Japanese deep-tech startup data links total funding amounts with researcher publication metrics and KAKENHI grant records. The dataset, created by Yoshifumi Mizuhara and last updated in April 2026, was used to build classification models distinguishing growing from non-growing startups. It underpins an identification framework to guide investment and policy decisions in Japan's deep-tech ecosystem.
Use Cases
- Classifying startup growth potential based on linked researcher publication and grant metrics.
- Analyzing feature importance to interpret which researcher attributes drive startup growth.
- Evaluating researcher profiles at top Japanese universities for entrepreneurial engagement.
- Identifying disconnects between high-quality research achievements and actual startup activity.
Strengths
- Data links startup funding with researcher publication metrics and KAKENHI grant records.
- Dataset is openly licensed under CC-BY-4.0.
- Includes classification models and feature-importance analysis for interpretation.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Dataset is very small at 5.5 KB, indicating limited scope.
Provenance
- Source
- University-Originated Venture Database.
- Collection Method
- Data linking total funding with researcher publication metrics and KAKENHI grant records.
- Time Range
- null
- Freshness
- Last updated 2026-04-03 20:02:00; freshness should be verified.
- Geography
- Japan, specifically top Japanese universities.