Eurographics Workshop on Visual Computing for Biology and Medicine (2023)
C. Hansen, J. Procter, and R. G. Raidou (Editors)

Rapid Prototyping for Coordinated Views of Multi-scale Spatial
and Abstract Data: A Grammar-based Approach
Philipp Harth1 , Arco Bast2,3 , Jakob Troidl5 , Bjorge Meulemeester2,3 , Hanspeter Pfister5
Johanna Beyer5 , Marcel Oberlaender2,4 , Hans-Christian Hege1 , and Daniel Baum1,

1 Department of Visual and Data-Centric Computing, Zuse Institute Berlin (ZIB), Germany
2 Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
3 International Max Planck Research School for Brain and Behavior, Bonn, Germany
4 Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU Amsterdam, the Netherlands
5 School of Engineering & Applied Sciences, Harvard University, USA

Abstract
Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create
static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D)views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the
problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomicalviews. An important aspect of our web-based framework is that user interactions with data items at various levels of detail canbe systematically integrated and used to control the overall layout of the application workspace. With the help of a concise
JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables
rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate theusefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented
grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics,
it can also serve as a template for applications beyond biological research.
CCS Concepts• Human-centered computing → Visual analytics; • Software and its engineering → Specification languages; • Appliedcomputing → Biological networks;

1. Introduction
Visualization frameworks have greatly simplified the process of
supporting interactive visual analysis workflows in different domains [RCM∗ 20]. Desktop-bound applications and libraries, suchas Amira [SWH05], ParaView [par], and Inviwo [JSS∗ 20] provide powerful toolkits to create interactive visualizations without the need for reimplementing basic functionality, such as volume rendering. Parallel to this, a rich ecosystem of visualization
frameworks and libraries has developed in the web-based world,
for example, D3.js [BOH11], Vega(-Lite) [SWH14, SRHH16,
SMWH17], Babylon.js [bab], and Plotly.js [Plo]. In fact, low-levelvisualization frameworks, such as D3.js [BOH11], and high-level
frameworks, such as Vega-Lite [SMWH17], have become de facto
standards and have gained widespread adoption in the visualization
community as a result.
An emerging trend for specifying visualizations in such frameworks, particularly in web-based scenarios, are JSON-based visualization grammars, which can formally be seen as instancesof domain-specific languages (DSLs) [McN22]. The prime ex© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.
This is an open access article under the terms of the Creative Commons Attribution License, which
permits use, distribution and reproduction in any medium, provided the original work is properly
cited.

ample in this category of visualization frameworks is VegaLite [SMWH17], where both visual encodings of the displayed
data and interaction behavior [SRHH16] are defined in a JSONbased specification language. Because the grammar-based specification and resulting visualization can be easily changed, this is an
attractive approach for prototyping and iterative refinement of visual analysis tools.
However, there are three main shortcomings of current visualization grammars: (1) They are typically tied to a specific frameworkand cannot easily be mixed and matched at will. (2) They do notsupport 3D views and brushing & linking of combined 2D and 3D
views. (3) They are not flexible enough to support entire analysis
workflows where the user explores multiple scales.
When creating visual analysis tools for domain experts from neurobiological research, we face all of the aforementioned challenges.
Technological advances now make it possible to scan neural tissue
samples at nanoscopic resolution [KD18], allowing the study ofbrain networks in unprecedented detail. A recurring structural feature of neural networks found in such experimental studies is theirHarth et al. / Coordinated Views

multilevel organization from larger brain regions to individual neuronal cells with complex branching structures, down to nanoscopic
synaptic contacts between neurons [FZB16]. Integrated visual analysis of such data [BTB∗ 22] requires the coordination of different
types of views, including both information views and 3D anatomical views, in configurations that are adjusted to the level of detailrequired for the respective analysis task.
In addition to the multilevel organization of brain networks, afurther challenge is the visual analysis of simulated neural activity data [BO22], which adds a temporal dimension to the neuroanatomical data under study. Neural activity simulations performed by our domain experts are controlled by more than 30
biophysical parameters and generate hundreds of spatio-temporalfeatures that describe the neural activity response of the simulated
neurons [BO22]. Thus, we are dealing with high-dimensional datathat domain experts would like to analyze visually – both with abstract representations suitable for high-dimensional parameter analysis [SHB∗ 14] (such as parallel coordinates or 2D embeddings using dimensionality reduction techniques [SZS∗ 17]) and 3D representations of neuroanatomy.
Contributions. The novelty of the proposed interaction grammar
is that it allows the integration of existing visualization frameworks, namely Vega(-Lite) [SWH14, SRHH16, SMWH17], which
are themselves internally configured through a JSON-based specification, with regular visualization libraries, such as Plotly.js [Plo]or Babylon.js [bab]. Importantly, this integration also includes the
combination of standard information views with 3D (anatomical)
views. To the best of our knowledge, doing so with a grammarbased approach (as opposed to manually programmed event andinteraction handling) has not been addressed so far. Unlike previous grammar-based approaches, the presented interaction grammar
also controls the overall layout of the application workspace. Finally, grammar-based approaches have not been employed for visual analysis in neuroscience.
2. Related WorkMultiple coordinated views. Multiple coordinated views are acommon tool to offer different perspectives on data. For example,
brushing and linking [DGH03, Dol04] can be used to create associations between multiple visualizations, representing different attributes of multi-faceted data [KH13]. Roberts [Rob07] surveyedvisualization approaches using multiple coordinated views, whichhave since then become a standard technique in visualization systems and dashboard design across all domains. Streamlit [Str] andDash [Das] are examples of existing frameworks for building data
analytic dashboards in which different types of views can be combined; however, they do not use a JSON-based specification.
Visualization grammars. In a recent survey, McNutt [McN22]
formalizes the design space of JSON-style domain-specific languages (DSLs) for visualization definition. DSLs have been applied in the biomedical domain [LWLG22], including visual analysis of neurobiological data [TCG∗ 22]. Troidl et al. [TCG∗ 22] focused primarily on spatial neighborhood analysis and comparisonof spatial brain structures but did not explicitly link multiple views
through individually defined interactions. Domain-agnostic visualization grammars [SWH14, SRHH16, SMWH17, ZPWS22] often

model interactions by modeling data selections as a subset of thedata that users want to manipulate. More recently, visualization
grammars specifically for 3D data have been introduced [Sch21],
which do not require users to be familiar with the intricate implementation details of 3D rendering systems, but rather allow specifying certain rendering and interaction parameters through JSON.
Our grammar specification unifies these previous approaches bycombining (1) visualization specification, (2) view layout definition, and (3) interaction definition for both information and 3D visualizations.

Multi-scale visualization. When analyzing measured and computed high-resolution data from neurobiology, experts must beable to examine detailed features of individual simulation runs,
such as neuron morphology, synapse types, and activation potentials. On the other hand, larger-scale distributions and statistical
patterns are of great importance to put small-scale findings into
context. Therefore, multi-scale visualization is a necessary tool
to navigate between the different scales of such highly complex
datasets. Cakmak et al. [CJS∗ 22] surveyed the state of the art
in multi-scale visualization. A typology of abstract visualization
tasks that also applies to multi-scale analysis scenarios is provided by Brehmer and Munzner [BM13]. An important aspectof multi-scale visualizations are data summarization techniques,
which have been reviewed in [SGS18]. For example, previous interactive tools [AABS∗ 14, MAAB∗ 17, TCG∗ 22] have used multiple levels of detail to analyze neuronal tissue. By increasing theinformation density and leveraging data abstraction, visual representations can be continuously transformed from analyzing a single object to effectively comparing larger groups of objects. A recent application example for multilevel analysis/semantic zoomin the domain of genomics is the grammar-based toolkit called
Gosling [LWLG22].

Visualization for neuroscience. Beyer et al. [BTB∗ 22] survey thelandscape of visualization approaches for high-resolution imagebased neuroscience. Neuron simulation models can be augmented
by visual analysis to better understand the effect of intricate parameter configurations on simulation outcomes. VIOLA [SCH∗ 18]
is an interactive visualization tool that uses multiple coordinatedviews for exploratory analysis and quality assessment of spatiotemporal features of a neuron simulation model. In contrast,
Nowke et al. [NDPW∗ 18] propose an interactive visual approachto study parameter configurations of connectivity generation models. VisNEST [NSA∗ 13] allows visual exploration of simulations
that consider both the low-level neuronal activity of individual neurons and the general interplay between multiple brain regions. NeuroVIISAS enables the integration of different neuro-ontologies,
imaging data, and neural activity simulation data in one framework [SE12]; it also facilitates comparative analyses of connectome data [SJES19]. Open Source Brain [GCM∗ 19] is a web-based
repository for neural network models with comprehensive visualization capabilities. Other approaches focus entirely on neuronalconnectivity analysis without considering activity simulations (e.g.,[TWC∗ 22, HVU∗ 22, BSG∗ 09]).
© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

Harth et al. / Coordinated Views{"views" : [{"name" : <view_name>,"type" : <type_name>,"dataTable" : <table_name>,"dataColumn" : [<column_name>, ...],"configuration" : {...},"minNumDataColumns" : int,
"maxNumDataColumns" : int}],"grid" : { // partitions workspace"cols" : int, "rowHeight": int, "width" : int},"layouts" : {<layout_name> : {"view" : <view_name>,"x": int, "y": int, "w": int, "h": int}}"initialLayout" : <layout_name>"interactions" : {<source_view_name> : [{ // source of interaction event"filter" : {"currentLayout" : <layout_name>,"interactionType" : <interaction_type>"selectedEntityType" : <type_name>"numSelectedRange" : int | [int, int],}"action" : {"assignData" : [<target_view_name>, ...],"clearData" : [<target_view_name>, ...],"assignSelectionAsData" : [<target_view_name>, ...],"assignSelection" : [<target_view_name>, ...],"intersectSelection" : [<target_view_name>, ...],"unionSelection" : [<target_view_name>, ...],"clearSelection" : [<target_view_name>, ...],"changeLayout" : <layout_name>
},
}]
}
}
<interaction_type> = "select" | "deselect" | <custom_event_name>

Listing 1: JSON structure of the proposed interaction grammar
for coordinating heterogeneous views. All <*_name>-fields are
string-valued. In the default scenario, the selectedEntityType is“row_index”, and the selection represents a set of rows in the underlying dataTable (see Supplementary Information).

3. Requirements and Design Goals
In this paper, we deal with multi-scale neuroanatomical data andhigh-dimensional neural activity simulation data (i.e., biophysicalmodel parameters and features describing the neural activity response of the simulated cells). In order to support the analysis questions of our domain experts, we developed an interaction grammar(Sect. 4) that supports easy and fast prototyping for coordinatedviews of multi-scale and abstract data. For a detailed descriptionof the associated data and analysis tasks, we refer to the respective case studies in Sects. 6.1 and 6.2. To clarify the requirements,
we had regular virtual meetings with our collaborators over several weeks in which we discussed their needs and performed interviews. Through these meetings, we iteratively defined and refined
the requirements and design goals of the interaction grammar. This
resulted in the following requirements:© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

R1: The analysis workflows of our domain experts require multiple coordinated views [Rob07] that include information views (e.g.,
scatter plots or line charts) and 3D anatomical views.
R2: To be of practical use in neuroscientific research and manyother application fields, the visual analysis tools should be available
on short notice and be easily changeable as the domain expert’s
insights and research questions evolve.
R3: The multi-scale organization of neuroanatomical entities requires that the application supports multiple and highly heterogeneous combinations of views, depending on the scale of analysis
and the specific research questions.
These requirements translate into the following design goals thatguided both the specification of the interaction grammar (Sect. 4)
and the implementation of the tool (Sect. 5):
G1: Support the combination of different view types (R1, R3).
G2: Integrate existing visualization frameworks and libraries to facilitate code reuse and reduce development time (R2).
G3: Enable interactive and iterative refinement (i.e., rapid prototyping) of views, workspace layout, and interaction logic (R2).
G4: Avoid hard-coded or domain-specific ontologies tied to a priori known analysis questions or workflows (R2, R3).
G5: Support dynamic transitions between different workspace
configurations (R3).
4. Interaction Grammar
To enable rapid prototyping and iterative refinement of thetool (G3), we chose a grammar-based approach to control
the overall workspace layout and interaction behavior between
views (Fig. 1A,B). The syntax specification of the resulting JSONbased grammar is given in Listing 1. We describe the different components of the grammar in the following sections.
4.1. Specifying Views
The views property in the grammar allows defining a set of named
views that can have different types. Views have the following attributes in the grammar:• name: User-selected name for the view.
• type: Type specifier describing what the view represents and withwhich library it is implemented. For example, one view may beof type vega [SMWH17] representing a line chart. Another one
may be of type babylon [bab] representing a 3D view (G1, G2).
• dataTable: Each view is associated with a data source that maps
to a csv file provided to the data server in the backend (seeSect. 5).
• dataColumn: Views can reference selected columns in the datatable.
• configuration (optional): Additional parameters that can be supplied to control the exact representations of the views (e.g., color
or mark type); which parameters can be specified depends on thetype/visualization library of the view.
• min/maxNumDataColumns: Number of data columns that are required/can be specified for the view type (needed for the UI editor and consistency checks).
An example of the view specification section of the grammar isshown in Fig. 1D.

Harth et al. / Coordinated Views

Figure 1: Technical outline and components of the user interface. A: JSON specification of interaction grammar. B: Interaction grammar
controls overall workspace layout and event propagation between views. C: Views from different existing libraries can be integrated by implementing a thin wrapper class that interfaces with our framework (see Sect. 5.3). D: View specification panel in the menu area. E: Interaction
grammar panel (left) and workspace (right).

4.2. Specifying Multi-Scale Analysis Layouts
The layout of the workspace area is also controlled by the grammar
and can dynamically change (Fig. 1B) to support multi-scale/multilevel analysis workflows that require different types and arrangements of views (G5). The named layouts are specified using thefollowing properties of the grammar:• grid: Specifies how the workspace area is partitioned into
equally sized grid cells, according to React-Grid-Layout [rea].
• layouts: A set of named layouts. Every layout references one or
multiple views (view) by their name and specifies which gridcells the view should occupy on the workspace (x, y, w, h).
For example, {x: 0, y: 0, w: 5, h: 4} positions the view on the
top left corner of the workspace and makes it occupy 5 gridcells horizontally and 4 vertically. For details of the layouting
scheme (e.g., how overlaps are resolved), refer to React-GridLayout [rea].
• initialLayout: Name of the workspace layout that is shown when
the application is started.

4.3. Specifying Interactions and Linking between Views
The interactions property in the grammar defines how interaction
events in one named source view are propagated to the specified
target views and how they may alter the workspace layout. Interaction event-triggered actions can depend on the following optional
filter conditions:• currentLayout: Only propagate event when the application iscurrently in the specified workspace layout state.
• interactionType: "select", "deselect", and any named event provided by one of the views.
• selectedEntityType: Type of data item(s) that was/were selectedby the user. We chose to keep these entity types generic and do
not introduce neuroscience terminology into the grammar (G4).
• numSelectedRange: How many data items were selected by theuser? For example, this condition can be used to show detailviews or change the layout of the workspace when one or a small
number of entities in a large collection are selected (G5).
The operations under the action property define what happens if thefilter condition is met. In our concept, every view v is associatedwith a set of data items D and a subset of data items S that are© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

Harth et al. / Coordinated Views

5.2. Front- and Backend(locally) selected in the view.
view = (S, D)S⊆DNote that D and S may be empty. On specified interaction events,
Dsource and Ssource from the view in which the event originated arepropagated to the named target views as follows:
specified action
assignData
clearDataassignSelectionAsData
assignSelection
intersectSelection
unionSelection
clearSelection

operation in named target views
Dtarget ← Dsource
Dtarget ← ∅Dtarget ← Ssource
Starget ← Ssource
Starget ← Starget ∩ Ssource
Starget ← Starget ∪ Ssource
Starget ← ∅If the changeLayout action is specified, the application workspace
will be dynamically changed as a result of the interaction (G5).
In conclusion, our grammar depends on data item selections,
how and under what conditions they are propagated between views,
and how they affect the workspace layout.

We wanted to integrate the functionalities of Vega-Lite [SMWH17]
for information views into our tool. To realize the case studies in Sects. 6.1 and 6.2, we additionally used the following
visualization libraries: Babylon.js [bab] for WebGL-enabled 3D
views, Plotly.js [Plo] for parallel coordinates views, and ReglScatterplot [Lek23] for WebGL-enabled scatterplots with large
numbers of points. Our application was built using the Meteor [met] web-development framework in combination with React. The grid-based layout of the application workspace is based on
React-Grid-Layout [rea].
In the backend, we run a Python server, which reads all csv files
in a designated folder. Mapping of these table-based data records to
the respective views can be specified by the user of the tool in theview specification panel (see Sect. 1D). Other resources in JSON
format (e.g., the neuron morphology shown in the anatomical viewin Fig. 3A are read from a designated resource folder and supplied
to the client by the backend server. Time-resolved neural activity
simulations for selected biophysical parameters (as shown in Fig. 5)
are computed on-demand on a separate data server (Python) thatruns a custom-implemented neural activity simulator [BO22].

5. Implementation
To offer platform-agnostic access to the tool and to take advantageof the robust capabilities of already-existing web-based visualization frameworks (G2, G3), we decided to develop an online tool.
We describe details of the implementation in the following sections.
5.1. Components of User Interface
The main UI components of the framework are depicted in Fig. 1B;
they consist of the menu area on the left with multiple control panels and the main application workspace on the right. The configuration of views in the workspace is entirely controlled by the interaction grammar, which modifies its configuration based on theuser-specified interactions. The menu area includes the following
panels:• Session panel. It provides standard project management settings
to load and save the state of the analysis session.
• View specification panel (shown in Fig. 1D). It defines theviews that can be referenced from the grammar and specifies towhich data sources they refer. The panel serves as GUI editor tomodify the “views” section of the interaction grammar. The UI
can be disabled to edit the JSON specification directly. An example of the underlying JSON specification is shown in Fig. 1D.
• Interaction grammar panel (shown in Fig. 1E). It provides
a code editor to modify the interaction grammar directly fromwithin the application (G3).
The workspace is partitioned into equally sized grid cells as specified in the “grid” section of the grammar (Listing 1). Views arethen assigned to occupy one or multiple of these grid cells, as
specified in the “layout” section of the grammar (Sect. 4), whichallows defining multiple named layout configurations. The structure and contents of the workspace, therefore, change dynamically(Fig. 1B), depending on user interactions and the layout configuration that is automatically activated.
© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

5.3. Extensibility and Code Availability
To add additional views in the front end, a wrapper class that is derived from CoordinatedView needs to be created by the developer(Fig. 1C). The wrapper class connects the events from our framework to the JavaScript library in which the view is implemented.
Several example implementations of the wrapper class are provided
in the github repository (see below). For data requests that refer not
to table-based (i.e., csv) or JSON-based records, the backend server
has to be modified, or a separate data server needs to be created (aswe did for the on-demand computations of neural activity simulations).
The code is available at https://github.com/zibneuro/
coordinated-views.
6. Evaluation
We demonstrate the utility of the proposed interaction grammar
for the rapid prototyping of coordinated views using two casestudies from ongoing neuroscientific research. The domain scientists are two Ph.D. students (first-year physics and fifth-year neuroscience/medicine) and their group leader/principal investigatorfrom the MPI for Neurobiology of Behavior – caesar; all three arecoauthors of this paper. The domain experts used the tool under
our guidance in four virtual sessions (using screen sharing and remote control of mouse and keyboard) in which we asked them tocomment on their interactions and share their thoughts (think-aloudmethod [LBI∗ 12]). The case studies described below are based on
the protocols of these sessions.
6.1. Case Study 1: Validating Anatomical Models
The domain experts in our group created detailed anatomical models of specific brain regions to perform biophysical simulations ofHarth et al. / Coordinated Views

Figure 2: Workspace as used in the first case study (Sect. 6.1). A: Parallel coordinates view of presynaptic neuron properties using Plotly.js.
B: Breakdown of selected neurons by cell type using Vega-Lite; most presynaptic cells in the current selection are layer-3/4 neurons (redarrow). C: xy-projection of neuron soma locations (colored by cortical column) using a WebGL-enabled scatter plot. D: xz-projection ofsoma locations. E: 3D view of presynaptic neurons (red) and dendrite morphology of postsynaptic neuron (blue) using Babylon.js. Workspacelayout and brushing & linking of views are controlled by the interaction grammar.

individual neurons and their neighbors [BO22, ENG∗ 20]. A brain
region of particular interest to them is the somatosensory cortex inrat, which processes sensory inputs from the whiskers in the snoutof the animal [UHM∗ 22]. In one realization of this anatomical
model, the following anatomical structures are found (from large
to small):naptic populations of point-like neurons to the postsynaptic fullcompartmental neuron and examine to which degree these model
parameters comply with known empirical knowledge.
T3: Group synapses by the cell type of the incoming connections.
This involves inspecting synaptic connections in 3D on the dendrite
morphology of the simulated postsynaptic neuron.

1. 24 cortical columns: subregions that partition the modeled brain
region,2. 32,587 presynaptic neurons (treated as point-like objects) whosecell bodies are located in these columns,
3. the detailed 3D dendrite morphology of a selected postsynaptic
neuron, and
4. 42,853 synaptic contacts from the presynaptic neurons to dendrites of the postsynaptic neuron.

The domain experts in our group suggested showing the coordinates of the cell bodies (soma_x, soma_y, soma_z) in the model
reference frame and their distance to the soma of the postsynaptic
neuron (intersomatic_distance), for which the users created a parallel coordinates view (Fig. 2A). A subpopulation of presynaptic neurons became immediately apparent, which is located deeper than
all other cells in the model (negative z-coordinate) and, in consequence, has a higher intersomatic distance to the postsynaptic neuron. We added a bar chart to the workspace (Fig. 2B) that groups allneurons by their cell type (T1). The interaction grammar was modified accordingly to sync the selection in the parallel coordinatesview to the bar chart. It then became clear that this subpopulationof cells comprises of VPM (ventral posteromedial nucleus) neurons
located in the Thalamus, while the other group of neurons resides in
the cortex. This was confirmed when adding an xz-projection viewof the soma coordinates (Fig. 2D).

A recurring nontrivial task that cannot be easily automated is thevalidation of the rich structural features in these digitally generated
anatomical models and the identification of potential limitations of
the model. This validation step, which is performed visually andincludes the tasks enumerated below, is a prerequisite for ensuring that subsequent biophysical simulations described in the secondcase study (Sect. 6.2) are realistic and interpretable.
T1: Characterize cell populations of presynaptic neurons that connect to one postsynaptic neuron, which is to be simulated as afull-compartmental model (i.e., with spatially resolved morphology at subcellular resolution). This requires understanding where
the presynaptic neurons (which are modeled as point-like objects)
are located and how they are divided by cell type.
T2: Inspect the average connectivity statistics from the presy-

The density of presynaptic cells in the xz-projection view ishighly heterogeneous and differs greatly between cortical layers(i.e., along the z-coordinate). After selecting the upper and lower
layers in the projection view using lasso-selection and inspecting
the respective breakdown of the selection by cell type, the experts commented that these non-uniform distributions of presynap© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

Harth et al. / Coordinated Views

originate mostly from layer-6 neurons (as would be expected dueto their depth along the z-axis), but remarkably also from layer3/4 neurons (Fig. S2), which can be attributed to their verticallyextended morphologies (T2, T3).
In conclusion, visual examination of this anatomical model with
our tool verified that morphological restrictions and cellular connectivity metrics are in line with empirical knowledge about structural connectivity in this brain region [UHM∗ 22]. Visual analysis
also uncovered the remaining limitations of the model in termsof intricate subcellular wiring preferences, such as the targeted
synapse formation of VPM neurons at the bifurcation zone of thedendrite [GBNO21].
To create an integrated analysis workflow, we can combine both
workspaces, for example, by specifying that the detail workspacewith synaptic connectivity information opens when the user clicks
on the dendrite morphology in the overview workspace (see Supplementary Information, Listing S3). We could also specify a single
workspace in which both levels of detail are combined (see Supplementary Information, Fig. S3).
6.2. Case study 2: Analyzing Biophysical Simulations
Figure 3: Workspace for detailed view of synapses on the dendrite.
A: Selected synapses near the bifurcation zone of a dendrite. B: xzprojection of synapses with selection. C: Breakdown of selectedsynapses by cell type of presynaptic neuron. Connections from VPM
neurons are likely underestimated in this model (red arrow).

tic cells reflect the horizontally extended morphologies of layer2 and layer-6 neurons [NEJ∗ 15]. The model therefore adequately
captures the complexity of these empirically established connectivity patterns (T2). The experts also observed that in the corticalcolumn, in which the postsynaptic neuron is located, the densityof presynaptic cells is much higher than in adjacent areas (along
the x-direction). To gain a better spatial understanding of the density distribution of presynaptic neurons (T1), we rearranged theworkspace layout using the interaction grammar editor and added
an xy-projection view (Fig. 2C) and a 3D view that also show
the morphology of the postsynaptic neuron (Fig. 2E). The experts
lasso-selected the central column in the xy-projection view, confirming that the postsynaptic neuron is mainly targeted by layer-3/4
neurons from the same cortical column (Fig. 2B) and that the proportion of layer-6 neurons [NEJ∗ 15] increases when more remotecolumns are selected (Fig. S1; T2).
The experts were interested in the locations on the dendrite
where the neurons establish synaptic connections. For this purpose, we created a second workspace layout showing a detailed 3D
anatomical view of synaptic contacts on the dendrite (T3), together
with an xz-projection view of the synapse locations and breakdown of the incoming synaptic connections by cell type (Fig. 3).
The experts selected the synapses near the bifurcation zone of thedendrite (Fig. 3A, B), noting that targeted synaptic contacts from
VPM neurons that had been observed in latest experimental findings [GBNO21] are likely underrepresented (Fig. 3C) in the current
version of the model (T2, T3). When synapses located near thesoma were selected, the cell types view (Fig. 3C) showed that they© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

The goal of the domain experts in their biophysical simulations is tounderstand the relationship between the expression of ion channels,
which control the membrane potential on the surface of the dendrite, and the energy efficiency of the computations that the neuron
performs [BO22].
The simulations result in high-dimensional data sets. In thiscase study, we consider a simulation experiment in which n =15, 000, 000 simulation runs were performed, of which we used
n = 10, 000 as an example in the evaluation, where each run was
associated with
1. sampled simulation parameters (dim = 35) that result in observable functional features of the cell (dim = 345). The simulation
parameters control ion channel expressions and the functionalfeatures describe how much current flows through the ion channels (i.e., ion channel utilization) as well as characteristics of themembrane potentials; collectively, we refer to these quantities as
scalar features (dim = 380).
2. spatiotemporal data: membrane potentials and ion channel activity mapped to the surface of the dendrite morphology for every
time step in the simulation.
Interpreting these simulation data requires performing the following analysis tasks:
T1: Understand the relationship between ion channel expressions
and ion channel utilizations.
T2: Identify features that have descriptive value for energyefficient computation from the large set of available features.
T3: Inspect the spatially and time-resolved neural activity on thedendrite morphology and compare it for different simulation parameters.
We first aimed to investigate the relationship (T1) between ion
channel expression and the response of the cell to current stimuli, which is a functional property of the cell that arises from the
dynamic activation of these ion channels. To this end, the experts

Harth et al. / Coordinated Views

total charge exchanged and the expression of Ca_HVA (T2). Additionally, the energy-efficient models also had a low expression of
Kv3.1. Thus, in contrast to the initial result indicating a weak link
between functional and structural features, we predicted a tighter
relationship for energy-efficient models (T1,T2). We, therefore,color-coded the UMAP [MHM20] embedding by our energy metricand found that indeed both the embeddings of the structural and thefunctional features are organized by this energy efficiency feature(Fig. 4B). We then repeated the lasso selection, this time specifically selecting those models that were the most energy-efficient.
Contrary to the first result, selecting energy-efficient models in thechannel utilization embedding led to a well-constrained selectionof models in the channel expression embedding (Fig. 4C). This indicates a tight link between channel expression and utilization forthis subset of models (T1, T2).

Figure 4: A-C: Subsequent steps in the analysis of simulation parameters (refer to Sect. 6.2 for details) using linked views of UMAP
embeddings [MHM20] of ion channel expressions (left column),
ion channel utilizations (center column), and two correlated features (right column). The coloring encodes the energy efficiency ofdendritic computations where bright yellow colors indicate highenergy efficiency.

To compare energy-efficient and inefficient samples, we examined the spatially and time-resolved neuronal activity of suchmodels on the dendritic morphology (T3). We specify in the interaction grammar that the simulation panel workspace layout is
opened (Fig. 5) when single samples are selected in the scatterplot. Comparing the time-resolved membrane potential of energy
efficient and energy inefficient models at different dendrite locations (Fig. 5C) revealed significant differences in the shape of theapical action potential decay (Fig. 5D, blue line), which can guide
further investigation and definition of more descriptive scalar features (T3).

7. Discussion & Conclusionsuggested to create 2D embeddings using UMAP [MHM20] forthe channel expression parameters (Fig. 4, left) and seven channel utilization features (Fig. 4, middle) that describe ion channel
utilization near the bifurcation zone of the dendrite (the region selected in Fig. 3A, B). Additionally, we selected these features ina parallel coordinates view, which we link to the 2D embeddingsof both feature sets for brushing and linking. To test if there isa close correspondence between simulation parameters and functional features (T1), we lasso-selected a small region in the channel utilization space. We found this to result in a wide spread in
the UMAP embedding [MHM20] of channel expression parameters (Fig. 5A). Similarly, lasso-selection of a small region in thechannel expression embedding leads to a wide spread in the channel utilization embedding. This indicates that channel expression
and channel utilization are generally weakly linked. This weak link
was further illustrated by selecting narrow ranges of features in theparallel coordinates view that did not constrain the ranges of otherfeatures (T1).
We found a notable exception to this: The expression of thecalcium channel (feature *.Ca_HVA) in the apical dendrites appeared to restrict the amount of total charge exchanged (feature BAC_dist_charge) during dendritic calcium action potentials (Fig. S4). It also limited the expression of fast, non-inactivating potassium conductances in the apical dendrite (feature*.SKv3_1.gSKv3_1bar). Therefore, we plotted the first two features against each other in a scatter plot and color-coded the third(Fig. 4, right). Indeed, we found a close correlation between the

Our grammar-based framework for rapid prototyping of coordinated views seamlessly integrates heterogeneous types of webbased views from different visualization frameworks and libraries.
A key design goal of our study was to link regular information
views with 3D (anatomical) views (G1) using a grammar-based
specification of the interaction behavior. Previously, implementing
the interaction between views in this particular scenario would haverequired complex event and callback structures in the code. Our approach eliminates this error-prone programming task by providing
the interaction grammar as an additional layer of abstraction.
The views used in the presented case studies (Sects. 6.1 and 6.2)
consist of information views based on Vega-Lite [SMWH17], aninteractive parallel coordinate view from Plotly.js [Plo] for analysis of high-dimensional simulation data [BO22], WebGL-enabled
scatter plots supporting display of large numbers of points [Lek23],
and 3D anatomical views based on Babylon.js [bab]. However, ourframework is not limited to the aforementioned visualization libraries and view types. To integrate a new view from another library, developers need to implement a thin wrapper class that connects the attributes and events of the new view with the messaging
interfaces of our framework (see Sect. 5.3, Fig. 1C). This facilitates
the reuse of code and the integration of the best available visualization modules from different libraries for the respective tasks (G2).
Our work follows a current trend of using JSON-based visualization grammars [McN22]. A key advantage of such grammar-based
specifications is that they allow for rapid prototyping of visual analysis tools (G3). We fully leverage this advantage by providing a© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

Harth et al. / Coordinated Views

Figure 5: Configuration of the workspace for comparing biophysical simulations. A: Two selected parameter samples (associated withhigh and low energy efficiency for dendritic computation). B: Simulation control panel (slider changes time step). C: Simulated membranepotential on the dendrites for both parameter samples; virtual probes (colored pins) have been placed on the dendrite to record the membranepotential at specific locations. D: Time-resolved membrane potentials at the probe sites. The probe near the bifurcation zone (blue) shows adifference between both parameter samples in the decay of the membrane potential.

grammar editor (Fig. 1E) that allows changing the view configurations and interaction behavior live in the application.
The specification of our grammar (Listing 1) is agnostic to specific ontologies from the application domain (G4). This makes it
usable for all conceivable use cases where coordinated views (evenfrom different libraries) are to be used. Furthermore, no adaptations are necessary if the application-related semantics change. The
JSON-based specifications are stored in a file on the backend, allowing to version-control the respective workspaces.
Our grammar allows us to specify dynamic layout changes of thewhole application workspace (G5), enabling, e.g., complex analysis workflows from overview to detail [Shn96] that require different arrangements of views. In the second case study (Sect. 6.2),
e.g., the tool was used to analyze neuroanatomy jointly with simulated neural activity. This demonstrates how the dynamic grammarbased layout of views and propagation of user selections facilitate amulti-staged filter process to explore the high-dimensional space of
simulation parameters in a drill-down manner. Hence, our tool supports aggregating, projecting, and filtering data [SGS18]. However,support for non-expert users in specifying the respective views andinteractions still needs to be improved.
The decision to use a web-based tool enabled the rapid development of the interaction grammar and the accompanying tool. On
the other hand, web-based techniques still face a problem whenhandling large amounts of data. For example, it is not possible todisplay all 15 million simulation samples from case study 2 (instead, we used a representative subset of 10, 000 simulation runs).
However, we believe that this limitation will become less severe
with further advances in web-based technology. To address thesescalability issues from the backend side, we are currently integrat© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

ing a Python library (vaex [BV18]) for big data handling that allows
fast computation of aggregate representations of the data, such as
2D density plots of selected attributes.
Another limitation is that the grammar-based application might
not be accessible to all domain experts since it requires some technical knowledge. To lower this burden, it would be desirable to haveconsistent local specification grammars in the “configuration” attribute of the views. A first step in this direction could be to provide
a more generic 3D viewer that can be configured using a syntax thatis similar to that of Vega(-Lite) [SWH14, SRHH16, SMWH17].
Future work will also include provenance tracking of the application with options to jump back to previous interaction states.
There also exist highly specialized web-based tools for structuralconnectivity analysis (e.g., Vimo for connectivity motif analysis [TWC∗ 22]) that could be integrated as views into our framework. It would also be desirable to be able to load graph data in
addition to tabular datasets. A key aspect for improving the usability of the tool would be to provide debugging help for the JSON
specification when it fails during runtime. Additional usability improvements include supporting regular expressions (e.g., “scatterplot*” instead of “scatterplot1” and “scatterplot2”) to reference theviews in the grammar, which would make the specification moreconcise.
In summary, we demonstrated in two case studies from neurobiology that the proposed grammar-based approach is feasible anduseful in real-world visual analysis scenarios that require the integration of heterogeneous web-based views and iterative refinementof the visual analysis tool together with domain experts. However,since the logic of the proposed interaction grammar is generic (independent of neuroscience specifics), our tool can serve as a tem-Harth et al. / Coordinated Views

plate for developing visual analysis applications in other domainsthat require the coordination of information views and 3D views
and/or interaction-driven changes to the overall workspace layout.

8. Acknowledgements
This work was supported by Deutsche Forschungsgemeinschaft
grant SPP 2041 Computational Connectomics (D. B. and M. O.),the Max Planck Institute for Neurobiology of Behavior – caesar (M. O.), European Research Council grant 101069192 (M. O.),
Neuroscience Network North Rhine-Westphalia grant iBehave(M. O.), and by the NSF grants NCS-FO-2124179, and IIS1901030 (H. P.).

References[AABS∗ 14] A L -AWAMI A. K., B EYER J., S TROBELT H., K ASTHURI
N., L ICHTMAN J. W., P FISTER H., H ADWIGER M.: Neurolines:
a subway map metaphor for visualizing nanoscale neuronal connectivity. IEEE Trans. Vis. Comput. Graph. 20, 12 (2014), 2369–2378.
doi:10.1109/TVCG.2014.2346312. 2[bab] Babylon.js. URL: https://www.babylonjs.com/. 1, 2, 3,
5, 8[BM13] B REHMER M., M UNZNER T.: A multi-level typology of abstract visualization tasks. IEEE Trans. Vis. Comput. Graph. 19 (12 2013),
2376–2385. doi:10.1109/TVCG.2013.124. 2[BO22] BAST A., O BERLAENDER M.: Ion channel distributions in cortical neurons are optimized for energy-efficient active dendritic computations. bioRxiv (1 2022), 2021.12.11.472235. doi:10.1101/2021.
12.11.472235. 2, 5, 6, 7, 8
[BOH11] B OSTOCK M., O GIEVETSKY V., H EER J.: D3 data-driven
documents. IEEE Trans. Vis. Comput. Graph. 17 (2011), 2301–2309.
doi:10.1109/TVCG.2011.185. 1[BSG∗ 09] B RUCKNER S., S OLTESZOVA V., G RÖLLER M. E.,
H LAD ŮVKA J., B ÜHLER K., Y U J. Y., D ICKSON B. J.: Braingazer- visual queries for neurobiology research. IEEE Trans. Vis. Comput.
Graph. (2009). doi:10.1109/TVCG.2009.121. 2[ENG∗ 20] E GGER R., NARAYANAN R. T., G UEST J. M., BAST A.,U DVARY D., M ESSORE L. F., DAS S., DE KOCK C. P., O BERLAEN DER M.: Cortical output is gated by horizontally projecting neurons in
the deep layers. Neuron 105 (1 2020), 122–137.e8. doi:10.1016/
j.neuron.2019.10.011. 6[FZB16] F ORNITO A., Z ALESKY A., B ULLMORE E. T.: Fundamentals
of Brain Network Analysis. Elsevier Academic Press, 2016. doi:10.
1016/C2012-0-06036-X. 2[GBNO21] G UEST J. M., BAST A., NARAYANAN R. T., O BERLAEN DER M.: Thalamus gates active dendritic computations in cortex during
sensory processing. bioRxiv (2021). doi:10.1101/2021.10.21.
465325. 7[GCM∗ 19] G LEESON P., C ANTARELLI M., M ARIN B., ET AL .: Open
source brain: A collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron 103 (8 2019), 395–411.e5. doi:10.1016/j.neuron.2019.
05.019. 2[HVU∗ 22] H ARTH P., VOHRA S., U DVARY D., O BERLAENDER M.,
H EGE H.-C., BAUM D.: A stratification matrix viewer for analysis ofneural network data. In Eurographics Workshop on Visual Computing
for Biology and Medicine (2022), Raidou R. G., Sommer B. et al., (Eds.),
The Eurographics Association. doi:10.2312/vcbm.20221194. 2[JSS∗ 20]

J ONSSON D., S TENETEG P., S UNDEN E., E NGLUND R., KOTTRAVEL S., FALK M., Y NNERMAN A., H OTZ I., ROPINSKI T.: Inviwo— a visualization system with usage abstraction levels. IEEE Trans. Vis.
Comput. Graph. 26 (11 2020), 3241–3254. doi:10.1109/TVCG.
2019.2920639. 1[KD18] KORNFELD J., D ENK W.: Progress and remaining challenges inhigh-throughput volume electron microscopy. Curr. Opin. Neurobiol. 50(June 2018), 261–267. 1[KH13] K EHRER J., H AUSER H.: Visualization and visual analysis ofmultifaceted scientific data: A survey. IEEE Trans. Vis. Comput. Graph.
19, 3 (2013), 495–513. doi:10.1109/TVCG.2012.110. 2[LBI∗ 12] L AM H., B ERTINI E., I SENBERG P., P LAISANT C., E MPIRI CAL S. C., C ARPENDALE H. L. E. B. P. I. C. P. S.: Empirical studies
in information visualization: Seven scenarios. IEEE Trans. Vis. Comput.
Graph. 18 (2012), 1520–1536. doi:10.1109/TVCG.2011.279. 5[Lek23] L EKSCHAS F.:
Regl-scatterplot: A scalable interactive
javascript-based scatter plot library. J. Open Source Softw. 8, 84 (4 2023),
5275. doi:10.21105/joss.05275. 5, 8

[BTB∗ 22] B EYER J., T ROIDL J., B OORBOOR S., H ADWIGER M.,
K AUFMAN A., P FISTER H.: A survey of visualization and analysis in
high-resolution connectomics. Comp. Graph. Forum 41, 3 (2022), 573–
607. doi:10.1111/cgf.14574. 2[LWLG22] LY I S., WANG Q., L EKSCHAS F., G EHLENBORG N.:
Gosling: A grammar-based toolkit for scalable and interactive genomics
data visualization. IEEE Trans. Vis. Comput. Graph. 28 (1 2022), 140–
150. doi:10.1109/TVCG.2021.3114876. 2[BV18] B REDDELS M. A., V ELJANOSKI J.: Vaex: big data explorationin the era of gaia. Astronomy & Astrophysics 618 (10 2018), A13. doi:
10.1051/0004-6361/201732493. 9[MAAB∗ 17] M OHAMMED H., A L -AWAMI A. K., B EYER J., C ALI C.,
M AGISTRETTI P., P FISTER H., H ADWIGER M.: Abstractocyte: A visual tool for exploring nanoscale astroglial cells. IEEE Trans. Vis. Comput. Graph. 24, 1 (2017), 853–861. doi:10.1109/TVCG.2017.
2744278. 2[CJS∗ 22] C AKMAK E., JACKLE D., S CHRECK T., K EIM D. A., F UCHS
J.: Multiscale visualization: A structured literature analysis. IEEE
Trans. Vis. Comput. Graph. 28 (12 2022), 4918–4929. doi:10.1109/
TVCG.2021.3109387. 2[Das]

Dash. URL: https://dash.plotly.com/. 2[DGH03] D OLEISCH H., G ASSER M., H AUSER H.: Interactive feature specification for focus+context visualization of complex simulation
data. In Proceedings of the 5th Joint IEEE TCVG – EUROGRAPHICS
Symposium on Visualization (VisSym) (2003), Bonneau G.-P., Hahmann
S., Hansen C. D., (Eds.), The Eurographics Association, pp. 239–248.
doi:10.2312/VisSym/VisSym03/239-248. 2[Dol04] D OLEISCH H.:
Visual Analysis of Complex Simulation
Data using Multiple Heterogenous Views. PhD thesis, TU Wien,
Vienna, Austria, 2004. URL: https://www.cg.tuwien.ac.
at/research/publications/2004/doleisch-thesis/
doleisch-thesis-pdf.pdf. 2[McN22] M C N UTT A. M.: No grammar to rule them all: A surveyof json-style dsls for visualization. IEEE Trans. Vis. Comput. Graph.
(2022), 1–11. doi:10.1109/TVCG.2022.3209460. 1, 2, 8
[met] MeteorJs: The platform for shipping JavaScript applications. URL:
https://www.meteor.com. 5[MHM20] M C I NNES L., H EALY J., M ELVILLE J.: Umap: Uniform
manifold approximation and projection for dimension reduction, 2020.
arXiv:1802.03426. 8[NDPW∗ 18] N OWKE C., D IAZ -P IER S., W EYERS B., H ENTSCHEL B.,
M ORRISON A., K UHLEN T. W., P EYSER A.: Toward rigorous parameterization of underconstrained neural network models through interactive
visualization and steering of connectivity generation. Front. Neuroinform. 12 (2018), 32. doi:10.3389/fninf.2018.00032. 2[NEJ∗ 15]NARAYANAN R. T., E GGER R., J OHNSON A. S.,© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

Harth et al. / Coordinated Views
M ANSVELDER H. D., S AKMANN B., DE KOCK C. P., O BER LAENDER M.:
Beyond columnar organization: Cell type- andtarget layer-specific principles of horizontal axon projection patterns
in rat vibrissal cortex. Cereb. Cortex 25 (11 2015), 4450–4468.
doi:10.1093/cercor/bhv053. 7[NSA∗ 13] N OWKE C., S CHMIDT M., A LBADA S. J. V., E PPLER J. M.,
BAKKER R., D IESRNANN M., H ENTSCHEL B., K UHLEN T.: VisNEST- interactive analysis of neural activity data. In IEEE Symposium on
Biological Data Visualization, BioVis 2013, Atlanta, GA, USA, October
13-14 (2013), IEEE Computer Society, pp. 65–72. doi:10.1109/
BioVis.2013.6664348. 2[par]ParaView. URL: https://www.paraview.org/. 1[Plo] Plotly.js. URL: https://plotly.com/javascript/. 1, 2,
5, 8[RCM∗ 20]R EINA G., C HILDS H., M ATKOVI Ć K., B ÜHLER K.,
WALDNER M., P UGMIRE D., KOZLÍKOVÁ B., ROPINSKI T., L JUNG
P., I TOH T., G RÖLLER E., K RONE M.: The moving target of visualization software for an increasingly complex world. Comp. Graph. 87 (42020), 12–29. doi:10.1016/j.cag.2020.01.005. 1[rea] React-Grid-Layout.
URL: https://github.com/
react-grid-layout/react-grid-layout. 4, 5
[Rob07] ROBERTS J. C.: State of the art: Coordinated & multiple viewsin exploratory visualization. In Proceedings - Fifth International Conference on Coordinated & Multiple Views in Exploratory Visualization2007 (CMV 2007), Zürich, Switzerland, 2 July (2007), Andrienko G.,
Roberts J. C., Weaver C., (Eds.), IEEE Computer Society, pp. 61–71.
doi:10.1109/CMV.2007.20. 2, 3
[SCH∗ 18]

S ENK J., C ARDE C., H AGEN E., K UHLEN T. W., D IES MANN M., W EYERS B.: VIOLA - A multi-purpose and web-based

visualization tool for neuronal-network simulation output. Front. Neuroinform. 12 (11 2018), 75. doi:10.3389/fninf.2018.00075.
2[Sch21] S CHOLZ D.: A modular domain-specific language for interactive 3d visualization. Diploma Thesis, 2021. doi:10.34726/hss.
2021.80484. 2[SE12] S CHMITT O., E IPERT P.: NeuroVIISAS: Approaching multiscale
simulation of the rat connectome. Neuroinformatics 10 (7 2012), 243–
267. doi:10.1007/s12021-012-9141-6. 2[SGS18] S ARIKAYA A., G LEICHER M., S ZAFIR D. A.: Design factors
for summary visualization in visual analytics. Comp. Graph. Forum 37(6 2018), 145–156. doi:10.1111/cgf.13408. 2, 9
[SHB∗ 14] S EDLMAIR M., H EINZL C., B RUCKNER S., P IRINGER H.,
M OLLER T.: Visual parameter space analysis: A conceptual framework.
IEEE Trans. Vis. Comput. Graph. 20 (12 2014), 2161–2170. doi:10.
1109/TVCG.2014.2346321. 2[Shn96] S HNEIDERMAN B.: The eyes have it: a task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE Symposium on Visual Languages (1996), pp. 336–343. doi:10.1109/VL.
1996.545307. 9[SJES19] S CHWANKE S., J ENSSEN J., E IPERT P., S CHMITT O.: Towards Differential Connectomics with NeuroVIISAS. Neuroinformatics
17 (1 2019), 163–179. doi:10.1007/s12021-018-9389-6. 2[SMWH17] S ATYANARAYAN A., M ORITZ D., W ONGSUPHASAWAT K.,
H EER J.: Vega-Lite: A grammar of interactive graphics. IEEE Trans. Vis.
Comput. Graph. 23 (2017), 341–350. doi:10.1109/TVCG.2016.
2599030. 1, 2, 3, 5, 8, 9
[SRHH16] S ATYANARAYAN A., RUSSELL R., H OFFSWELL J., H EER
J.: Reactive Vega: A streaming dataflow architecture for declarativeinteractive visualization. IEEE Trans. Vis. Comput. Graph. 22 (2016),
659–668. doi:10.1109/TVCG.2015.2467091. 1, 2, 9[Str]Streamlit. URL: https://streamlit.io/. 2[SWH05]S TALLING D., W ESTERHOFF M., H EGE H.-C.: amira: A© 2023 The Authors.
Proceedings published by Eurographics - The European Association for Computer Graphics.

highly interactive system for visual data analysis. In Visualization Handbook, Charles D. Hansen C. R. J., (Ed.). Academic Press / Elsevier, 2005,
pp. 749–767. doi:10.1016/b978-012387582-2/50040-x. 1[SWH14] S ATYANARAYAN A., W ONGSUPHASAWAT K., H EER J.:
Declarative interaction design for data visualization. In The 27th Annual
ACM Symposium on User Interface Software and Technology UIST ’14,
Honolulu, HI, USA, October 5-8 (2014), Benko H. et al., (Eds.), ACM,
pp. 669–678. doi:10.1145/2642918.2647360. 1, 2, 9
[SZS∗ 17]

S ACHA D., Z HANG L., S EDLMAIR M., L EE J. A., P EL TONEN J., W EISKOPF D., N ORTH S. C., K EIM D. A.: Visual in-teraction with dimensionality reduction: A structured literature analysis. IEEE Trans. Vis. Comput. Graph. 23 (1 2017), 241–250. doi:
10.1109/TVCG.2016.2598495. 2[TCG∗ 22] T ROIDL J., C ALI C., G RÖLLER E., P FISTER H., H ADWIGER
M., B EYER J.: Barrio: Customizable spatial neighborhood analysis andcomparison for nanoscale brain structures. Comp. Graph. Forum 41(2022). doi:10.1111/cgf.14532. 2[TWC∗ 22] T ROIDL J., WARCHOL S., C HOI J., M ATELSKY J., D HANYSAI N., WANG X., W ESTER B., W EI D., L ICHTMAN J. W., P FISTER
H., B EYER J.: Vimo: Visual analysis of neuronal connectivity motifs.
bioRxiv (2022), 2022–12. doi:/10.1101/2022.12.09.519772.
2, 9[UHM∗ 22] U DVARY D., H ARTH P., M ACKE J. H., H EGE H.-C.,
DE KOCK C. P. J., S AKMANN B., O BERLAENDER M.: The impact
of neuron morphology on cortical network architecture. Cell Rep. 39 (42022). doi:10.1016/j.celrep.2022.110677. 6, 7
[ZPWS22] Z ONG J., P OLLOCK J., W OOTTON D., S ATYANARAYAN A.:
Animated Vega-Lite: Unifying animation with a grammar of interactive
graphics. IEEE Trans. Vis. Comput. Graph. (2022), 1–11. doi:10.
1109/TVCG.2022.3209369. 2