A fine-grained dataset of visualisation and interaction practices in web-based Digital Humanities projects

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.Description 本数据集对从186个基于网页的数字人文（Digital Humanities）项目中提取的587个可视化-交互单元（visualisation–interaction unit）进行分类，该数据集此前已在相关数据集[1]中完成标注，可实现跨数据集的交叉引用。每一行代表一个项目内可视化技术（例如地图、柱状图、网络图）与相关交互功能的独特组合。本数据集提供了更细粒度的设计选择视角，记录了可视化与交互功能的实现方式，包括其与叙事或非叙事语境的关联、时间编码，以及多视图或重配置策略。 本数据集的构成单元：可视化-交互单元的定义 可视化-交互单元是将一种可视化技术（或通过协同视图（coordinated views）关联的多种技术）与一组特定交互功能相结合的独特配置。参照[2]的研究，我们认为这些元素相互协同以达成共同的数据相关目标。这些单元构成了本数据集的基础分析层级，每一行对应一个单元。单元的区分不仅依据其可视化技术与支持的交互功能，还包括其时间特征与叙事语境。时间编码——例如时间轴、动画过渡或其他基于时间的变量——即使可视化与交互功能未发生变化，也会定义为一个新的单元。同理，同一配置若分别出现在叙事与非叙事语境中，则被计为两个独立单元，以反映其不同的意图与功能。因此，项目内的单元数量并不直接等同于其包含的可视化数量。若两个原本一致的图表在交互功能、时间编码或叙事语境上存在差异，则被视为不同的单元；而完全无差异的重复项则仅计为一个单元。例如，若某项目包含五个均支持点击下钻的柱状图，则仅计为一个单元。反之，若这五个柱状图各自具备不同的交互功能，则将根据其独特的可视化-交互组合视为独立单元。每个单元至少包含一种可视化技术，而交互功能则可有可无（例如静态图表）。 分类框架：类别与字段 标识符。数据集中共包含三个字段，用于唯一标识单元及其在项目内的关联关系： - project_id：单元所属项目的标识符。该字段复用了[1]中使用的递增ID，以实现跨数据集的交叉引用。 - vis_unit_id：单个可视化-交互单元的标识符。ID在每个项目内递增，新项目则重置为1。 - visualisation_version：用于追踪可视化的交互变换的标识符。若多行数据代表同一视图的不同状态（由用户交互触发可视化形式变更），则可共享相同的project_id与vis_unit_id。 叙事性。我们记录可视化-交互单元是否被应用于叙事语境。部分项目仅在叙事或非叙事场景中使用单元，而部分项目则同时包含两类语境。相关字段如下： - non_narrative：布尔值，指示该单元是否出现在非叙事语境中。 - narrative：布尔值，指示该单元是否被用于叙事语境（包括引导性强的作者驱动数据故事，以及更具交互性的用户驱动叙事）。 可视化技术。我们采用并在必要时调整了[3]中的术语与定义。每个字段对应一种特定的可视化类型，并通过布尔值指示该可视化技术是否存在于给定的可视化-交互单元中。本数据集采用以下字段与纳入标准对该信息进行编码： - plot：将数据点映射至二维坐标系的可视化表现形式。 - cluster_or_set：用于揭示对象间潜在相似性的集合或聚类可视化。 - map：用于展示空间洞察的地理地图。本数据集未限定地图的变体类型（例如标记地图、点密度地图、流向地图等），但存在例外：若地图中的每个数据点都由另一可视化形式表示（例如每个数据点为饼图的地图），则需同时在对应字段中标记两种可视化技术的存在。 - network：通过节点与连接边来凸显关系属性的可视化表现形式。 - hierarchical_diagram：树状结构，例如树图、径向树，也包括树状聚类图。与网络图的区别在于其严格的层级结构与无闭合连接环的特征。 - treemap：同样属于层级结构，但通过面积大小来量化表达数值，同时包括圆形包装（circle packing）变体。 - word_cloud：词云，其中每个词的大小与其在相关语境中的出现频率成正比。 - bars：包括柱状图、直方图及其变体。与[7]中的"bar charts"术语一致，但采用更通用的表述以指代所有基于条形的可视化。 - line_chart：以连续数据点通过直线段连接的形式展示信息。 - area_chart：与折线图类似，但在直线段下方填充区域，同时包括密度图。 - pie_chart：被划分为扇形的圆形图表，也可采用多层结构。 - plot_3d：利用第三维度编码额外变量的绘图。 - proportional_area：通过面积大小比较数值的表现形式，通常采用圆形或方形等形状。 - timeline：按时间顺序展示数据点或时间段的列表，包括带有时间刻度或仅按顺序展示事件的时间线。参照[3]的标准，本数据集同时纳入类似甘特图布局的结构化方案。 - other：涵盖所有未归入上述类别的其他非时序可视化。 时间编码。我们识别用于编码时间性的技术（时间线除外，其时间编码为默认隐含）。相关字段如下： - temporal_dimension：用于记录时间被映射至可视化的任意维度的情况。相较于[3]中的"轴"，我们采用"维度"一词，以更适配径向布局或更复杂的可视化选择。 - animation：通过随时间流变化的动画来感知时间性。 - visual_variable：采用其他视觉编码策略来表征任何与时间相关的变量（例如颜色）。 多类型协同视图。本数据集对协同视图的追踪仅限于在单个视图内可清晰识别多种可视化类型的场景。针对此类场景，设置了专属字段以指示哪种可视化（若存在）占据核心或主导地位： - primary_visualisation：包含在协同视图中占据主导地位的可视化技术名称（如对应字段中定义）。若无法确定单一主导类型（多种类型感知重要性相近），则该字段取值为"NA"。 交互与允许操作。本数据集基于用户意图[2]与用户允许操作[4]的概念，通过一组类别评估可视化支持的交互功能。以下类别大致匹配[5]中提出的操作方法子集。仅考虑影响可视化或其数据点、符号、标记的视觉表现的交互。本数据集通过两级分析实现交互分类：一级对应[1]中在项目聚合层面探索的交互类别，二级则为具体的交互能力（多个取值以分号分隔）。此处的交互能力指可视化系统提供的交互可能性，我们采用"允许操作"[4]一词，指代用户可执行的用于修改可视化表现的交互范围，具体包括： - basic_selection：对元素进行划定，可在交互期间持续（高亮）或保持永久状态直至再次选择（标记）。 - advanced_selection：对元素的划定会触发同一可视化内相关实例的划定（关联），或引发跨视图的刷选与联动效果。基础选择为默认隐含的前提。 - navigation：移动类交互，包括缩放、平移、旋转与滚动，但仅适用于可视化本身而非网页；下钻交互，用于在数据细节的不同层级或部分间导航，通常生成替换或伴随原视图的新视图；展开交互，通过折叠与展开节点生成数据的新视角。 - arrangement：对可视化元素（符号、标记等）或多可视化布局进行空间组织，可通过拖拽逐个手动调整，或基于触发条件一次性自动调整所有元素。 - change：对整体可视化的视觉编码进行变更。变更可包括可视化类型的转换（例如柱状图转为饼图）、同一类型内可视化变体的切换（例如面积图转为堆叠面积图）、任意属性（例如标记的颜色、大小、形状等）的修改，或坐标轴反转与比例尺调整等其他可能的变更。类型变更会生成新的单元，因此相关行数据可共享相同的project_id与vis_unit_id，并通过新增visualisation_version标识符进行追踪。 - visualisation_filter：用于基于空间、时间或其他标准无缝排除或包含现有可视化元素，无需重新加载或生成新的可视化。 - collection_filter：可视化元素作为过滤器，用于关联的集合或项目列表（例如点击地图区域过滤项目列表）。可基于空间、时间或其他标准与维度实现。 - aggregation：根据空间、时间或其他标准调整可视化元素的粒度，生成可视化数据的汇总或拆分为更小的实例。 参考文献 [1] T. Battisti，《基于信息可视化技术的网页数字人文项目分类》，Zenodo，2024年11月20日，DOI: 10.5281/zenodo.14192758。 [2] J. S. Yi, Y. A. Kang, J. Stasko, and J. A. Jacko，《深入理解交互在信息可视化中的作用》，IEEE Transactions on Visualization and Computer Graphics，卷13，第6期，第1224-1231页，2007年，DOI: 10.1109/TVCG.2007.70515。 [3] F. Windhager 等，《文化遗产馆藏数据可视化：现状与未来挑战》，IEEE Transactions on Visualization and Computer Graphics，卷25，第6期，第2311-2330页，2019年6月，DOI: 10.1109/TVCG.2018.2830759。 [4] E. Dimara and C. Perin，《数据可视化中的交互是什么？》，IEEE Transactions on Visualization and Computer Graphics，卷26，第1期，第119-129页，2020年1月，DOI: 10.1109/TVCG.2019.2934283。 [5] M. Brehmer and T. Munzner，《抽象可视化任务的多级分类法》，IEEE Transactions on Visualization and Computer Graphics，卷19，第12期，第2376-2385页，2013年，DOI: 10.1109/TVCG.2013.124。