DOI: 10.5281/zenodo.14192757
title: Classification of web-based Digital Humanities projects leveraging information visualisation techniques

description: Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation techniques. Each project has been classified according to visualisation techniques and interaction possibilities, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

 

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

 

Narrativity. It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:





non_narrative (boolean)




narrative (boolean)



 

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:





domain (categorical):






History and archaeology




Art and art history




Language and literature




Music and musicology




Multimedia and performing arts




Philosophy and religion




Other: both extra-list domains and cases of collections without a unique or specific thematic focus.




 

Visualisation of uncertainty and interpretation. Building upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise approaches explicitly represent quantifiable uncertainty, such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction (usually employing filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:





uncertainty_interpretation (categorical):






Interactive distinction




Visual distinction




Ambiguation




Interpretative metrics




 

Critical adaptation. We identify projects in which, with regard to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:





critical_adaptation (boolean)



 

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:





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




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




map (boolean): 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 (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.




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




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




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




bars (boolean): 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 (boolean): the display of information as sequential data points connected by straight-line segments.




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




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




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




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




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



 

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:





timeline (boolean): 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 [7], we also include structured solutions resembling Gantt chart layouts.







temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.




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




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



 

Interactions. A set of categories to assess affordable interactions based on the concept of user intent [8] and user-allowed perceptualisation data actions [9]. The following categories roughly match the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the aspect of the visualisation or the visual representation of its data points, symbols, and glyphs are taken into consideration. Columns:





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




advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.




navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view, but only when applied to the visualisation and not to the web page. It also includes “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 (boolean): the organisation of visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or according to a criterion via more automatic triggers.




change (boolean): visual encoding alterations involving different aspects of visualisation as a whole: the same content is presented with another visualisation technique; the change involves symbols or glyphs aspect (colour, size, shape, etc.); the visualisation type is unaltered, but the layout variant changes (e.g., to stacked layouts); or other changes like axes inversion and scale modifications. The presence of all the visualisation techniques involved in a change is reported.




visualisation_filter (boolean): filters to exclude or include visualisation elements with respect to defined criteria, without reloading or generating a new visualisation. Unlike options that trigger the fetch of new data to alter the visualisation content, filters seamlessly operate on existing visual elements.




collection_filter (boolean): the interaction with visualised elements acts as a filter for a related collection or list of items (e.g., clicking a region on a map filters a list of items according to spatial metadata).




aggregation (boolean): changes to the granularity of visual elements according to a variable. It produces either visual data summarisations or segregations.




btfw_interaction (boolean): to identify the use of “breaking the fourth wall interactions” as defined [11]. It applies only to narratives.



 

Narrative flow factors. Other categories aim to identify patterns in the design of narrative solutions. It is worth noticing that a project with multiple and diverse narratives can potentially report multiple design choices for the same column. Part of the factors and definitions from [12] are here reused and adapted.

Story layout columns define the layout, or genre, of the narrative format:





document_layout (boolean)




slideshow_layout (boolean)




hybrid_layout (boolean): mixing document and slideshow layouts.




other_layout (boolean): more complex solutions.



Role of visualisation columns describe the role visualisations play with respect to the entire story, in particular, with reference to the textual part of the narratives:



equal_role (boolean): visualisations and text play an equal role in the narrative.

figure_role (boolean): visualisations are supporting elements compared to the role of text.

annotated_role (boolean): visualisations are the drivers of the narrative.


Story progression columns categorise the shape of possible story paths:





linear_progression (categorical): strongly author-driven or user-directed narrative. Possible values specify the potential to skip certain parts while not having a fully explorative experience:






Skip




No-skip





user_directed (bool): users can select a path among multiple alternatives and compose narrative pieces, providing a broader degree of interaction and exploration possibilities [1]. If a linear path can be suggested, here it remains merely one option among many others. Different from a linear-skip approach, it has a low level of guidance oriented towards linear navigation.



Navigation input columns define the ways users can move through the narrative:





button_input (boolean)




scroll_input (boolean)




slider_input (boolean)



Navigation progress columns describe methods through which the reader perceives its placement within the narrative:





text_progression (boolean): text or numbers act as signifiers for user's position.




dots_progression (boolean)




visualisation_progression (boolean): the visualisation used in the narrative, or a visualised progress widget, acts as a signifier for user position.



Level of control columns describe how much control a reader has over the text, visualisations, and animated transitions. Control could be discrete (D) when it triggers the motion, continuous (C) when it can act throughout all the keyframes, or hybrid (H) if it supports aspects of both. When animation is absent, control cannot be available (NA). In particular, while visualisation control is related to the visualisation as a whole (e.g., the entire scatter plot moving up or down the page), the animated transition is related to more specific, data-relevant motion.Columns:





text_control (categorical):






D




C




H





visualisation_control (categorical):






D




C




H





animation_control (categorical):






D




C




H




NA




 

References

[1] E. Segel and J. Heer, “Narrative Visualization: Telling Stories with Data,” IEEE Trans. Visual. Comput. Graphics, vol. 16, no. 6, pp. 1139–1148, 2010, doi: 10.1109/TVCG.2010.179.

[2] M. Terras, J. Nyhan, and E. Vanhoutte, Defining Digital Humanities: A Reader. Routledge, 2016.

[3] S. Schreibman, R. G. Siemens, and J. Unsworth, Eds., A companion to digital humanities. in Blackwell companions to literature and culture, no. 26. Malden, MA: Blackwell Pub, 2004.

[4] C. Kinkeldey, A. M. MacEachren, and J. Schiewe, “How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies,” The Cartographic Journal, vol. 51, no. 4, pp. 372–386, 2014, doi: 10.1179/1743277414Y.0000000099.

[5] G. Panagiotidou, H. Lamqaddam, J. Poblome, K. Brosens, K. Verbert, and A. Vande Moere, “Communicating Uncertainty in Digital Humanities Visualization Research,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 635–645, Jan. 2023, doi: 10.1109/TVCG.2022.3209436.

[6] J. Drucker, “Humanities Approaches to Graphical Display,” Digital Humanities Quarterly, vol. 5, no. 1, 2011, Accessed: Sep. 17, 2024. [Online]. Available: https://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] Y. Shi, T. Gao, X. Jiao, and N. Cao, “Breaking the Fourth Wall of Data Stories Through Interaction,” IEEE Trans. Visual. Comput. Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209409.

[12] S. McKenna, N. Henry Riche, B. Lee, J. Boy, and M. Meyer, “Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences,” Computer Graphics Forum, vol. 36, no. 3, pp. 377–387, 2017, doi: 10.1111/cgf.13195.

 

Fundings

Project funded by the European Union – NextGenerationEU under the National Recovery and Resilience Plan (NRRP), Investment I.4.1 - Borse PNRR Patrimonio Culturale.