Dr. Daniel Reimann
Research Interests
Learning, visual perception, graph comprehension, data visualization
Articles in peer-reviewed journals
Reimann, D., Ram, N., & Gaschler, R. (2023). Lollipops help align visual and statistical fit estimates in scatterplots with nonlinear models. IEEE Transactions on Visualization and Computer Graphics, 29(7), 3436-3440. https://doi.org/10.1109/tvcg.2022.3158093
Blech, C., Reimann, D., Ram, N., & Gaschler, R. (2023). Is detecting discontinuity difficult? Evidence from the visual trend classification of scatterplots. The American Journal of Psychology, 136(1), 1-19. https://doi.org/10.5406/19398298.136.1.01
Reimann, D., Schulz, A., Ram, N., & Gaschler, R. (2023). Color-encoded links improve homophily perception in node-link diagrams. IEEE Transactions on Visualization and Computer Graphics, 29(12), 5593-5598. https://doi.org/10.1109/tvcg.2022.3221014
Reimann, D., Struwe, M., Ram, N., & Gaschler, R. (2022). Typicality effect in data graphs. Visual Communication. https://doi.org/10.1177/14703572221130445
Reimann, D., Schulz, A., & Gaschler, R. (2021). Homophily at a glance: Visual homophily estimation in network graphs is robust under time constraints. SN Social Sciences, 1(6). https://doi.org/10.1007/s43545-021-00153-2
Reimann, D., Blech, C., Ram, N., & Gaschler, R. (2021). Visual model fit estimation in scatterplots: Influence of amount and decentering of noise. IEEE Transactions on Visualization and Computer Graphics, 27(9), 3834-3838. https://doi.org/10.1109/tvcg.2021.3051853
Reimann, D., Blech, C., & Gaschler, R. (2020). Visual model fit estimation in scatterplots and distribution of attention: Influence of slope and noise level. Experimental Psychology, 67(5), 292-302. https://doi.org/10.1027/1618-3169/a000499
Schoor, C., Hahnel, C., Artelt, C., Reimann, D., Kröhne, U., & Goldhammer, F. (2020). Entwicklung und Skalierung eines Tests zur Erfassung des Verständnisses multipler Dokumente von Studierenden. Diagnostica, 66(2), 123-135. https://doi.org/10.1026/0012-1924/a000231
