Datasets:

AFOS-Analytics1
/

brazil-2026-electoral-divergence

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
description: string
generated_from: string
count: int64
polls: list<item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: st (... 573 chars omitted)
  child 0, item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: string, relia (... 561 chars omitted)
      child 0, institute: string
      child 1, date: timestamp[s]
      child 2, sample: int64
      child 3, margin: double
      child 4, register: string
      child 5, reliability: int64
      child 6, method: string
      child 7, scenarios: list<item: struct<name: string, results: list<item: struct<candidate: string, percent: double>>>>
          child 0, item: struct<name: string, results: list<item: struct<candidate: string, percent: double>>>
              child 0, name: string
              child 1, results: list<item: struct<candidate: string, percent: double>>
                  child 0, item: struct<candidate: string, percent: double>
                      child 0, candidate: string
                      child 1, percent: double
      child 8, secondRound: list<item: struct<matchup: string, candidate1: string, percent1: double, candidate2: string, percent (... 11 chars omitted)
          child 0, item: struct<matchup: string, candidate1: string, percent1: double, candidate2: string, percent2: double>
              child 0, matchup: string
              child 1, candidate1: string
              child 2, percent1: double
              child 3, candidate2: string
              child 4, percent2: double
      child 9, tse_registration: struct<register_tse: string, cnpj: string, institute_full: string, statistician: string, conre: stri (... 125 chars omitted)
          child 0, register_tse: string
          child 1, cnpj: string
          child 2, institute_full: string
          child 3, statistician: string
          child 4, conre: string
          child 5, cost_brl: double
          child 6, own_poll: bool
          child 7, methodology: string
          child 8, sampling_plan: string
          child 9, control_system: string
          child 10, matched_by: string
      child 10, fieldDates: string
      child 11, note: string
      child 12, source: string
enriched_at_note: string
date: timestamp[s]
items: list<item: struct<source: string, title: string, url: string, published: string>>
  child 0, item: struct<source: string, title: string, url: string, published: string>
      child 0, source: string
      child 1, title: string
      child 2, url: string
      child 3, published: string
to
{'date': Value('timestamp[s]'), 'count': Value('int64'), 'items': List({'source': Value('string'), 'title': Value('string'), 'url': Value('string'), 'published': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              description: string
              generated_from: string
              count: int64
              polls: list<item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: st (... 573 chars omitted)
                child 0, item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: string, relia (... 561 chars omitted)
                    child 0, institute: string
                    child 1, date: timestamp[s]
                    child 2, sample: int64
                    child 3, margin: double
                    child 4, register: string
                    child 5, reliability: int64
                    child 6, method: string
                    child 7, scenarios: list<item: struct<name: string, results: list<item: struct<candidate: string, percent: double>>>>
                        child 0, item: struct<name: string, results: list<item: struct<candidate: string, percent: double>>>
                            child 0, name: string
                            child 1, results: list<item: struct<candidate: string, percent: double>>
                                child 0, item: struct<candidate: string, percent: double>
                                    child 0, candidate: string
                                    child 1, percent: double
                    child 8, secondRound: list<item: struct<matchup: string, candidate1: string, percent1: double, candidate2: string, percent (... 11 chars omitted)
                        child 0, item: struct<matchup: string, candidate1: string, percent1: double, candidate2: string, percent2: double>
                            child 0, matchup: string
                            child 1, candidate1: string
                            child 2, percent1: double
                            child 3, candidate2: string
                            child 4, percent2: double
                    child 9, tse_registration: struct<register_tse: string, cnpj: string, institute_full: string, statistician: string, conre: stri (... 125 chars omitted)
                        child 0, register_tse: string
                        child 1, cnpj: string
                        child 2, institute_full: string
                        child 3, statistician: string
                        child 4, conre: string
                        child 5, cost_brl: double
                        child 6, own_poll: bool
                        child 7, methodology: string
                        child 8, sampling_plan: string
                        child 9, control_system: string
                        child 10, matched_by: string
                    child 10, fieldDates: string
                    child 11, note: string
                    child 12, source: string
              enriched_at_note: string
              date: timestamp[s]
              items: list<item: struct<source: string, title: string, url: string, published: string>>
                child 0, item: struct<source: string, title: string, url: string, published: string>
                    child 0, source: string
                    child 1, title: string
                    child 2, url: string
                    child 3, published: string
              to
              {'date': Value('timestamp[s]'), 'count': Value('int64'), 'items': List({'source': Value('string'), 'title': Value('string'), 'url': Value('string'), 'published': Value('string')})}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

AFOS — Brazil 2026 Electoral Divergence Dataset

🌐 English · Português · Español

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.

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

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.

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

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.

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

📁 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`	350	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`	158	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`	38	Published head-to-head runoff matchups (`candidate1 vs candidate2`, percentages).
`polls/national-polls.json`	22	Full structured national polls with results (first round + runoff + methodology), reconstructed from the platform history. Each poll now carries a `tse_registration` block linking it to its public TSE registration (full methodology, sampling/weighting design, statistician, CONRE, CNPJ, cost).
`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 2026-04-17.
`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/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.
Updates: dated and append-only; each daily commit preserves the full history natively (see CHANGELOG.md).

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

Downloads last month: 238

Total file size:

6.39 MB