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OneStop dataset

OneStop Eye Movements is a 360-participant English eye-tracking-while-reading corpus (Berzak, Malmaud, Shubi, Meiri, Lion, Levy, Scientific Data 2025, doi:10.1038/s41597-025-06272-2). The app's bundled demo is a 3-participant subset of it; this page covers loading the full public corpus from OSF as a public dataset.

Two ways to load OneStop

  • Public dataset (this page) — downloads paragraph-level reports from OSF on demand, no setup. Pick the reading regime in the sidebar.
  • OneStop server bundle — points at a local lacclab export via the $ONESTOP_DATA_DIR environment variable, with per-pid Parquet shards for review-app deep links. See Export & troubleshooting.

Loading it

OneStop is exposed as a Public dataset. In the app, choose Public datasets → OneStop, then in OneStop options pick a reading regime and (optionally) a download folder:

Regime What it is
Ordinary reading Standard paragraph reading.
Information seeking Reading to answer a known question.
Repeated reading Re-reading the same paragraphs.
Information seeking (repeated) Information seeking during repeated reading.

Each regime is a separate OSF download of two paragraph-level reports — the interest-area report (one row per word, with bounding boxes and reading measures) and the fixation report. The OneStop options panel lists the Expected files for the folder and shows whether the chosen regime's reports are already present. If they are, the corpus loads with no network access; if not, click ⬇ Download to fetch them into the folder (cached on disk, so only the first load of a regime pays the download — the reports range from tens to a few hundred MB).

OneStop's reports use the same schema as the bundled demo, so they flow through the normal auto-detect → normalize pipeline — the sidebar Column mapping panels still appear and stay overridable. Fixation and interest-area coordinates are full-screen pixels on OneStop's 2560×1440 presentation monitor, so the canvas renders true-to-scale to that monitor.

From the Python API

The same loader is available headlessly:

from scanpath_studio.datasets import onestop_raw_frames
from scanpath_studio import data

# Fetch + read the chosen regime's OSF reports (cached under `root`).
words, fixations = onestop_raw_frames(
    "data/OneStop", regime="ordinary", download=True
)

# Normalize like an upload, then plot.
ws, fs = data.propose_word_schema(words), data.propose_fix_schema(fixations)
words = data.normalize_words(words, ws)
fixations = data.normalize_fixations(fixations, fs)
words, fixations = data.harmonize_frames(words, fixations)

The implementation lives in datasets.py (onestop_raw_frames / download_onestop); the OSF file ids per regime come from the OneStop repo's download_data_files.py.