DOI: 10.5281/zenodo.17158329
title: A fine-grained dataset of visualisation and interaction practices in web-based Digital Humanities projects

description: Description

This dataset classifies 587 visualisation–interaction units extracted from 186 web-based Digital Humanities projects, previously classified in a related dataset [1], allowing cross-references between them. Each row represents a distinct combination of visualisation technique(s) (e.g., map, bar chart, network) and associated interactive features within a project. The dataset provides a finer-grained view of design choices, documenting how visualisations and interactive possibilities are implemented, including their connection to narrative or non-narrative contexts, temporal encodings, and multi-view or reconfiguration strategies.

The building blocks of our dataset: defining visualisation–interaction units

A visualisation–interaction unit is a distinct configuration combining a visualisation technique (or multiple techniques when linked through coordinated views) with a specific set of interactive features. Following [2], we consider these elements as working interdependently to achieve a shared data-related goal.These units form the basic level of analysis in our dataset, with each row representing one unit. Units are distinguished not only by their visualisation technique and affordable interaction, but also by their temporal characteristics and narrative context. Temporal encodings—such as time axes, animated transitions, or other time-based variables—define a new unit even if the visualisation and interaction remain unchanged. Similarly, an identical configuration appearing in both a narrative and a non-narrative context counts as two separate units, reflecting their differing intent and function.Accordingly, the number of units in a project does not directly correspond to the number of visualisations it contains. Two otherwise identical charts are treated as distinct units if they differ in interactive features, temporal encoding, or narrative context, while exact duplicates without variation are counted as a single unit. For example, if a project contains five bar charts that all support drill-down on click, they are counted as a single unit. Conversely, if the same five bar charts each offer different interactive capabilities, they are treated as separate units based on their unique visualisation–interaction combinations.Every unit includes at least one visualisation technique, although interaction may or may not be present (for instance, in a static chart).

 

Classification schema: categories and columns

Identifiers. Three columns in the dataset are dedicated to uniquely identifying units and their relationships within projects:





project_id: the identifier of the project to which the unit belongs. This reuses the same incremental IDs from [1] to enable cross-referencing between datasets.




vis_unit_id: the identifier of the individual visualisation–interaction unit. IDs increment within each project and reset to 1 for a new project.




visualisation_version: an identifier used to track interactive transformations of visualisations. Multiple rows can share the same project_id and vis_unit_id if they represent different states of the same view, triggered by user interactions that modify the visual form.



 

Narrativity. We record whether a visualisation–interaction unit is employed within a narrative context. Some projects contain units exclusively in narrative or non-narrative settings, while others include units in both. The relevant columns are:





non_narrative: a boolean value indicating whether the unit appears in non-narrative contexts.




narrative: a boolean value indicating whether the unit is used in narrative contexts (including both strongly guided, author-driven data stories and more interactive, user-driven narratives).



 

Visualisation techniques. We adopt, and where necessary adapt, the terminology and definitions from [3]. Each column corresponds to a specific type of visualisation and indicates (by means of a boolean value) whether that visualisation technique is present in a given visualisation–interaction unit. The following columns and inclusion criteria are used to encode this information:





plot: visual representations that map data points onto a two-dimensional coordinate system.




cluster_or_set: sets or cluster-based visualisations used to unveil possible inter-object similarities.




map: geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.




network: visual representations highlighting relational aspects through nodes connected by links or edges.




hierarchical_diagram: tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.




treemap: still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.




word_cloud: clouds of words, where each instance’s size is proportional to its frequency in a related context




bars: includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.




line_chart: the display of information as sequential data points connected by straight-line segments.




area_chart: similar to a line chart but with a filled area below the segments. It also includes density plots.




pie_chart: circular graphs divided into slices, which can also use multi-level solutions.




plot_3d: plots that use a third dimension to encode an additional variable.




proportional_area: representations used to compare values through area size. Typically, using circle- or square-like shapes.


timeline: the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [3], we also include structured solutions resembling Gantt chart layouts.



other: it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.



 

Temporal encodings. We identify techniques used to encode temporality (except for timelines, where temporal encoding is tacitly assumed). Columns:





temporal_dimension: to report when time is mapped to any dimension of a visualisation. We use the term “dimension” and not “axis” as in [3] as more appropriate for radial layouts or more complex representational choices.




animation: temporality is perceived through an animation changing the visualisation according to time flow.




visual_variable: another visual encoding strategy is used to represent any temporality-related variable (e.g. colour).



 

Multi-type coordinated views. Tracking coordinated views across the dataset is limited to cases where multiple visualisation types can be clearly identified within a single view. For these instances, a dedicated column indicates which visualisation—if any—plays a central or dominant role:



primary_visualisation: contains the name of the visualisation technique (as defined in the corresponding column) that holds a dominant role in the coordinated view. If no single type can be considered guiding because multiple types have similar perceived importance, the column contains "NA".


 

Interactions and allowable actions. A set of categories to assess affordable interactions based on the concept of user intent [2] and user-allowed actions [4]. The following categories roughly match the manipulative subset of methods in the conception of [5]. Only interactions that affect the aspect of the visualisation or the visual representation of its data points, symbols, and glyphs are taken into consideration. A two-level analysis is enabled by the columns, referring to interaction categories also explored at an aggregated project level in [1], and their values, exposing more specific interactive capabilities (multiple values are divided by a semicolumn). By interaction capabilities, we refer to the interactive possibilities offered by a visualisation system. Specifically, we adopt the term allowable actions [4] to denote the range of interactions users can perform to modify the representation. They include:





basic_selection: the demarcation of an element either for the duration of the interaction (highlight) or more permanently until the occurrence of another selection (mark).




advanced_selection: the demarcation of an element triggers the demarcation of related instances within the same visualisation (connect) or lead to a brush and link effect across views. Basic selection is tacitly implied.




navigation: move interactions as zooming, panning, rotating, and scrolling, but only when applied to the visualisation and not to the web page; drill interactions to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original; and expand interactions, generating new perspectives on data by expanding and collapsing nodes.




arrangement: the organisation of visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially, either one by one e.g. via drag and drop (manual) or involving all elements at once by means of criteria-based triggers (automatic).




change: alteration of visual encoding involving the visualisation as a whole. A change can involve a shift in visualisation type (e.g. a bar chart transforming into a pie chart), a transition between visualisation-variants within the same type (e.g. an area chart transitioning into a stacked area chart), any attribute like glyph colour, size, shape, etc., or axes inversion and scale modifications, and other possible changes. Type changes generate a new unit, and therefore a group of related rows that are tracked by sharing the same project_id and vis_unit_id while introducing a visualisation_version identifier.




visualisation_filter: used to seamlessly exclude or include existing visualisation elements with respect to spatial, temporal, or other criteria, without reloading or generating a new visualisation.




collection_filter: visualisation elements act as a filter for a related collection or list of items (e.g. lists of items filtered by clicking on a map region). They can be related to spatial, temporal, or other criteria and dimensions.




aggregation: changes the granularity of visual elements according to spatial, temporal, or other criteria, producing either visual data summarisation or segregation into smaller instances.



 

References

[1] T. Battisti, «Classification of web-based Digital Humanities projects leveraging information visualisation techniques». Zenodo, nov. 20, 2024. doi: 10.5281/zenodo.14192758.

[2] J. S. Yi, Y. A. Kang, J. Stasko, and J. A. Jacko, ‘Toward a Deeper Understanding of the Role of Interaction in Information Visualization’, IEEE Trans. Visual. Comput. Graphics, vol. 13, no. 6, pp. 1224–1231, 2007, doi: 10.1109/TVCG.2007.70515.

[3] F. Windhager et al., “Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges,” IEEE Trans. Visual. Comput. Graphics, vol. 25, no. 6, pp. 2311–2330, Jun. 2019, doi: 10.1109/TVCG.2018.2830759.

[4] E. Dimara and C. Perin, “What is Interaction for Data Visualization?,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 119–129, Jan. 2020, doi: 10.1109/TVCG.2019.2934283.

[5] M. Brehmer and T. Munzner, “A Multi-Level Typology of Abstract Visualization Tasks,” IEEE Trans. Visual. Comput. Graphics, vol. 19, no. 12, pp. 2376–2385, 2013, doi: 10.1109/TVCG.2013.124.