Evidence map›Paper›PMID 41491873›Full record

ArticleBehavior research methods2026

PupEyes: An interactive Python library for eye movement data processing.

Han Zhang, John Jonides

Abstract read
In one paragraph

Article in Behavior research methods, 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

2 authors.

Han ZhangUniversity of Michigan, Ann Arbor, MI, USA. hanzh@umich.edu.ORCID http://orcid.org/0000-0001-6087-0428
John JonidesUniversity of Michigan, Ann Arbor, MI, USA.

Funding

Investigating interference-control in ADHD using a novel forced-response methodR21MH129909 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JONIDES, JOHN · 2023 to 2024
$429k
National Science Foundation 2238151NIMH NIH HHS R21 MH129909NIMH NIH HHS R21MH129909
6 · The paper itself

Abstract

We present PupEyes, an open-source Python package for preprocessing and visualizing pupil size and fixation data. PupEyes supports data collected from EyeLink and Tobii eye-trackers as well as any generic dataset that conforms to minimal formatting standards. Developed with current best practices, PupEyes provides a comprehensive pupil preprocessing pipeline and interactive tools for data exploration and diagnosis. In addition to pupil size data, PupEyes provides interactive tools for visualizing fixation data, drawing areas of interest (AOIs), and computing AOI-based metrics. PupEyes uses the pandas data structure and can work seamlessly with other data analysis packages within the Python ecosystem. Overall, PupEyes (1) ensures that pupil size data are preprocessed in a principled, transparent, and reproducible manner, (2) helps researchers better understand their data through interactive visualizations, and (3) enables flexible extensions for further analysis tailored to specific research goals. To ensure computational reproducibility, we provide detailed, executable tutorials ( https://pupeyes.readthedocs.io/ ) that allow users to reproduce and modify the code examples in a virtual environment.

Indexed as

Eye MovementsSoftwareAnimalsEye-Tracking TechnologyFixation, OcularHumansPupilData analysisEye-trackingFixationPupillometryPython

Identifiers

PMID41491873
PMCPMC12769653

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.