Data from: An extensive dataset of eye movements during viewing of complex images
Data files
Dec 09, 2017 version files 7.50 GB
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3D_raw.hdf
31.59 MB
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additional_meta.zip
11.05 KB
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AFC_raw.hdf
42.46 MB
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Age_study_raw.hdf
60.41 MB
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APP_raw.hdf
120.61 MB
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APPC_raw.hdf
21 MB
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Bias_raw.hdf
107.47 MB
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CrossModal_raw.hdf
38.05 MB
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CrossModal2_raw.hdf
17.98 MB
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EEG_raw.hdf
53.66 MB
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etdb_v1.0.hdf5
33.11 MB
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Eye Tracking Training Course.pdf
93.07 KB
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FaceDiscrimination_raw.hdf
152.63 MB
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FaceLearning_raw.hdf5
313.83 MB
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Filtered_raw.hdf
62.35 MB
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fixmat.py
1.08 KB
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Gap_raw.hdf
34.22 MB
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get_fixmat.m
750 B
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Head_Fixed_raw.hdf
279.07 MB
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Memory_I_raw.hdf
115.49 MB
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Memory_II_raw.hdf
68.50 MB
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meta.csv
5.64 KB
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Monocular_raw.hdf
184.57 MB
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Patch_raw.hdf
48.90 MB
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Scaled_raw.hdf
96.38 MB
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Stimuli_10.zip
82.38 MB
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Stimuli_11.zip
162.90 MB
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Stimuli_12.zip
79.80 MB
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Stimuli_14.zip
120.11 MB
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Stimuli_15.zip
51.07 MB
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Stimuli_16.zip
147.53 MB
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Stimuli_17.zip
641.49 MB
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Stimuli_18.zip
2.23 GB
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Stimuli_19.zip
179.28 MB
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Stimuli_20.zip
263.83 MB
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Stimuli_21.zip
2.46 MB
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Stimuli_22.zip
769.66 KB
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Stimuli_23.zip
274.63 MB
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Stimuli_24.zip
147.81 MB
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Stimuli_25.zip
126.77 MB
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Stimuli_26.zip
43.95 MB
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Stimuli_27.zip
35.59 MB
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Stimuli_28.zip
163.57 MB
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Stimuli_6.zip
44.79 MB
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Stimuli_7.zip
147.93 MB
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Stimuli_8.zip
124.92 MB
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Tactile_raw.hdf
250.99 MB
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Webtask_at_School_raw.hdf
291.46 MB
Abstract
We present a dataset of free-viewing eye-movement recordings that contains more than 2.7 million fixation locations from 949 observers on more than 1000 images from different categories. This dataset aggregates and harmonizes data from 23 different studies conducted at the Institute of Cognitive Science at Osnabrück University and the University Medical Center in Hamburg-Eppendorf. Trained personnel recorded all studies under standard conditions with homogeneous equipment and parameter settings. All studies allowed for free eye-movements, and differed in the age range of participants (~7-80 years), stimulus sizes, stimulus modifications (phase scrambled, spatial filtering, mirrored), and stimuli categories (natural and urban scenes, web sites, fractal, pink-noise, and ambiguous artistic figures). The size and variability of viewing behavior within this dataset presents a strong opportunity for evaluating and comparing computational models of overt attention, and furthermore, for thoroughly quantifying strategies of viewing behavior. This also makes the dataset a good starting point for investigating whether viewing strategies change in patient groups.