VisRecall

We 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.

The full dataset can be downloaded at: https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2826.

Contact: Yao Wang,

The data is only to be used for non-commercial scientific purposes. If you use this dataset in a scientific publication, please cite the following paper:

1. 

   VisRecall: Quantifying Information Visualisation Recallability via Question Answering

   Yao Wang, Chuhan Jiao, Mihai Bâce, Andreas Bulling

   IEEE Transactions on Visualization and Computer Graphics (TVCG), 28 (12), pp. 4995-5005, 2022.

   Abstract Links BibTeX Project

   Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.

   doi: 10.1109/TVCG.2022.3198163

   Paper: wang22_tvcg.pdf

   Supplementary Material: wang22_tvcg_sup.pdf

   Dataset: https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2826

   @article{wang22_tvcg, title = {VisRecall: Quantifying Information Visualisation Recallability via Question Answering}, author = {Wang, Yao and Jiao, Chuhan and Bâce, Mihai and Bulling, Andreas}, year = {2022}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, volume = {28}, number = {12}, pages = {4995-5005}, doi = {10.1109/TVCG.2022.3198163} }