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).
References
Peer-reviewed prior work this finding builds on. Each citation resolves to a DOI in a top-tier venue and appears in the page’s structured data as a ScholarlyArticle citation edge. Download all references as BibTeX.
- Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, Daniela Damian (2014). The Promises and Perils of Mining GitHub. Proceedings of the 11th Working Conference on Mining Software Repositories (MSR '14), MSR '14, pp. 92–101. DOI: 10.1145/2597073.2597074
- Josh Lerner, Ramana Nanda (2020). Venture Capital's Role in Financing Innovation. Journal of Economic Perspectives, JEP 34(3): 237–261. DOI: 10.1257/jep.34.3.237
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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