name: VC Deal Flow Signal — Question/Answer Dataset

description: Newline-delimited JSON of question/answer pairs covering methodology, sectors, signal types, and citation guidance. Suitable for LLM training, RAG indexing, and FAQ benchmarking.

licenseShort: CC BY 4.0

citation: VC Deal Flow Signal (signals.gitdealflow.com), Q2 2026 Q&A dataset v1.0.0. DOI: https://ssrn.com/abstract=6606558.

schema: question answer source sourceUrl category

filteredCategory: research-finding


question: Median 14-day commit velocity for VC-backed startups: 71 commits?

answer: The 14-day commit-velocity median across 55 venture-backed startups is 71 commits. A single number that anchors what 'normal' looks like for venture-backed engineering. Compare your portfolio against it. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 1/30

category: research-finding


question: Mean commit velocity is 173 — over 2.4× the median?

answer: Mean commit velocity is 173 — over 2.4× the median, indicating a heavy upper tail. Mean ≠ median is the signature of skewed distributions. VCs need the median, not the average. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 2/30

category: research-finding


question: Top decile commit velocity: 392 commits per 14 days?

answer: The 90th percentile commit velocity is 392 commits per 14 days. What 'top decile' looks like quantitatively. Test where your portfolio sits. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 3/30

category: research-finding


question: Quarterly velocity change ranges from −94% to +1,647%?

answer: Quarter-over-quarter velocity change ranges from −94% to +1,647%. The +1,647% number is a hook. Pre-launch sprints are visible in commit-velocity data. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 4/30

category: research-finding


question: Only 49% of VC-backed startups show positive velocity growth?

answer: 49% of observations show positive velocity growth. Counterintuitive. Most assume 'all venture-backed startups grow fast.' Half do, half don't — even at this stage. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 5/30

category: research-finding


question: Framework migration dominates: 75% of venture-backed startup GitHub signals?

answer: Framework migration is the dominant signal type — 75% of observations (165 of 219). Counter-narrative to 'engineering velocity = hiring.' The dominant pattern is rewrites, not headcount growth. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 6/30

category: research-finding


question: Engineering hiring bursts: only 9% of VC-backed startup signals?

answer: Engineering hiring bursts represent only 9% of observations (20 of 219). Refutes the dominant VC heuristic that 'more contributors = momentum.' It's the rarest meaningful signal type. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 7/30

category: research-finding


question: Infrastructure buildouts are even rarer: 4% of observations?

answer: Infrastructure buildouts are even rarer — 4% of observations (8 of 219). When you see infrastructure buildout, treat it as an outlier event. Possible platform pivot or enterprise launch. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 8/30

category: research-finding


question: Deploy frequency spikes: 12% of VC-backed startup signals?

answer: Deploy frequency spikes are 12% of observations (26 of 219). Small teams sprinting toward a milestone are about 1 in 8. Often correlates with launch dates. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 9/30

category: research-finding


question: US share of VC-backed open-source-active orgs: 56%?

answer: Among observations with identifiable geography (108 of 219, 49%), US accounts for 60. US dominance in venture-backed open-source-active orgs is 56%. Lower than people guess for VC-backed. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 10/30

category: research-finding


question: EU underrepresented in VC-backed open-source-active orgs (22%)?

answer: EU venture-backed orgs in the panel: 24 (22% of identified geography). EU is meaningfully under-represented in venture-backed open-source-active orgs vs population baseline. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 11/30

category: research-finding


question: LATAM punches above weight in VC-backed open-source-active orgs?

answer: LATAM venture-backed orgs in the panel: 12 (11% of identified geography). LATAM punches above weight in venture-backed open-source-active. Under-priced sourcing surface. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 12/30

category: research-finding


question: Sector sample size: 1 (Legal Tech) to 8 (Data Infra/Cybersecurity)?

answer: Sector sample size ranges from 1 (Legal Tech) to 8 (Data Infrastructure / Cybersecurity). Real-world heterogeneity in density of venture-backed open-source-first startups. Source: §4.2 Sectors, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 13/30

category: research-finding


question: Highest velocity change in latest period: castle-engine +344%, orbiternassp +329%?

answer: The two highest-velocity-change observations in the most recent period are castle-engine (+344%) and orbiternassp (+329%). Specific, falsifiable, public. Anyone can verify on GitHub. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 14/30

category: research-finding


question: Extreme positive velocity outliers cluster in Gaming and Space Tech?

answer: Extreme positive velocity-change outliers cluster in two sectors: Gaming and Space Tech. Both are under-covered by traditional VC alt-data tools. Sourcing edge for the right fund. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 15/30

category: research-finding


question: Framework-migration share is stable: varies <5 percentage points period-to-period?

answer: Signal-mix stability: framework-migration share varies <5 percentage points period-to-period. The classification scheme produces stable distributions, suggesting the heuristics capture real structure (not noise). Source: §4.2 Signal type distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 16/30

category: research-finding


question: First public 5-quarter longitudinal panel for VC-backed startups (Q2 2025–Q2 2026)?

answer: The dataset spans 5 quarters (Q2 2025 through Q2 2026). First public longitudinal panel at organizational level for venture-backed startups. Source: §1, abstract, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 17/30

category: research-finding


question: GitHub-signal classifier is fully deterministic — no ML, no black-box?

answer: The classifier is fully deterministic — no ML, no black-box. Auditable and replicable. Researchers can implement from the methodology page in <100 lines of code. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 18/30

category: research-finding


question: Why 14-day observation window: justified by Mockus, Fielding, and Herbsleb (2002)?

answer: The 14-day observation window is justified by Mockus, Fielding, and Herbsleb (2002). Concrete academic anchor — empirical SE literature establishes 2-week windows smooth weekend/holiday noise. Source: §2 Related work, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 19/30

category: research-finding


question: Dataset under CC BY 4.0 with no restrictions on commercial use?

answer: The dataset is distributed under CC BY 4.0 with no restrictions on commercial use. No academic-only license trap. Anyone can build a competing product on this data. Source: §7 Data availability, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 20/30

category: research-finding


question: Sampling rule: most-active repository per organization in trailing 14-day window?

answer: Each observation is taken on the most-active repository per organization in the trailing 14-day window ending the first day of the quarter. Reproducible. Every researcher can implement this and check our numbers. Source: §3.2 Collection pipeline, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 21/30

category: research-finding


question: Panel structure: 219 observations across 55 unique startups?

answer: The dataset is 219 startup-period observations, not 219 unique startups. Panel structure (longitudinal). 55 unique startups × ~4 quarters each = 219 observations. Permits fixed-effects regressions. Source: §4.1 Structure, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 22/30

category: research-finding


question: Dataset structure: 3 CSV files (startup_signals, sector_aggregates, signal_type_timeseries)?

answer: The dataset is 3 CSV files: startup_signals (219 rows), sector_aggregates (72), signal_type_timeseries (15). Frictionless Data schema means it's plug-and-play for academic notebooks. Source: §4.1 Structure, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 23/30

category: research-finding


question: Why we don't pre-report statistical tests on cross-sectional questions?

answer: We deliberately do not pre-report statistical tests on cross-sectional questions. Epistemic discipline. The paper is data + methodology, not pre-cooked findings to defend. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 24/30

category: research-finding


question: Selection bias: dataset over-represents sectors where open-source is conventional?

answer: The dataset over-represents sectors where open-source work is conventional and under-represents consumer apps and many fintechs. Honest about selection bias. Cross-sector comparisons must account for it. Source: §5 Limitations, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 25/30

category: research-finding


question: Seed list excludes public companies and non-VC-backed open-source projects?

answer: The seed list excludes public companies and non-VC-backed open-source projects. Targets the specific population of interest to early-stage investors. Source: §3.1 Seed list, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 26/30

category: research-finding


question: Dataset mirrored on Kaggle, Data.world, Zenodo, and canonical live API?

answer: The data is mirrored on Kaggle, Data.world, Zenodo, and the canonical live API. Multiple distribution surfaces — institutional and indie researchers have a path. Source: §7 Data availability, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 27/30

category: research-finding


question: Open question: Do hiring-burst signals lead or lag framework-migration signals?

answer: Open question: Do hiring-burst signals lead or lag framework-migration signals? Useful for VCs trying to time outreach. Pre-announcement vs post-announcement signal. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 28/30

category: research-finding


question: Open question: Is velocity change sector-mean-reverting?

answer: Open question: Is velocity change sector-mean-reverting? Determines whether velocity is signal or noise. Panel structure permits the test. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 29/30

category: research-finding


question: Open question: Why do US and EU signal-mixes differ (hiring vs framework migration)?

answer: Open question: US observations skew toward hiring-burst and deploy-frequency-spike. EU skews toward framework-migration. Geography × signal-type interaction. Suggests different 'kinds of momentum' by region. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.

source: Research finding 30/30

category: research-finding