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Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.

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Cultural object collections attract and delight spectators since ancient times.Yet, they also easily overwhelm visitors due to their perceptual richness and associated information. Similarly, digitized collections appear as complex, multifaceted phenomena, which can be challenging to grasp and navigate. Though visualizations can create various types of collection overviews for that matter, they do not easily assemble into a "big picture" or lead to an integrated understanding. We introduce coherence techniques to maximize connections between multiple views and apply them to the prototype PolyCube system of collection visualization: with map, set, and network visualizations it makes spatial, categorical, and relational collection aspects visible. For the essential temporal dimension, it offers four different views: superimposition, animation, juxtaposition, and space-time cube representations. A user study confirmed that better integrated visualizations support synoptic, cross-dimensional insights. An outlook is dedicated to the system's applicability within other arts and humanities data domains.

Spatiotemporal visualizations pose certain challenges for comprehension and reasoning processes. This specifically applies to casual users who want to gain an overview of the spatiotemporal origins of large cultural collections in a relatively short time. Based on a distributed cognition approach, we compare four visualization techniques (coordinated multiple views, color coding, animation and space-time cube representations), which offer different solutions to represent spatial and temporal information in an integrated fashion. We firstly assess the strengths and weaknesses of these techniques with regard to existing work. We then complement these findings with a close look at the reasoning processes of 18 casual users. In a mixed methods study, they explored a photo collection from the middle of the 20th century by means of all four spatiotemporal visualization techniques. We investigate and compare the effects of these techniques on processes of internalization, spatiotemporal reasoning and comprehension by analyzing the observed spatial, temporal and spatiotemporal insights and the degree of connectedness of these insights. Our results provide an in-depth look at how each visualization technique supports the construction of coherent internal representations and, together with data on cognitive load and subjective references, help to understand for which tasks and users they are suited.