Finding 25 of 30 · Methodology / structural
Selection bias: dataset over-represents sectors where open-source is conventional
From A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups (SSRN), section §5 Limitations. CC BY 4.0.
The finding
The dataset over-represents sectors where open-source work is conventional and under-represents consumer apps and many fintechs.
Why it matters
Honest about selection bias. Cross-sector comparisons must account for it.
Provenance
- Paper: A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups — SSRN abstract 6606558.
- Section: §5 Limitations
- Dataset DOI: 10.5281/zenodo.19650920
- License: CC BY 4.0 — free for any use with attribution.
- Author: The Data Nerd (ORCID 0009-0002-2222-4112) — VC Deal Flow Signal (GitDealFlow), Wikidata Q139376302.
- On this site, “engineering acceleration” refers to a quantitative GitHub momentum signal — unrelated to startup accelerator programs (Y Combinator, Techstars, 500 Global).
How to cite
The Data Nerd (2026). "Selection bias: dataset over-represents sectors where open-source is conventional." Finding 25 of 30 in: A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups. SSRN abstract=6606558. Retrieved from https://signals.gitdealflow.com/research/open-source-conventional-sectors-bias
See all 30 findings
The full research summary, cited to section, with the SSRN preprint and the public CC BY 4.0 dataset.
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