Datasets:
File size: 4,590 Bytes
eb72cfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # PolyCAT Datasheet
Following the "Datasheets for Datasets" framework (Gebru et al., 2021).
## Motivation
**For what purpose was the dataset created?**
PolyCAT was created to study how geometric viewing constraints (polygon-shaped apertures) affect visual attention allocation on natural images. It enables saliency prediction research under non-rectangular viewing conditions.
**Who created the dataset and on behalf of which entity?**
Created by researchers at [institution name]. Funded by [funding source].
**Who funded the creation of the dataset?**
[To be filled]
## Composition
**What do the instances that comprise the dataset represent?**
Each instance is one eye-tracking trial: a participant's gaze behavior while viewing a CAT2000 image through a polygon aperture for 4 seconds.
**How many instances are there in total?**
~21,000 trials across 30 participants (702 trials per fully included participant, 2 parts x 351 trials). All 30 participants are fully included with both parts.
**Does the dataset contain all possible instances or is it a sample?**
It is a complete recording of all trials for all included participants. 600 of the ~2000 CAT2000 images were used.
**What data does each instance consist of?**
- Sample-level gaze coordinates at 500 Hz (binocular)
- Fixation events with position, duration, and timing
- Trial metadata (stimulus, polygon, cue position)
- Session metadata (calibration, validation)
**Is there a label or target associated with each instance?**
No explicit labels. The gaze data itself can be used to derive saliency maps.
**Is any information missing from individual instances?**
Some trials have tracking loss (blinks, look-away). Quality metrics are provided per-trial.
**Are there any errors, sources of noise, or redundancies?**
- EyeLink tracking noise (~0.25-0.5 deg typical accuracy)
- Blinks cause data gaps
- Some edge fixations may be at aperture boundaries
## Collection Process
**How was the data associated with each instance acquired?**
Eye movements were recorded using an EyeLink 1000+ eye tracker while participants viewed stimuli on a 27" 4K monitor.
**What mechanisms or procedures were used to collect the data?**
Custom PsychoPy experiment with EyeLink integration. 13-point calibration before each block, drift check before each trial.
**Who was involved in the data collection process?**
Trained experimenters supervised each session. Participants were university students.
**Over what timeframe was the data collected?**
January 15 - February 25, 2026.
**Were any ethical review processes conducted?**
Yes. The study was approved by the institutional ethics review board. All participants provided written informed consent.
## Preprocessing
**Was any preprocessing/cleaning/labeling of the data done?**
- Raw EDF files were converted to CSV/TSV format
- Fixations were extracted using EyeLink's built-in detection algorithm
- Split sessions were concatenated using trial UIDs
- Quality metrics were computed per trial and participant
**Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?**
Yes. The original session logs and EDF files are preserved in `data/raw_output_data/`.
## Uses
**Has the dataset been used for any tasks already?**
Initial analyses are presented in the accompanying ETRA 2026 paper.
**What (other) tasks could the dataset be used for?**
- Saliency prediction with aperture constraints
- Scanpath modeling and prediction
- Center bias analysis under geometric constraints
- Polygon geometry effects on visual exploration
**Is there anything about the composition of the dataset or the way it was collected that might impact future uses?**
The polygon apertures create a unique viewing constraint not present in standard saliency datasets. Models should account for the masked regions.
## Distribution
**How will the dataset be distributed?**
Via the university lab's SharePoint repository. A permanent link will be provided in the published paper.
**When was the dataset first released?**
2026 (ETRA 2026 conference).
**What license is the dataset distributed under?**
CC BY 4.0. Note that the CAT2000 images are subject to their original license.
## Maintenance
**Who is supporting/hosting/maintaining the dataset?**
[Institution name and contact information]
**Will the dataset be updated?**
Version updates will be documented with changelogs.
**How can the owner/curator/manager of the dataset be contacted?**
[Contact email]
|