---
pretty_name: Verasight U.S. Public Opinion Data Library
license: cc-by-4.0
language:
- en
multilinguality:
- monolingual
source_datasets:
- original
size_categories:
- 10K<n<100K
tags:
- tabular
- text
- datasets
- pandas
- dask
- mlcroissant
- public-opinion
- polling
- polls
- survey
- survey-research
- social-science
- united-states
- artificial-intelligence
- politics
- health
- economics
- culture
configs:
- config_name: questions
  default: true
  data_files:
  - split: train
    path: data/questions.parquet
- config_name: estimates
  data_files:
  - split: train
    path: data/estimates.parquet
- config_name: pages
  data_files:
  - split: train
    path: data/pages.parquet
---

# A source-linked index of what U.S. adults think

[![Explore the Verasight Data Library](https://data.verasight.io/og/verasight-data-library.png)](https://data.verasight.io/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=hero)

The Verasight Data Library makes original U.S. public opinion research
searchable and ready for analysis. Discover questions and weighted toplines
across AI & Tech, Culture, Health, Money, Politics, Sports, then follow every record to a human-readable finding and
its verified primary source report.

**[Explore findings, search topics, and cite the research at data.verasight.io](https://data.verasight.io/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=hero)**

## Coverage at a glance

| Survey waves | Years | Published questions | Topline estimates | Data Library pages | Categories |
| ---: | :---: | ---: | ---: | ---: | ---: |
| 37 | 2022 to 2026 | 1,697 | 8,695 | 1,430 | 6 |

[Browse all questions](https://data.verasight.io/questions/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=questions) · [Search the library](https://data.verasight.io/search/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=search) ·
[Read the methodology](https://data.verasight.io/methodology/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=methodology)

Explore by topic: [AI & Tech](https://data.verasight.io/c/ai/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_ai) · [Culture](https://data.verasight.io/c/culture/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_culture) · [Health](https://data.verasight.io/c/health/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_health) · [Money & Finance](https://data.verasight.io/c/money/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_money) · [Politics](https://data.verasight.io/c/politics/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_politics) · [Sports](https://data.verasight.io/c/sports/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=topic_sports)

## What you can do with it

| If you are... | Use the dataset to... |
| --- | --- |
| A journalist or editor | Find citable public opinion numbers and trace them to exact wording and verified sources. |
| A researcher or social scientist | Filter questions by topic, field dates, sample size, and weighted topline estimates. |
| An AI, search, or RAG builder | Index current, sourced opinions while retaining URLs for retrieval and attribution. |
| A policy, communications, or insights team | Explore how U.S. adults respond across six recurring topic areas. |

## Why this dataset

- **Original measured opinion:** findings come from Verasight survey research,
  not synthetic responses or a collection of prompts without respondent data.
- **Question-level provenance:** question wording, field dates, base sizes,
  source status, verified report links, and Data Library links travel with the
  records.
- **Human and machine-readable:** Parquet tables support analysis, while the
  linked Data Library pages add summaries, context, and citation paths.
- **Transparent methodology:** Verasight publishes its recruitment, weighting,
  margin-of-error, wording, and topline practices and participates in the AAPOR
  Transparency Initiative.
- **Audited public boundary:** this package contains only reviewed aggregate
  findings. It excludes respondent-level records, raw survey files, unpublished
  questions, and free-text responses.

## Use the right layer

| Layer | Best for | Role |
| --- | --- | --- |
| This Hugging Face dataset | Filtering, analysis, indexing, and reproducible discovery | Machine-readable distribution layer |
| [Verasight Data Library](https://data.verasight.io/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=hero) | Reading, browsing, sharing, and citing findings | Canonical interpretation layer |
| Verified Verasight source reports | Exact questionnaires, toplines, and survey methodology | Primary source record |

## Dataset contents

- `questions`: 1,697 published source questions with
  wording, field dates, methodology, source citations, and Data Library URLs.
- `estimates`: 8,695 weighted topline option estimates,
  each with a direct Data Library URL.
- `pages`: 1,430 published featured-topic and standalone
  question pages with summaries and canonical URLs.

All three subsets use the `train` split only as a Hugging Face loading
convention. The data is not divided into machine-learning training and test
sets.

## Load with Datasets

```python
from datasets import load_dataset

questions = load_dataset("Verasight/verasight-data-library", "questions", split="train")
estimates = load_dataset("Verasight/verasight-data-library", "estimates", split="train")
pages = load_dataset("Verasight/verasight-data-library", "pages", split="train")
```

Find matching questions and continue to the human-readable finding:

```python
for row in questions:
    if "artificial intelligence" in row["title"].lower():
        print(row["title"])
        print(row["data_library_url"])
```

## Key fields

### `questions`

- `title` and `context`: published wording and supporting prompt text.
- `field_date_start`, `field_date_end`, `base_n`, and `margin_of_error`:
  survey context needed for interpretation.
- `canonical_url`: the verified source report or question anchor.
- `data_library_url`: the preferred human-readable page for this question.
- `page_urls`: every Data Library page that uses the question.
- `categories`: the topic areas assigned by the Data Library.

### `estimates`

- `option_label`: the published response option.
- `weighted_share`: the estimate on a 0 to 1 scale.
- `weighted_percent`: the same estimate on a 0 to 100 scale.
- `unweighted_n`: the available unweighted count for that option.
- `canonical_url` and `data_library_url`: source and interpretation paths.

### `pages`

- `title`, `dek`, and `summary`: the published editorial framing.
- `url` and `markdown_url`: canonical HTML and machine-readable page versions.
- `source_question_ids` and `source_wave_ids`: provenance for the page.

## Source and scope

The release boundary is the site build's `structured-output.json` manifest.
That manifest contains the reviewed public surface, not the broader canonical
or confidential pipeline data. Source reports remain the canonical survey
references. Each applicable record includes a public report URL.

Every question and estimate includes both a Data Library URL and a verified
canonical source report URL. The public release exporter rejects pending or
unavailable source records before creating the package.

## Interpretation and methodology

`weighted_share` is a proportion from 0 to 1. `weighted_percent` is the same
estimate on a 0 to 100 scale. `unweighted_n` is the available unweighted option
count. Consult the linked source and methodology before comparing estimates or
drawing conclusions.

For methodology details, including panel recruitment, respondent verification,
weighting, margins of error, and transparency practices, see the
[Data Library methodology](https://data.verasight.io/methodology/?utm_source=huggingface&utm_medium=dataset&utm_campaign=verasight_data_library&utm_content=methodology). Methods and question context can
vary by survey, so use field dates, base sizes, and linked source reports when
making comparisons.

## Citation and attribution

For a specific finding, cite the record's `data_library_url` and consult its
`canonical_url` source report. This preserves the wording, timing, methodology,
and interpretation that belong with the estimate.

Recommended dataset attribution: Verasight, *Verasight U.S. Public Opinion Data
Library*, [data.verasight.io](https://data.verasight.io/) and `Verasight/verasight-data-library`,
licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Indicate if you made changes.

## Limitations and responsible use

- The tables contain aggregate toplines, not respondent-level microdata.
- Published percentages describe measured opinion during the listed field
  dates. They do not establish causation or predict future opinion.
- The dataset does not include every question Verasight has fielded. It includes
  only findings approved for the public Data Library.
- The `train` split is a loading convention, not a recommendation to use the
  records as model-training data.

## Release freshness

This release covers 37 source waves from
2022 to 2026. Source build timestamp: `2026-10-23T18:34:54.108Z`. The clean URLs
stored in the tables are stable citation targets even when this snapshot is
superseded by a later release.

## License

Unless otherwise noted, the dataset files and original documentation in this
repository are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). See `LICENSE`
for scope, attribution, and exclusions.