The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
lastUpdate: timestamp[s]
lastSyncedAt: timestamp[s]
institutes: list<item: struct<name: string, reliability: int64, type: string, note: string>>
child 0, item: struct<name: string, reliability: int64, type: string, note: string>
child 0, name: string
child 1, reliability: int64
child 2, type: string
child 3, note: string
polls: list<item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: st (... 328 chars omitted)
child 0, item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: string, relia (... 316 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, fieldDates: string
child 8, note: string
child 9, 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 10, secondRound: list<item: struct<matchup: string,
...
president: string, approval6m: int64, disapproval6m: int64>>
child 0, item: struct<president: string, approval6m: int64, disapproval6m: int64>
child 0, president: string
child 1, approval6m: int64
child 2, disapproval6m: int64
child 5, lastUpdate: timestamp[s]
polymarketComparison: struct<note: string, sources: string, candidates: list<item: struct<name: string, pesquisaRange: str (... 181 chars omitted)
child 0, note: string
child 1, sources: string
child 2, candidates: list<item: struct<name: string, pesquisaRange: string, polymarket: string, tendenciaPesquisa: string (... 105 chars omitted)
child 0, item: struct<name: string, pesquisaRange: string, polymarket: string, tendenciaPesquisa: string, tendencia (... 93 chars omitted)
child 0, name: string
child 1, pesquisaRange: string
child 2, polymarket: string
child 3, tendenciaPesquisa: string
child 4, tendenciaPolymarket: string
child 5, odds: double
child 6, percentage: int64
child 7, lastUpdate: timestamp[s]
child 8, value: string
child 3, updatedAt: timestamp[s]
count: int64
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
lastUpdate: timestamp[s]
lastSyncedAt: timestamp[s]
institutes: list<item: struct<name: string, reliability: int64, type: string, note: string>>
child 0, item: struct<name: string, reliability: int64, type: string, note: string>
child 0, name: string
child 1, reliability: int64
child 2, type: string
child 3, note: string
polls: list<item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: st (... 328 chars omitted)
child 0, item: struct<institute: string, date: timestamp[s], sample: int64, margin: double, register: string, relia (... 316 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, fieldDates: string
child 8, note: string
child 9, 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 10, secondRound: list<item: struct<matchup: string,
...
president: string, approval6m: int64, disapproval6m: int64>>
child 0, item: struct<president: string, approval6m: int64, disapproval6m: int64>
child 0, president: string
child 1, approval6m: int64
child 2, disapproval6m: int64
child 5, lastUpdate: timestamp[s]
polymarketComparison: struct<note: string, sources: string, candidates: list<item: struct<name: string, pesquisaRange: str (... 181 chars omitted)
child 0, note: string
child 1, sources: string
child 2, candidates: list<item: struct<name: string, pesquisaRange: string, polymarket: string, tendenciaPesquisa: string (... 105 chars omitted)
child 0, item: struct<name: string, pesquisaRange: string, polymarket: string, tendenciaPesquisa: string, tendencia (... 93 chars omitted)
child 0, name: string
child 1, pesquisaRange: string
child 2, polymarket: string
child 3, tendenciaPesquisa: string
child 4, tendenciaPolymarket: string
child 5, odds: double
child 6, percentage: int64
child 7, lastUpdate: timestamp[s]
child 8, value: string
child 3, updatedAt: timestamp[s]
count: int64
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 matchNeed 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
| Path | Content / Conteúdo / Contenido |
|---|---|
data/divergence-{date}.csv |
date, candidate, polymarket_pct, poll_pct, divergence_pp |
snapshots/analysis-criteriosa/{date}.json |
Daily analysis: market × poll × press, per candidate |
snapshots/analysis-cards/{date}.json |
Thematic cards (sentiment, institutional, macro) |
polls/polls-data-{date}.json |
National polls (1st/2nd round) referenced that day |
news/news-{date}.json |
Public news links (source, title, URL, date) — no article bodies |
Sources / Fontes / Fuentes: Polymarket (live USD markets) · TSE-registered institutes · 400+ press outlets. Method & source code (Apache 2.0): github.com/AFOS-Analytics.
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