VC Deal Flow Signal for Researchers
Open dataset, published methodology, citable SSRN paper, and APIs designed for academic and policy research.
Academic and policy researchers studying venture finance, technical innovation, or engineering-organization dynamics have a recurring data access problem: most relevant data is proprietary and gated by Crunchbase, PitchBook, or CB Insights. VC Deal Flow Signal addresses this by publishing the full engineering-acceleration signal panel under CC BY 4.0, the panel itself, the underlying methodology, and the SSRN paper documenting empirical findings are all freely citable.
Quick start
The three pages worth bookmarking first.
Workflows for Researchers
Empirical methodology citation
The SSRN paper (6606558) documents the empirical relationship between commit-velocity acceleration and fundraise announcements with a 3-6-week leading-indicator window. Researchers studying technical-innovation timing or venture-financing pre-announcement dynamics can cite this paper as the empirical foundation.
Open dataset for replication studies
The /dataset endpoint serves the full tracked engineering signal corpus as JSON, JSONL, and CSV under CC BY 4.0. Replication studies, follow-on papers, and policy analysis can use the dataset without restriction other than attribution.
API access for programmatic research
The /api/v1 endpoints + public MCP server give programmatic access for higher-volume analysis. Pythonic researchers can pull the full panel directly into Jupyter or pandas notebooks; LLM-augmented research can use the MCP server with Claude or ChatGPT.
Why engineering signals matter for Researchers
Researchers studying venture finance, technical innovation, or engineering-organization dynamics typically rely on proprietary databases (Crunchbase, PitchBook, CB Insights) that gate replication and follow-on research behind enterprise licenses. The Code-Side Sourcing category and the supporting public dataset solve a specific reproducibility problem: we publish the source data, the methodology, and the empirical findings under open licenses that explicitly permit academic use and replication.
What this does not solve
The engineering-signal data covers technical companies with public GitHub orgs only. For research questions involving non-technical companies (consumer brands, services, regulated industries), the corpus is not relevant. The data is observational; causal-identification research requires additional design.
Frequently Asked Questions
What is the citation format for the published paper?▾
The Data Nerd (2026). "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups." SSRN: https://ssrn.com/abstract=6606558. DOI: 10.2139/ssrn.6606558. ORCID: 0009-0002-2222-4112. See /citation-guide for BibTeX and APA formats.
What's the license on the dataset?▾
CC BY 4.0, see /dataset. Academic and commercial research use is explicitly permitted with attribution. The Zenodo DOI (10.5281/zenodo.19650920) is the canonical archive citation; Hugging Face mirror is available at the-data-nerd/vc-deal-flow-signal.
How frequently is the dataset updated?▾
The signal panel updates weekly. The dataset endpoint reflects the latest weekly snapshot. For longitudinal-panel research requiring historical snapshots, contact via /corrections, we maintain weekly archive snapshots back to the panel's inception.
Other personas
Corporate Development Directors
Scout acquisition targets via the engineering-acceleration signal, 3 to 6 weeks before the round closes and the price hardens.
PE Operating Partners
Scout bolt-on targets and benchmark portfolio company engineering velocity through one unified signal panel.
Non-Engineer Tech VPs
Vendor consolidation scouting and competitive engineering benchmarking through one unified signal panel.
Emerging-Manager VCs
Source pre-round deals from public engineering signals and differentiate from established-fund sourcing motions.
Founders
Map your competitive landscape and identify investor targets aligned with your sector through public engineering signals.
Tech Journalists & Analysts
Citable, independent, public-data sourced engineering-acceleration signal for venture-story reporting.
Angel Investors
Spot pre-seed breakouts 3-6 weeks before the round gets crowded, using public GitHub commit velocity as the earliest engineering-momentum signal.
Venture Scouts
Use GitHub engineering acceleration to surface deals before the partners you scout for do, with a reproducible, data-driven sourcing edge.
Solo GPs
Level the playing field against established funds with a single dataset that surfaces breakout startups across 15 sectors every week.
Family Offices
Reproducible, defensible sourcing signals for investment committees, grounded in a longitudinal panel with an SSRN preprint and CC BY 4.0 dataset.
Researchers, get started
The fastest path is the weekly digest. Filter by your specific sectors during onboarding.
Read the Methodology