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Apr 8, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)", https://doi.org/10.18419/darus-3138, DaRUS, V1, UNF:6:NwphGtoYrBQqd2TyRh0OHA== [fileUNF]
This dataset contains stimuli and collected participant data of VisRecall++. The structure of the dataset is described in the README-File. Further, if you are interested in related codes of the publication, you can find a copy of the code repository (see Metadata for Research Sof... |
Python Source Code - 5.7 KB -
MD5: bc554567b355511c4dadc543f9209a16
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Python Source Code - 4.3 KB -
MD5: 6dbc9847ff4b0d1c126a047f2c39d330
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Python Source Code - 2.0 KB -
MD5: 9ec79413fcd39fd024d78aca990b11a5
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Markdown Text - 471 B -
MD5: 6d5e28696c130c0c7d2ca4595e4483f1
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Markdown Text - 1.6 KB -
MD5: 7f5e4f3aa91c96da0dab17a722df928c
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Tabular Data - 1.3 MB - 8 Variables, 3000 Observations - UNF:6:MAoeMQ143uppJ/W6lUsOhg==
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Tabular Data - 4.3 MB - 8 Variables, 9445 Observations - UNF:6:ERaGx3GYsw/WTGloE0ANNw==
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Tabular Data - 951.9 KB - 8 Variables, 2200 Observations - UNF:6:nGvaIRk1SuiVJGs9NrbiTw==
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Tabular Data - 4.7 MB - 8 Variables, 10245 Observations - UNF:6:gF0WNHAi8l/kp/Q7ZxVdpA==
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