Evidence map›Paper›PMID 41559118›Full record

ArticleScientific data2026

Scene-level movie data from Amazon X-Ray in the US market combined with IMDb.

Safal Shrestha, Yeonie Heo, Alexander T J Barron, Minsu Park

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Safal Shrestha *New York University Abu Dhabi, Abu Dhabi, UAE.
Yeonie Heo *New York University Abu Dhabi, Abu Dhabi, UAE.
Alexander T J BarronNew York University Abu Dhabi, Abu Dhabi, UAE. atb8710@nyu.edu.ORCID http://orcid.org/0000-0002-3761-7683
Minsu ParkNew York University Abu Dhabi, Abu Dhabi, UAE. mp5500@nyu.edu.ORCID http://orcid.org/0000-0002-9610-2938

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a structured, scene-level dataset of movie content that addresses the limitations of previous research relying on small or non-standardized screenplay collections. Such collections often lack consistent scene representations and actor metadata and use draft versions that differ from their final cinematic products, limiting both the scale and accuracy for content-level analysis. To overcome these limitations, we compile scene breakdowns for 3,265 movies from Amazon X-Ray in the US Amazon Prime Video market, detailing the characters appearing in each scene and linking them to their corresponding IMDb IDs. Subtitles are included for the subset of 3,110 movies, providing complementary dialogue-level data, and each title is linked to its corresponding IMDb ID to enable augmentation with additional metadata for extended analyses. Integration of these resources can allow accurate, large-scale analyses of on-screen representation, character interactions, and narrative structure that were not feasible with earlier screenplay-based datasets. This dataset enhances the consistency and accessibility of movie data, providing a reliable stepping stone for quantitative research on film as cultural artifacts.

Identifiers

PMID41559118
PMCPMC12920774

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.