A dataset linking total funding amounts for deep-tech university-originated startups with researchers' publication metrics and KAKENHI grant records. The 5.5 KB XLS file, authored by Yoshifumi Mizuhara and last updated in April 2026, was used to build classification models distinguishing growing from non-growing startups. Feature-importance and breakdown-tree analyses were applied to interpret which researcher attributes drive startup growth.
Use Cases
- Classifying startup growth potential based on linked researcher publication and grant metrics
- Analyzing feature importance of researcher attributes for startup success
- Evaluating researcher profiles at top Japanese universities for entrepreneurial engagement
- Informing investment and policy decisions in Japan's deep-tech sector
Strengths
- Dataset links three distinct data sources: startup funding, researcher publications, and KAKENHI grant records.
- Includes results from classification models, feature-importance, and breakdown-tree analyses.
- Released under a permissive CC-BY-4.0 license.
Limitations
- Row count and column-level documentation are unknown, limiting suitability assessment.
- The dataset is very small at 5.5 KB, indicating limited scope or a summary-level view.
Provenance
- Source
- University-Originated Venture Database, researcher publication metrics, and KAKENHI grant records.
- Collection Method
- Data linkage and subsequent analysis using classification models, feature-importance, and breakdown-tree methods.
- Time Range
- null
- Freshness
- Last updated 2026-04-03 20:02:02
- Geography
- Japan, with a focus on top Japanese universities.