AFOS-Analytics1/brazil-2026-electoral-divergence

AFOS — Brazil 2026 Electoral Divergence Dataset

🌐 English · Português · Español

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English

Open, auditable daily dataset that cross-references prediction markets (Polymarket) × polling institutes (TSE-registered) × press coverage for Brazil's 2026 presidential cycle, with explicit divergence between sources instead of smoothed averages.

Maintained by AFOS Analytics — open-source civic infrastructure for electoral political-risk intelligence. This is the public mirror of the same data the platform serves live, updated daily. Files are dated and append-only: each day adds new files, past dates are never overwritten, and every update is a git commit — so the full history is preserved natively.

🔒 No personal data (privacy / LGPD): contains only public electoral data (market odds, registered polls, news links). No subscriber data, no emails, no leads, no personal information of any kind. The export pipeline is database-free by construction and never accesses any user table. Brazil's LGPD and equivalent principles are respected in full.

License (dual): Data → CC BY 4.0 (LICENSE-CC-BY-4.0); code/scripts → Apache 2.0 (LICENSE-APACHE-2.0). Both require attribution to AFOS Analytics.

Cite: AFOS Analytics. Brazil 2026 Electoral Divergence Dataset. Hugging Face, 2026. CC BY 4.0.

Permanent archive: also deposited at the Harvard Dataverse — DOI 10.7910/DVN/2D0UK7.

Disclaimer: observational research. Not investment advice, not voting guidance. AFOS observes the markets — it does not trade them.

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Português

Dataset diário aberto e auditável que cruza mercados de previsão (Polymarket) × institutos de pesquisa (registrados no TSE) × cobertura de imprensa para o ciclo presidencial brasileiro de 2026, com divergência explícita entre as fontes em vez de médias suavizadas.

Mantido pela AFOS Analytics — infraestrutura cívica open-source de inteligência de risco político eleitoral. É o espelho público dos mesmos dados que a plataforma serve ao vivo, atualizado diariamente. Os arquivos são datados e append-only: cada dia adiciona novos arquivos, datas passadas nunca são sobrescritas, e cada atualização é um commit git — o histórico completo fica preservado nativamente.

🔒 Sem dados pessoais (privacidade / LGPD): contém apenas dados eleitorais públicos (odds de mercado, pesquisas registradas, links de notícia). Nenhum dado de assinante, nenhum email, nenhum lead, nenhuma informação pessoal. O pipeline de export é database-free por construção e nunca acessa qualquer tabela de usuário. A LGPD e princípios equivalentes são respeitados integralmente.

Licença (dual): Dados → CC BY 4.0 (LICENSE-CC-BY-4.0); código/scripts → Apache 2.0 (LICENSE-APACHE-2.0). Ambas exigem atribuição à AFOS Analytics.

Citação: AFOS Analytics. Brazil 2026 Electoral Divergence Dataset. Hugging Face, 2026. CC BY 4.0.

Arquivo permanente: também depositado no Harvard Dataverse — DOI 10.7910/DVN/2D0UK7.

Aviso: pesquisa observacional. Não é recomendação de investimento nem orientação de voto. A AFOS observa os mercados — não opera neles.

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Español

Dataset diario abierto y auditable que cruza mercados de predicción (Polymarket) × encuestadoras (registradas en el TSE) × cobertura de prensa para el ciclo presidencial brasileño de 2026, con divergencia explícita entre las fuentes en lugar de promedios suavizados.

Mantenido por AFOS Analytics — infraestructura cívica open-source de inteligencia de riesgo político electoral. Es el espejo público de los mismos datos que la plataforma sirve en vivo, actualizado diariamente. Los archivos son fechados y append-only: cada día agrega archivos nuevos, las fechas pasadas nunca se sobrescriben, y cada actualización es un commit git — el historial completo se preserva de forma nativa.

🔒 Sin datos personales (privacidad / LGPD): contiene solo datos electorales públicos (odds de mercado, encuestas registradas, enlaces de noticias). Ningún dato de suscriptor, ningún email, ningún lead, ninguna información personal. El pipeline de exportación es database-free por construcción y nunca accede a ninguna tabla de usuarios. La LGPD y principios equivalentes se respetan íntegramente.

Licencia (dual): Datos → CC BY 4.0 (LICENSE-CC-BY-4.0); código/scripts → Apache 2.0 (LICENSE-APACHE-2.0). Ambas requieren atribución a AFOS Analytics.

Citar: AFOS Analytics. Brazil 2026 Electoral Divergence Dataset. Hugging Face, 2026. CC BY 4.0.

Archivo permanente: también depositado en el Harvard Dataverse — DOI 10.7910/DVN/2D0UK7.

Aviso: investigación observacional. No es asesoría de inversión ni orientación de voto. AFOS observa los mercados — no opera en ellos.

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📁 Structure · Estrutura · Estructura

Full column-level definitions for every file are in DATA_DICTIONARY.md. Citation metadata in CITATION.cff; version history in CHANGELOG.md.

🗳️ Electoral polls (priority) · Pesquisas eleitorais · Encuestas

Path	Rows	Content
polls/tse-registry.csv · .json	399	Official TSE poll registry — full public fields, built directly from the TSE Open Data file. Every presidential poll filed for 2026 with its complete registration sheet: institute, CNPJ, sample, field dates, declared cost, named responsible statistician + CONRE, and the full (un-truncated) methodology and sampling/weighting design — including the demographic/geographic quota design (sex, age, education, income, region) with the declared quota percentages. Registration-design fields only — no per-candidate results, and the complete questionnaire is a PesqEle attachment, not in the open-data file. (Lei 9.504/97 art. 33)
polls/national-poll-results-firstround.csv	196	Published first-round results, long format: one row per candidate × scenario × poll. Carries the TSE registration number, institute, sample, margin, field dates.
polls/national-poll-results-secondround.csv	50	Published head-to-head runoff matchups (candidate1 vs candidate2, percentages).
polls/national-polls.json	32	Full structured national polls with results (first round + runoff + methodology), reconstructed from the platform history. Each poll carries a tse_registration block (full methodology, sampling/weighting design, statistician, CONRE, CNPJ, cost) and, since 2026-06-13, fieldwork-midpoint dating (field_midpoint, days_to_first_round/runoff) plus tse_registration.sample_design (parsed sample composition/weighting — layer A).
polls/sample-demographics.csv	159	Sample-design demographics (layer A), long format: each poll's declared sex/age/education/income quota composition parsed from the TSE sampling plan, with explicit per-poll coverage (full_percentages 15/32 · mentioned_no_pct 17/32). This is sample composition/weighting — not vote-by-demographic crosstabs (layer B), which are not part of TSE open data.
polls/polls-data-{date}.json	—	Daily snapshot of the national polls referenced on that date.

📈 Market & divergence time-series

Path	Content
data/market-odds-timeseries.csv	Polymarket presidential odds per candidate, daily (date, candidate, party, polymarket_pct, volume_usd_m) — full history from 2026-04-04.
data/divergence-timeseries.csv	Market × poll divergence per candidate (poll_date, institute, register_tse, candidate, poll_pct, polymarket_pct, polymarket_date, divergence_pp) — each national poll joined to the market odds on its date. The dataset's namesake signal.
data/poll-divergence.csv	Poll-level market × poll pairing anchored on each poll's fieldwork midpoint; naive_gap_pp is explicitly flagged naive_winprob_minus_voteshare — the market prices P(win) while the poll reports vote share, so the gap is not scale-reconciled (reconciling the scales is a modeling choice left to the researcher).
data/divergence-{date}.csv	Per-day market × poll divergence snapshot.

📰 Daily analysis & news

Path	Content
snapshots/analysis-criteriosa/{date}.json	Daily analysis: market × poll × press, per candidate (incl. quadroComparativo).
snapshots/analysis-cards/{date}.json	Thematic cards (sentiment, institutional, macro).
news/news-{date}.json	Public news links (source, title, URL, date) — no article bodies.

🎓 For researchers

• Start with DATA_DICTIONARY.md (every column, type, unit, provenance) and polls/ (the registered-poll universe + published results).
• Reproducibility: every value traces to a public primary source — the TSE registry, a named pollster's release, or a live Polymarket contract. Nothing is imputed or smoothed; where a number is missing it is left blank, not filled.
• Editorial stance: AFOS reports divergence between sources rather than a single blended average — the spread is treated as signal, not noise.
• Demographics: sample-design demographics — the declared composition/weighting of each poll's sample (layer A) — are included (polls/sample-demographics.csv). Vote-by-demographic crosstabs (layer B — e.g. vote share by sex/age/income) are not part of Brazil's TSE open data; institutes publish those separately, so they are intentionally absent here rather than partially scraped.
• Scale caveat (market vs poll): Polymarket prices probability of winning; polls report vote share. The two divergence files keep both raw values side by side and flag the naive gap accordingly — they are not a like-for-like error metric.
• Updates: dated and append-only; each daily commit preserves the full history natively (see CHANGELOG.md). The file for the current date may be regenerated during the day; files for dates already closed are never modified.
• Known errors: see ERRATA.md. When a defect is found in a past date, the file is not rewritten — the correction is published in the errata instead, so the record stays as distributed and the defect stays discoverable. Erros conhecidos em ERRATA.md. · Errores conocidos en ERRATA.md.

Sources / Fontes / Fuentes: Polymarket (live USD markets) · TSE-registered institutes · 400+ press outlets. Method & source code (Apache 2.0): github.com/AFOS-Analytics.