<p>Predictor variables.</p>

Predictor variables linking total funding amounts for deep-tech university-originated startups to researchers' publication metrics and KAKENHI grant records. The dataset was created by Yoshifumi Mizuhara and last updated in April 2026. It is a 13.5 KB XLS file, suggesting a small-scale, tabular dataset.

Use Cases

• Classify growing versus non-growing startups based on linked researcher metrics.
• Analyze feature importance of researcher attributes for startup growth prediction.
• Evaluate researcher profiles at Japanese universities for entrepreneurial engagement.
• Guide investment and policy decisions in Japan's deep-tech ecosystem using the defined identification framework.

Strengths

• Dataset is small at 13.5 KB, facilitating quick download and initial exploration.
• Data links multiple sources: startup funding, researcher publications, and KAKENHI grant records.
• Released under a permissive CC-BY-4.0 license.
• Last update timestamp is explicitly provided (2026-04-03 20:02:01).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to its focus on Japan and Japanese universities.

Provenance

Source

figshare, author Yoshifumi Mizuhara.

Collection Method

Data was likely constructed using the University-Originated Venture Database to link funding with researcher metrics.

Time Range

null

Freshness

Last updated 2026-04-03 20:02:01; freshness should be verified.

Geography

Japan, with a focus on top Japanese universities.