OneStop: A 360-Participant English Eye Tracking Dataset with Different Reading Regimes#

📄 Paper | 📚 Documentation | 💾 Data | 🔬 More from LaCC Lab

Example#

Trial GIF

Overview#

OneStop Eye Movements (in short OneStop) is a large-scale English corpus of eye movements in reading with 360 L1 participants, 2.6 million word tokens and 152 hours of recorded eye tracking data. The dataset was collected using an EyeLink 1000 Plus eyetracker (SR Research).

OneStop comprises four sub-corpora, one for each of the following reading regimes:

  • Ordinary reading for comprehension

  • Information seeking

  • Repeated reading

  • Information seeking in repeated reading

OneStop Overview

We provide the entire corpus, as well as each of the sub-corpora separately. If you are looking for a general purpose eye tracking corpus (like Dundee, GECO, MECO and others), we recommend downloading the ordinary reading sub-corpus.

Key Features#

Texts and Reading Comprehension Materials#

  • Taken from the OneStopQA dataset (Paper Data 🤗)

  • 30 articles with 162 paragraphs in English from the Guardian.

  • Annotations of part-of-speech tags, syntactic dependency trees, word frequency and word surprisal.

  • Each paragraph has two versions: an Advanced version (original Guardian text) and a simplified Elementary version.

  • Extensively piloted reading comprehension questions based on the STARC (Structured Annotations for Reading Comprehension) annotation framework.

    • 3 multiple-choice reading comprehension questions per paragraph.

    • 486 reading comprehension questions in total.

    • Auxiliary text annotations for answer choices.

Statistics#

Statistics of OneStop and other public broad-coverage eyetracking datasets for English L1.

Category

Dataset

Subjects

Age

Words

Words Recorded

Qestions

Subjects per Question

Questions per Subject

Reading Comprehension

OneStop

360

22.8±5.6

19,425 (Advanced)
15,737 (Elementary)
19,221 (QA)

2,632,159 (Paragraphs)
1,311,752 (QA)

486

20

54

SB-SAT L1

66

NA

2,539

167,574

20

95

20

Passages

Dundee

10

NA

51,502

307,214

NA

10

NA

GECO L1

14

21.8±5.6

56,410

774,015

NA

14

NA

Provo

84

NA

2,689

225,624

0

0

0

MECO En

46

21.0±2.2

2,109

83,246

48

46

48

Sentences

CELER L1

69

26.3±6.7

61,233

122,423

78

69

78

ZuCo

18

34.3±8.0

15,138

272,484

42

18

42

UCL

43

25.8±7.5

1,932

81,144

110

43

110

‘Reading Comprehension’ are datasets with a substantial reading comprehension component over piloted reading comprehension materials. The remaining datasets are general purpose datasets over passages or individual sentences. ‘Words’ is the number of words in the textual corpus. ‘Words Recorded’ is the number of word tokens for which tracking data was collected. ‘NA’: data not available.

Controlled experimental manipulations#

  1. Reading goal: ordinary reading for comprehension or information seeking.

  2. Paragraph difficulty level: original Guardian article (Advanced) or simplified (Elementary).

  3. Question identity: one of three possible questions for each paragraph.

  4. Prior exposure to the text: first reading or repeated reading.

Experiment Structure#

OneStop Experiment Design

Each participant reads 10 Guardian articles paragraph by paragraph, and answers a reading comprehension question after each paragraph. After reading a 10-article batch, participants read two of the previously presented articles for a second time. Half of the participants are in an information seeking regime where they are presented with the question prior to reading the paragraph.

Trial Structure#

A single experimental trial consists of reading a paragraph and answering one reading comprehension question about it. Trials have the following Interest Periods, corresponding to pages in the experiment:

  1. Question Preview (only in the information seeking regime) - the participant reads the question (self-paced).

  2. Paragraph - the participant reads the paragraph (self-paced).

  3. Question - the participant reads the question again (self-paced).

  4. QA - retains the question, and also displays the four possible answers in a cross arrangement. Participants are then required to choose one of the answers and confirm their choice (self-paced).

  5. Feedback - informs the participants on whether they answered the question correctly (presented for one second).

Pages presented only in the information seeking regime are depicted in green.

Trial Structure

Overall, the dataset includes 19,438 regular trials, 9,720 in the information seeking regime and 9,718 in the ordinary reading regime. The dataset also includes 3,888 repeated reading trials, split equally between the two reading regimes.

Obtaining the Data#

Direct Download from OSF#

The data is hosted on OSF. We provide the possibility to download four sub-corpora with eye movement recordings from paragraph reading, one for each of the following reading regimes:

  1. OneStop Ordinary Reading - download this data if you are interested in a general purpose eye tracking dataset

  2. OneStop Information Seeking

  3. OneStop Repeated Reading

  4. OneStop Information Seeking in Repeated Reading

and two versions of the full dataset:

  1. OneStop - paragraph reading for the entire dataset.

  2. OneStop - Full - complete trials for the entire dataset, divided into title, question preview, paragraph, question, answers, QA (question + answers) and feedback.

In addition, we release metadata files which include the participant questionnaire and experiment summary statistics.

Python Script#

The data can also be downloaded using the provided Python script. The script will download and extract the data files.

Basic usage to download the entire dataset:

  1. Make sure you have Python installed.

    • If you don’t have Python installed, you can download it from here.

  2. Get the Code

    • Open your terminal/command prompt:

      • Windows: Press Win + R, type cmd and press Enter

      • Mac: Press Cmd + Space, type terminal and press Enter

    • Run this command to download the code:

      git clone https://github.com/lacclab/OneStop-Eye-Movements.git

    • Move into the downloaded folder:

      cd OneStop-Movements

  3. Run the Download Script

    • Run this command to download the full dataset:

      python onestop/download_data_files.py

    • The data will be downloaded to a folder called “OneStop”

    • Available options:

      • --extract: Extract downloaded zip files (default: True)

      • --asc: Download ASC files (default: False)

      • --edf: Download EDF files (default: False)

      • -o, --output-folder: Specify output folder (default: “OneStop”)

      • --mode: Choose dataset subcorpora to download (default: “full”)

        • Options: “full”, “all-regimes”, “ordinary”, “information-seeking”, “repeated”,”information-seeking-in-repeated”

  4. Download Specific Parts (Optional)

Example usage to download only the ordinary reading subset:


python onestop/download_data_files.py --mode ordinary

pymovements integration#

OneStop is partially integrated into the pymovements package. The package allows for easy download of the full paragraph corpora. The following code snippet shows how to download the data:

First, install the package in the terminal:

pip install pymovements

Then, use the following python code to download the data:

import pymovements as pm
dataset = pm.Dataset('OneStop', path='data/OneStop')

dataset.download()

This will download the data to the data/OneStop folder. You can also specify a different path by changing the path argument.

Documentation Structure#

The documentation is organized into the following sections:

  • Data Files and Variables: Provides detailed information about the data files and variables used in the project.

  • Known Issues: Documents any known issues.

  • Scripts: Contains scripts for data preprocessing and analysis reproduction.

Citation#

Paper: OneStop: A 360-Participant English Eye Tracking Dataset with Different Reading Regimes

@article{berzak2025onestop,
  title={OneStop: A 360-Participant English Eye Tracking Dataset with Different Reading Regimes},
  author={Berzak, Yevgeni and Malmaud, Jonathan and Shubi, Omer and Meiri, Yoav and Lion, Ella and Levy, Roger},
  journal={PsyArXiv preprint},
  year={2025}
}

License#

The data and code are licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License