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Part 1: Document Description
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Citation |
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Title: |
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" |
Identification Number: |
doi:10.18419/darus-2826 |
Distributor: |
DaRUS |
Date of Distribution: |
2022-06-21 |
Version: |
1 |
Bibliographic Citation: |
Wang, Yao; Bulling, Andreas, 2022, "Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"", https://doi.org/10.18419/darus-2826, DaRUS, V1, UNF:6:AuvgRc09o1rESd63AqlW9Q== [fileUNF] |
Citation |
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Title: |
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" |
Alternative Title: |
VisRecall dataset |
Identification Number: |
doi:10.18419/darus-2826 |
Authoring Entity: |
Wang, Yao (Universität Stuttgart) |
Bulling, Andreas (Universität Stuttgart) |
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Other identifications and acknowledgements: |
Wang, Yao |
Other identifications and acknowledgements: |
Bulling, Andreas |
Other identifications and acknowledgements: |
Jiao, Chuhan |
Grant Number: |
251654672 |
Distributor: |
DaRUS |
Access Authority: |
Bulling, Andreas |
Depositor: |
Wang, Yao |
Date of Deposit: |
2022-05-03 |
Holdings Information: |
https://doi.org/10.18419/darus-2826 |
Study Scope |
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Keywords: |
Computer and Information Science, Information Visualization, Data Visualization, Deep Learning, Short-term Memory |
Abstract: |
<p>Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far.</p> <p>We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions from five question types, which are related to titles, filtering information, finding extrema, retrieving values, and understanding visualisations. It aims to make fundamental contributions towards a new generation of methods to assist designers in optimising information visualisations.</p> <p>This dataset contains stimuli and collected participant data of VisRecall. The structure of the dataset is described in the <a href="https://darus.uni-stuttgart.de/file.xhtml?persistentId=doi:10.18419/darus-2826/15">README</a>-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 Software) within this dataset. </p> |
Date of Collection: |
2021-08-01-2021-08-23 |
Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Y. Wang, C. Jiao, M. Bâce and A. Bulling, "VisRecall: Quantifying Information Visualisation Recallability via Question Answering," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 4995-5005, 1 Dec. 2022. |
Identification Number: |
10.1109/TVCG.2022.3198163 |
Bibliographic Citation: |
Y. Wang, C. Jiao, M. Bâce and A. Bulling, "VisRecall: Quantifying Information Visualisation Recallability via Question Answering," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 4995-5005, 1 Dec. 2022. |
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Readme of this dataset |
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economist_daily_chart_127.png |
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image/png |
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economist_daily_chart_184.png |
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image/png |
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economist_daily_chart_200.png |
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economist_daily_chart_202.png |
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image/png |
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economist_daily_chart_267.png |
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image/png |
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economist_daily_chart_319.png |
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image/png |
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economist_daily_chart_513.png |
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image/png |
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treasuryI01_1.png |
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image/png |
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treasuryJ02_2.png |
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image/png |
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treasuryK03_3.png |
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image/png |
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treasuryL03_2.png |
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image/png |
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whoB04_1.png |
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image/png |
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whoB08_1.png |
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image/png |
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whoJ01_1.png |
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image/png |
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whoN06_1.png |
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image/png |
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whoN27.png |
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image/png |
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wsj593.png |
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image/png |
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_Di4yk2H64QEBkEncRGMhg==.0.png |
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image/png |
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subject_file_0_1.json |
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application/json |
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subject_file_0_10.json |
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application/json |
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subject_file_0_2.json |
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application/json |
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subject_file_0_3.json |
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application/json |
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subject_file_0_4.json |
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application/json |
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subject_file_0_5.json |
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application/json |
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subject_file_0_6.json |
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application/json |
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subject_file_0_7.json |
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application/json |
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subject_file_0_8.json |
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application/json |
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subject_file_0_9.json |
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application/json |
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blur_vis.py |
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text/x-python |
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check_bad_img.py |
Notes: |
text/x-python |
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create_codecharts_dir.py |
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text/x-python |
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create_padded_image_dir.py |
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text/x-python |
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distribute_image_files_by_buckets.py |
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text/x-python |
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generate_central_fixation_cross.py |
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text/x-python |
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generate_codecharts.py |
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text/x-python |
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generate_sentinels.py |
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text/x-python |
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generate_spotlight.py |
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text/x-python |
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generate_tutorials.py |
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text/x-python |
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main.ipynb |
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application/x-ipynb+json |
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face_0.jpg |
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image/jpeg |
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face_1.jpg |
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face_2.jpg |
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face_3.jpg |
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face_4.jpg |
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face_5.jpg |
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face_6.jpg |
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face_7.jpg |
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image/jpeg |
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face_8.jpg |
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image/jpeg |
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face_9.jpg |
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image/jpeg |
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tutorial_real1.jpg |
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image/jpeg |
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tutorial_real2.jpg |
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tutorial_real3.jpg |
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tutorial_real4.jpg |
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tutorial_real5.jpg |
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image/jpeg |
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tutorial_real6.jpg |
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image/jpeg |
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tutorial_real7.jpg |
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image/jpeg |
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tutorial_real8.jpg |
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image/jpeg |
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tutorial_real9.jpg |
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image/jpeg |