Datasets:

AFOS-Analytics1
/

brazil-2026-electoral-divergence

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
name: string
title: string
description: string
version: string
created: timestamp[s]
homepage: string
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licenses: list<item: struct<name: string, title: string, path: string>>
  child 0, item: struct<name: string, title: string, path: string>
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contributors: list<item: struct<title: string, role: string, path: string>>
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                      child 0, @id: string
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                      child 0, column: string
cr:conformsTo: string
@context: struct<@vocab: string, cr: string, sc: string, dataType: struct<@id: string, @type: string>, field:  (... 95 chars omitted)
  child 0, @vocab: string
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license: string
url: string
to
{'@context': {'@vocab': Value('string'), 'cr': Value('string'), 'sc': Value('string'), 'dataType': {'@id': Value('string'), '@type': Value('string')}, 'field': Value('string'), 'fileObject': Value('string'), 'recordSet': Value('string'), 'source': Value('string'), 'extract': Value('string'), 'column': Value('string')}, '@type': Value('string'), 'cr:conformsTo': Value('string'), 'name': Value('string'), 'description': Value('string'), 'license': Value('string'), 'url': Value('string'), 'version': Value('string'), 'citation': Value('string'), 'keywords': List(Value('string')), 'distribution': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'contentUrl': Value('string'), 'encodingFormat': Value('string'), 'sha256': Value('string')}), 'recordSet': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'field': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'dataType': Value('string'), 'source': {'fileObject': {'@id': Value('string')}, 'extract': {'column': Value('string')}}})})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              name: string
              title: string
              description: string
              version: string
              created: timestamp[s]
              homepage: string
              keywords: list<item: string>
                child 0, item: string
              licenses: list<item: struct<name: string, title: string, path: string>>
                child 0, item: struct<name: string, title: string, path: string>
                    child 0, name: string
                    child 1, title: string
                    child 2, path: string
              contributors: list<item: struct<title: string, role: string, path: string>>
                child 0, item: struct<title: string, role: string, path: string>
                    child 0, title: string
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              sources: list<item: struct<title: string>>
                child 0, item: struct<title: string>
                    child 0, title: string
              resources: list<item: struct<name: string, type: string, path: string, scheme: string, format: string, mediatyp (... 115 chars omitted)
                child 0, item: struct<name: string, type: string, path: string, scheme: string, format: string, mediatype: string,  (... 103 chars omitted)
                    child 0, name: string
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                        child 0, fields: list<item: struct<name: string, type: string>>
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              0, item: struct<@type: string, @id: string, name: string, field: list<item: struct<@type: string, @id: string (... 117 chars omitted)
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                    child 3, field: list<item: struct<@type: string, @id: string, name: string, dataType: string, source: struct<fileObj (... 60 chars omitted)
                        child 0, item: struct<@type: string, @id: string, name: string, dataType: string, source: struct<fileObject: struct (... 48 chars omitted)
                            child 0, @type: string
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                                    child 0, @id: string
                                child 1, extract: struct<column: string>
                                    child 0, column: string
              cr:conformsTo: string
              @context: struct<@vocab: string, cr: string, sc: string, dataType: struct<@id: string, @type: string>, field:  (... 95 chars omitted)
                child 0, @vocab: string
                child 1, cr: string
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                child 5, fileObject: string
                child 6, recordSet: string
                child 7, source: string
                child 8, extract: string
                child 9, column: string
              license: string
              url: string
              to
              {'@context': {'@vocab': Value('string'), 'cr': Value('string'), 'sc': Value('string'), 'dataType': {'@id': Value('string'), '@type': Value('string')}, 'field': Value('string'), 'fileObject': Value('string'), 'recordSet': Value('string'), 'source': Value('string'), 'extract': Value('string'), 'column': Value('string')}, '@type': Value('string'), 'cr:conformsTo': Value('string'), 'name': Value('string'), 'description': Value('string'), 'license': Value('string'), 'url': Value('string'), 'version': Value('string'), 'citation': Value('string'), 'keywords': List(Value('string')), 'distribution': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'contentUrl': Value('string'), 'encodingFormat': Value('string'), 'sha256': Value('string')}), 'recordSet': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'field': List({'@type': Value('string'), '@id': Value('string'), 'name': Value('string'), 'dataType': Value('string'), 'source': {'fileObject': {'@id': Value('string')}, 'extract': {'column': Value('string')}}})})}
              because column names don't match

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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.

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.

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.

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.

📁 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).

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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13.9 MB