Observed Signal · Jul 14, 2026 · Product Launch · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
indian-fakedata: Contextual Indian Mock Data Library
A developer published 'indian-fakedata', an open-source library (Node.js and Python) that generates realistic, demographically coherent synthetic Indian profiles. The tool offers a CLI, programmatic APIs, demographic filters (religion, state, caste, age, etc.), multiple output formats, and progressive enrichment layers (outcomes, narrative, persona) with a controllable bias dial. Data generation is calibrated to public sources including Census of India 2011, NFHS-5, MSME Census, UIDAI/RTO records, and CSDS/Lokniti studies. Packages are available as @abhay557/indian-fakedata (npm) and indian-fakedata (pip), requiring Node.js >=18 or Python 3.8+.
A practical developer release that improves quality of synthetic demographic data for testing, model training, and fairness audits, but it is not a platform-level or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- 'indian-fakedata' is published for Node.js as @abhay557/indian-fakedata and on PyPI as indian-fakedata.
- The library provides a CLI ('indian-fakedata') and programmatic APIs for Node.js/TypeScript and Python to generate synthetic Indian demographic profiles.
- It supports demographic constraints (e.g., religion, state, caste, socialCategory, areaType, min/max age, education, occupation, maritalStatus) and output formats json/jsonl/csv.
- Offers progressive enrichment layers (outcomes, narrative, persona) and a 'bias' dial (0.0–1.0) to simulate historical discrimination in outcome simulation.
- Generation is calibrated against public data sources: Census of India 2011, NFHS-5, MSME Census, UIDAI & RTO records, and CSDS/Lokniti election studies.
Ontology Mapping & Concepts
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