Model Performance Metrics for Japanese Deep-Tech Startup Growth Classification

5.5 KB of model performance metrics from a study linking researcher publication and grant metrics to startup funding in Japan. The dataset, created by Yoshifumi Mizuhara and last updated in April 2026, likely contains results from classification models distinguishing growing and non-growing university-originated startups. The analysis underpins a framework for evaluating researcher entrepreneurial engagement.

Use Cases

• Benchmarking classification model performance based on the reported metrics for Cases 1 to 4
• Analyzing the relationship between researcher attributes and startup growth based on the described feature-importance analysis
• Evaluating the disconnect between research quality and startup activity based on the study's findings
• Informing investment or policy decisions for deep-tech ventures based on the identification framework

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Released under a permissive CC-BY-4.0 license.
• Last update date (2026-04-03) is explicitly provided.

Limitations

• Row count and column-level documentation are unknown; field semantics must be inferred after download.
• The 5.5 KB size indicates a very limited scope, likely containing summary metrics rather than raw data.

Provenance

Source

figshare, authored by Yoshifumi Mizuhara.

Collection Method

Data likely derived from the University-Originated Venture Database, linked with researcher publication metrics and KAKENHI grant records.

Time Range

null

Freshness

Last updated 2026-04-03 20:02:03; freshness should be verified.

Geography

Japan, with a focus on top Japanese universities.