph: Discovery for UX Research and Design Teams
summary
The ReOps Tools Census gathers data on ~461 tools from ~400+ survey responses and the team loads Airtable into Neo4j via Python, creates a graph projection, runs Graph Data Science kNN to write similarity scores and produces a user‑based recommendation engine that ranks tools for a given team profile. The pipeline uses Neo4j + Python, Airtable, AuraDB, Neo Dash and Bloom to visualize results and it returns concrete ranked tool lists and example starter stacks that demonstrably work for targeted demographics.
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chapters (8)
- 0:00Introduction and presenters
- 3:00ResearchOps Community and Toolbox
- 8:27Recommendation systems for tools
- 15:04Data pipeline and graph setup
- 19:38Importing Airtable data
- 23:29Building the recommender system
- 27:41Visualizing recommendations
- 30:53Next steps and Q&A
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