Is the startup signals dataset really free?

Yes. The full dataset of 350+ trending startups is free to download as CSV or JSON. No signup, no email, no API key. Licensed under Creative Commons BY 4.0, just cite GitDealFlow as the source.

Can I use this dataset for academic research?

Yes. The dataset is licensed CC BY 4.0 and is suitable for academic research on startup ecosystems, engineering signals as funding predictors, and venture capital deal flow analysis. Cite as: VC Deal Flow Signal (signals.gitdealflow.com), Q2 2026 data. An SSRN preprint is available at papers.ssrn.com/sol3/papers.cfm?abstract_id=6606558.

What fields are included in the CSV?

Each row includes: startup name, sector, commit velocity (14-day), commit velocity change (%), contributor count, contributor growth rate, new repositories (30-day), signal type (breakout, acceleration, steady, cooling), and detection date.

Free Startup GitHub Signals Dataset

Name: GitDealFlow Startup GitHub Signals Dataset
Creator: GitDealFlow
License: https://creativecommons.org/licenses/by/4.0/

350+ trending startups ranked by engineering velocity. Download as CSV or JSON. Updated weekly. CC BY 4.0.

Direct answer: The dataset is the weekly engineering-velocity panel: three measured behaviors across 350+ venture-backed GitHub organizations in 15 sectors, refreshed weekly, free to download and cite under CC BY 4.0.

TL;DR: The GitDealFlow signals dataset tracks 350+ startups across 15 sectors using public GitHub engineering data. Every Monday, the scan refreshes with commit velocity, contributor growth, and new repository creation for each tracked startup. The full dataset is free to download, no signup required. Licensed Creative Commons BY 4.0.

Download the dataset

Two formats, same data. No email, no API key, no credit card.

Download CSV: Spreadsheet-ready. Opens in Excel, Google Sheets, Numbers, pandas, R.
Download JSON: Developer-ready. Structured data for apps, dashboards, MCP servers.
OpenAPI spec: Full API documentation for programmatic access.

What is in the dataset

The dataset contains one row per tracked startup, scored on three dimensions of public GitHub activity:

Field	Description	Example
name	Startup name	Supabase
sector	One of 15 sectors	developer-tools
commit_velocity_14d	Total commits to main repo, rolling 14 days	342
velocity_change_pct	Percent change vs prior 14-day window	+47.3%
contributors_active	Distinct contributors, 6-week window	28
contributor_growth_rate	6-week growth rate	+12.5%
new_repos_30d	New public repos created in last 30 days	3
signal_type	Classification: breakout, acceleration, steady, cooling	breakout
detected_date	When the signal first appeared	2026-07-14

How the dataset is built

Every Sunday at approximately 09:00 UTC, the scan collects public GitHub activity metrics across 350+ startup organizations. The methodology uses three signals: commit velocity (14-day rolling), contributor growth (6-week window), and new repository creation (30-day window). The full methodology is documented at signals.gitdealflow.com/methodology.

An SSRN preprint analyzing the predictive power of GitHub engineering signals for startup funding is available: abstract_id=6606558.

Citation

If you use this dataset in research, analysis, or a product, cite it as:

VC Deal Flow Signal (signals.gitdealflow.com), Q2 2026 data.
https://gitdealflow.com/dataset

License

Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share, adapt, and build upon this data for any purpose, including commercial, provided you cite GitDealFlow as the source.

Programmatic access

Beyond the static downloads, the data is available through three free interfaces:

MCP server: npx -y @gitdealflow/mcp-signal (for Claude, Cursor, any MCP host)
REST API: GET https://signals.gitdealflow.com/api/signals.json
CSV export: GET https://signals.gitdealflow.com/api/signals.csv

Why This Page Exists

GitDealFlow is a public deal flow signal dataset: 350+ startup GitHub organizations across 15 sectors, refreshed weekly, with breakout teams surfacing 21 to 47 days before their round is announced. This page makes one part of that system legible: what it measures, how it is computed, and how to use it in a live sourcing workflow. The method is published end to end and falsifiable by design, with the working paper on SSRN and the dataset downloadable under CC BY 4.0.

Start Here

A practical read-through of Free Startup GitHub Signals Dataset: the dataset behind this page refreshes weekly across 350+ organizations and 15 sectors, and every figure shown traces to a public GitHub REST API pull. That matters for two reasons. Reproducibility: any number here can be re-derived from primary sources, which is the standard the published methodology sets for itself. Timeliness: engineering acceleration precedes announcements, so this page follows the data cadence rather than the news cycle, and the freshness endpoint always reports the exact pull date.

What a download buys you, methodologically

The panel's value is not the snapshot but the cadence: weekly pulls of commit velocity (14-day windows, percentage change, two-period confirmation), contributor concentration (Gini coefficient), and repository expansion, across 350+ organizations in 15 sectors. Time-series behavior, not point-in-time counts, is what separates a team accelerating toward a round from a team that once shipped, and the measured lead between breakout flags and announcements is 21 to 47 days.

Everything is reproducible by design: the methodology is published, the data carries a CC BY 4.0 license, and live JSON and CSV endpoints plus an OpenAPI spec let you pipe the panel into any analysis stack without scraping. Cite the dataset and the pull date; the freshness endpoint always reports the exact one.

Download CSV now Download JSON