=Paper=
{{Paper
|id=Vol-2119/paper11
|storemode=property
|title=Beyond One-Dimensional Portraits: A Synoptic Approach to the Visual Analysis of Biography Data
|pdfUrl=https://ceur-ws.org/Vol-2119/paper11.pdf
|volume=Vol-2119
|authors=Florian Windhager,Matthias Schlögl,Maximilian Kaiser,Ágoston Zénó Bernád,Saminu M. Salisu,Eva Mayr
|dblpUrl=https://dblp.org/rec/conf/bd/WindhagerSKBSM17
}}
==Beyond One-Dimensional Portraits: A Synoptic Approach to the Visual Analysis of Biography Data==
https://ceur-ws.org/Vol-2119/paper11.pdf
Beyond One-Dimensional Portraits: A Synoptic Approach to the
Visual Analysis of Biography Data
Florian Windhager1, Matthias Schlögl2, Maximilian Kaiser2, Ágoston Zénó Bernád2,
Saminu Salisu1, Eva Mayr1
1 Danube University Krems, Austria 2 Austrian Academy of Sciences, Vienna
Dr. Karl Dorrek-Str. 30, 3500 Krems Hollandstrasse 11–13, 1020 Vienna .
E-mail: firstname.lastname@donau-uni.ac.at firstname.lastname@oeaw.ac.at .
Abstract
The study of biography data – and the reasoning with it – can be supported by multiple visualization techniques. Biographical data-
bases contain massive amounts of temporally structured biographical entries, connecting events, places, institutions and actors with a
variety of relations between them. We present a synoptic visualization concept for multi-dimensional biographical analyses, to go
beyond well-established techniques to portray one-dimensional data aspects. We discuss synergies arising from the combination of
multiple synchronic and diachronic views into a more coherent visual analytics environment. Possible synchronic views include
geographic, relational and categorial perspectives on biography data (e.g., maps, network and treemap diagrams), while multiple
diachronic perspectives are provided by coordinated multiple views, animation, layer superimposition, layer juxtaposition, and
space-time cube representations. By closely intertwining these visualization methods we aim to support the creation of more integrated
and connected mental models of biographical data. This visual framework is open for other fields of application like prosopographical
research, digital history, or many other time-oriented arts and humanities data domains.
Keywords: biography data, prosopography data, information visualization, visual analytics, information integration, mental model
background, we consider the integration of
1. Introduction one-dimensional data portraits into bigger pictures to be a
Digital biographical databases are a rich resource for novel and noteworthy objective for advanced visualiza-
historical research: They provide a massive amount of tion system design.
information, which used to be scattered in different text To do so, we will look at the initial state of textual
collections or local archives, and make it possible to biography data (e.g., as given by biographical lexica) and
technically connect them to bigger pictures of the life how it is currently transformed into structured digital data
patterns of historic individuals and groups. Yet, analyzing, (Section 2). A discussion of related work in visualization
as well as reasoning and sensemaking with these multi- research (Section 3) will be followed by reflections on
dimensional data remains challenging, especially for challenges posed by the utilization of multiple but sepa-
non-experts in digital methods. In this paper we present rated perspectives (Section 4). To effectively tackle these
how an integrated visualization framework (PolyCube challenges with a novel visualization system design we
project, 2018) addresses these challenges by developing a introduce the PolyCube framework (Section 5) and out-
synoptic visualization approach for the study of biog- line options for its future elaboration (Section 6).
raphy data.
Information visualizations “use computer- support- 2. Textual biography data
ed, interactive, visual representations of abstract data to Collecting, documenting and sharing facts and stories
amplify cognition” (Card et al., 1999). Visual representa- about the lives of relevant individuals is a core activity of
tions help to explore and analyze data distributions and human cultures, and the essential objective for biography
patterns immediately, and to reason on them interactively. researchers since centuries (Roberts, 2002).
Some biographical databases already offer such support-
ive measures in form of basic visual representations like
maps, networks or timelines (cf. APIS project, 2018).
These techniques allow to analyze single da-
ta-dimensions, such as geographical, relational or tem-
poral aspects of individual biographies. However, such
selective or one-dimensional visualizations do not allow
to investigate cross-dimensional questions like “How
does the movement of actors affect their social networks,
institutional affiliations, or their means and rhythms of
cultural production?”. Figure 1: Biographical lexica collect textual data and
Going beyond the use of multiple but unconnected images about historically relevant individuals.
views, visualization research already provides various Screenshot from the ÖBL (Österreichisches Biogra-
synoptic design strategies, which require a careful adap- phisches Lexikon, 2018).
tation to the biography research realm. Against this
67
As a result, hundreds of thousands of textual descriptions For the project, a custom relation-based data-model was
have been accumulated into biographical libraries and developed. It covers persons, places, institutions, works
dictionaries, which are recently transformed into struc- and events and allows for interrelating all of these entities.
tured data collections by digital humanities initiatives While entities also contain easy to adapt attributes, rela-
(Bernád, Gruber & Kaiser, 2017). tions only consist of a time frame and a type. Attributes of
entities and the relation type are SKOS (Simple
2.1 Digital biography projects Knowledge Organization System) based vocabularies.
While traditional written collections have largely ap- The data model also allows for keeping a complete edit
peared as meaningless textual “strings” to digital research log.
approaches before, methods of natural language pro-
cessing (NLP) allow to transform these texts into struc-
tured, semantically enriched data. Several research groups
throughout Europe are currently working on creating
enriched linked open datasets (LOD) based on national
biographical dictionaries (e.g.. Fokkens et al., 2014;
Reinert et al., 2015; APIS project, 2018). Starting from
textual entries on historic individuals - see Figure 1 for an
exemplary entry on the bishop Friedrich Piffl (1864–
1932) from the Austrian Biographical Dictionary
1815-1950 (ÖBL)1 – NLP methods enable the extraction
of structured entities, including (names of) actors, places,
institutions, or events, all featuring different attributes and
interrelations, which are changing due to actions and
developments over time (Reinert et al., 2015; Reznik &
Shatalov 2016; Schlögl & Lejtovicz, 2017). The resulting
data collections are often modeled as time-oriented
knowledge graphs, which are accessible for novel data
and text-analytical procedures, including methods of
visual analysis and communication (see sec. 3).
While the future promises of such technologies for
historic research are striking – in terms of openly acces-
sible databases containing millions of actors and relations
- there are still a lot of problems to solve. Most of the
biographical dictionaries started several decades ago
when printing books was still expensive and therefore Figure 2: Digital biography projects extract entities (such
make extensive use of abbreviations. Most of modern as places, persons, institutions, events and works) and
NLP tools on the other hand are trained on digital born their interrelations as time-oriented, structured data.
texts and perform very bad on these abbreviations. Even Screenshot from APIS project (2018).
when the NLP part (mainly named entity extraction)
works well, the automatic linking of entities - finding the For a smooth and easy editing process, a web application
real world expression of a string - is still a merely un- was developed (see Fig. 2). Amongst others, it features
solved problem. This is especially true for biographies autocompletes, automatic links to reference resources
where we often miss additional information on the entities (such as Geonames and GND), the possibility to highlight
found in the text. Visual analytics is not only important for or annotate entities directly in the biography, a basic
analyzing the final data, but can also play a crucial role in mapping and network visualization component with
detecting errors in this unsteady process. export functionality (Schlögl & Lejtovicz, 2017).
Similar to other biography digitization projects, the
2.2 The APIS system APIS system aggregates large amounts of structured data
The APIS system was developed in the course of the to support historians and humanities scholars’ research
identically named digitization project (APIS, 2018). The activities. Yet to make these large amounts of data more
APIS project deals with semantically enriching the Aus- accessible and to efficiently support the corresponding
trian Biographical Dictionary (Österreichisches Biogra- reasoning and sensemaking processes, advanced (visual)
phisches Lexikon 1815–1950, ÖBL), which is a suprana- analysis methods are required.
tional work of reference covering courses of life and
career of about 20.000 historical figures of the former 3. Visualization of biography data
Austro-Hungarian monarchy and the First and Second Recent work in the visualization realm has documented
Republics of Austria. multiple options to support the visual analysis of bio-
1 graphical data from various synchronic (i.e. geographic,
Austrian Biographical Dictionary entry accessible online at
relational or structural) and diachronic (i.e.,
http://www.biographien.ac.at/.
68
time-oriented) perspectives. The table in figure 3 shows Implemented within multiple coordinated views,
different synchronic (i.e., not primarily time-oriented but synchronic perspectives (showing cross-sectional, struc-
structure or distribution-oriented) perspectives as rows. tural, or distributional data aspects, see fig. 1, first col-
Due to their general prominence, maps have already been umn) can combine their analytical features, but common-
widely adapted for the visualization of biography data ly have to be complemented by at least one analytical
(APIS project, 2018), and methods for the geo-temporal perspective on temporal aspects of data organization.
visualization of actor movements are under constant These diachronic perspectives can be added as linear
development (Ellegaard et al., 2004; Kapler & Wright, representations (e.g., as timelines in coordinated multiple
2005; Kwan et al., 2005; Goncalvez et al., 2015). For the views, see fig. 3, second column), or as various hybrid
visualization of relations between different actors, net- techniques to encode time as joint projections together
work frameworks (Schich et al., 2014; Kaiser, 2017), and with synchronic representations (see Figure 3, third to
mixed method approaches (Armitage, 2016) have been sixth column).
proposed. Attributes of historic individuals (such as
professions or fields of activity) have been visualized by
treemaps (Hidalgo et al, 2014), whereas other approaches
engaged in multi-method investigations and visualiza-
tions (Gergaud et al., 2017).
For diachronic perspectives, various approaches
have been developed to map time linearly as timelines
(Hiller, 2011; Brehmer, 2017). Other hybrid methods to
visually encode time in addition to synchronic data as-
pects include animation, layer juxtaposition, layer super-
imposition, and space-time cube representations, which
are represented as columns in figure 3.
Despite the growing amount of visualization tech-
niques, which are technically available to analyze selected Figure 3: A cross tabulation of synchronic (including
dimensions of biographical data collections, their orches- geographic, relational, and categorial visualizations;
trated use has not been advocated and investigated so far. rows) and diachronic visualization methods (split screen,
Also the challenge of integrating multiple views on dif- animation, superimposition, juxtaposition, and space-time
ferent data dimensions has not been addressed systemati- cube perspective; columns) for biography data.
cally so far. With regard to both of these research gaps, we Multiple views are a design principle of general relevance
consider the development of multi-perspective interfaces, for complex data, "in order to maximise insight, balance
which support the integration of different perspectives, to the strengths and weaknesses of individual views, and
be a next level design objective. Such a multi- avoid misinterpretation" (Kerracher et al., 2014). This
ple-perspective interface would also improve the chances applies for both synchronic and diachronic perspectives:
to detect fundamental errors in NLP-based data creation Given the importance of the temporal dimension in biog-
pipelines early on. raphy research, it seems obvious that multiple solutions to
represent time can increase the analytical diversity and
4. Combining multiple visualization capacity of a visualization system. Multiple views allow
perspectives researchers to select and switch between the most appro-
priate representations for the data and task at hand.
Given the complex and multidimensional nature of biog- Figure 3 cross-tabulates the various synchronic and
raphy data, every single visualization technique can diachronic visualization techniques mentioned so far, and
reveal only a rather one-sided or one-dimensional data depicts a basic design space for biography data visualiza-
portrait. Specific visualization methods (such as maps, tion, which remains also open for the addition of novel
networks or timelines) provide analytical benefits with methods (see section 6). It offers well-established options
regard to certain data and tasks, but are limited or useless for the visualization of biographic pathways through
with regard to others. Advanced visual interfaces aim to multiple “space-times” - as orthogonal combinations of
overcome these limitations by combining and utilizing synchronic (rows) and diachronic perspectives (columns)
multiple visualization techniques synchronously, which on the data. While single methods have already been
cover multiple data dimensions and aspects either by an implemented separately by various interfaces to bio-
interface of parallel views (often as coordinated multiple graphical data collections (cf. Section 2), their
views, Scherr, 2008) or as perspectives to be chosen in a well-composed combination and integration is a
serial manner. With regard to the distinction between next-level design challenge not tackled up to now.
synchronic and diachronic visualization techniques, we Yet, especially for interfaces with multiple views, a
argue that advanced visual-analytical interfaces to biog- new problem of visual-analytical complexity emerges:
raphy data are well-advised to integrate multiple views When historians aim to answers questions combining
and instances from both categories, also to cover the multiple data dimensions (such as “How did the migration
relevance of the temporal dimension for biographies. of an individual affect her/his social network, institutional
69
affiliations, or means and motivations of cultural pro- were extracted from the textual data shown in figures 1
duction?”) they commonly have to combine information and 2 (APIS project, 2018). The trajectory shows the main
from multiple views. This requires to build up a mental stations (from top to bottom) of his life, including Lan-
model bridging and integrating different data dimensions, skroun (Czech Republic), Vienna, Rome, and Hungary.
which is a task high in cognitive effort (Trafton et al.,
2000). Attention is commonly split between multiple
views and linked data have to be identified and related,
before they can be integrated into one mental model. Yet,
different visualization techniques (which we refer to as
“coherence techniques”, Schreder et al., 2016) can sup-
port researchers in assembling their local insights into a
bigger picture. Well-established techniques for such a
support derive from the visual integration of different data
dimension into a multidimensional visualization, and
among those, space-time cube representations show a
significant potential to mediate across the different splits
and separations of usually unconnected and particularistic
perspectives.
In the following we introduce a framework revolv- Figure 4: Visualization of the biographical trajectory of
ing around space-time cube representations. While this Friedrich Piffl (1864–1932) from a geo-temporal per-
framework initially demonstrates what one specific dia- spective, created with GeoTime (Kapler et al., 2005).
chronic perspective (i.e. the space-time cube) can do for
the visual analysis of biography data, we also show how For the purpose of comparative and combinatory re-
this perspective can play a crucial role for the cognitive search, composite visualizations (such as juxtaposed or
integration and mutual translation of multiple other dia- superimposed space-time paths) enable the visual com-
chronic perspectives (Bach et al., 2016). parison and combination of biographical life patterns,
including the study of similarities and differences of
5. A synoptic visualization framework uti- patterns among different actors. Figure 5 illustrates this
lizing multiple space-time representations option by displaying the pathways of the Austrian artists
and siblings Josefine and Rudolf Swoboda, whose careers
The PolyCube framework has been set up to support as portrait painters led them into opposite directions and
synoptic visual data analysis with regard to cultural col- to different royal courts spread across the world map.
lection data (Windhager et al., 2016; 2018). With regard
to history and biography data, it provides even richer
options to support visual investigation and information
integration between multiple views. We outline its main
perspective by tracing its geo-temporal origins, and move
on to demonstrate its analytical potential also for
non-geographic aspects of biography data. For this pur-
pose we combine prototype visualizations developed
across three different research projects (Federico et al.,
2011; Smuc et al., 2015; Mayr & Windhager, 2018), and
showcase an exploratory study conducted with biography
data (cf. Windhager et al., 2017).
5.1 Geographic space-time
The visual notation of the space-time cube originated in
human geography to allow for the visual analysis of Figure 5: The trajectories of the Austrian artists and
human movement patterns and of the diffusion of inno- siblings Josefine (1861-1924, orange) and Rudolf
vation. This visualization method blends synchronic (1959-1915, green) Swoboda, seen from a geotemporal
views like maps (as horizontal plane) and a diachronic perspective.
timeline (vertical z-axis) in an orthogonal fashion, which
Analyzing and visualizing exemplary entries from the
allows to model spatiotemporal data points (like events of
APIS data collection also made the problem of incomplete
historic travels) as a three-dimensional shape. Any spati-
and implicit information obvious: Biographical articles
otemporal behavior thus translates into a unique
contain a lot of implicit information that is hard to extract
space-time trajectory and enables historians to interpret
and visualize: Exemplarily, an entry stating “1922 X
biographic movements as visual patterns.
moved to Rome and became a professor at the University
Figure 4 illustrates this option for biography re-
of Vienna in 1928” makes clear that X moved to Rome in
search by taking on the geo-temporal movements of the
1922, but says only implicitly that he moved to Vienna in
Austrian archbishop Friedrich Piffl (1864-1932), which
70
1928. Similar is the problem of incomplete data: Piffl was production, professions, cultural domains, or knowledge
known to have managed monastery estates in Hungary. areas), visualizations like treemaps can provide a valuable
However, his biography does not mention the exact loca- synchronic perspective (cf. Hidalgo et al., 2014). Thus, by
tions of these monasteries. By proxy, the visualization in implementing treemaps into categorial-temporal cubes
Figure shows a point where Piffl most certainly never was (see Fig. 7), a diachronic perspective unfolds, which
(i.e., the middle of Hungary). We consider data aspects discloses novel patterns of movement or persistence
and issues like these to be drivers for the future devel- through categorial spaces (Smuc et al., 2015).
opment and necessary implementation of methods of
uncertainty visualization in the historical research and
visualization realm (sec. 6.4).
5.2 Relational space-time
Going beyond the geo-temporal data domain, space-time
cube representations can also offer insights into the dy-
namics and developments of different other
non-geographic data dimensions. The resulting trajecto-
ries then represent the movements of individuals through
further space-times of analytical value, like so-
cial-relational space-times, generated by interaction
patterns of collaboration or conflict.
Figure 7: Individual movement through categorial
space-time, as demonstrated with regard to the knowledge
space of a patent classification by Smuc et al. (2015).
5.4 Linking multiple space-time cubes
In analogy to multiple coordinated views (Scherr, 2008),
we promote the connection of multiple space-time cubes
to synoptic ensembles. This enables the visual exploration
of biographies in multiple relevant space-times in parallel
(Figure 8). The specific line up of space-time-cubes -
which could include various further methods - naturally
depends on available data (and data dimensions), and the
Figure 6: Visualization of an individual movement intended analytical tasks. We consider such a synoptic
through social-relational space-time, as demonstrated by setup to provide an effective visualization environment,
Federico et al. (2011). which could be explored by the means of different inter-
action techniques (such as brushing and linking), but
Figure 6 conceptually illustrates this option by the high- which could also serve as a versatile scaffold for the
lighted movement of an actor through an evolving so- selection of more detailed analytical perspectives, in-
cial-relational structure, as defined by a group of other cluding well-established methods of flat visualization
actors (Federico et al., 2011). Depending on the richness design, as will be discussed in the next section.
of relational and temporal data, such visualizations can
enable historians to study the interactions of individuals
of interest and to track their careers as movements, which
often lead them from the socio-cultural peripheries of
larger network graphs or clusters to their structural cores.
These visualization thus can show macro patterns and also
detailed interactions of individuals, including their rela-
tive positions and the development of their network
centrality measures (Weingart, 2013; Bernád et al., 2017).
5.3 Categorial space-time
As a third variation of space-time cube representation we
outline the option to visualize the pathways of individuals
through any other space defined by categories, which
Figure 8: The PolyCube visualization environment for
historians use for classifying activities. With regard to all
biography data using multiple coordinated cubes, based
possible activity spaces, in which historical individuals
on space-time cube representations utilizing maps, net-
have been active (such as social-structural fields of re-
work diagrams and treemaps (from left to right).
71
5.5 Mediating multiple synchronic and
diachronic views
Bach et al. (2016) have shown, that space-time cube
representations also support the (cognitive) translation
and mediation of the working principles of multiple
diachronic and synchronic views - also by the means of
seamless canvas transitions and the smooth adaptation of
the perspective on the visualization (figure 9). Given the
outlined (linked) visualization of the outer right column of
figure 3, the other temporal visualizations (i.e. layer
juxtaposition, layer superimposition, or animation - as
well as all possible “space-flattened or time-flattened”
standard perspectives - could be seamlessly generated out
of the different space-time cubes. We contend that such Figure 10: Visualization of the temporal development
seamless translations will have a positive effect on the patterns of groups or organizations, as seen from a
preservation of mental models of complex time-oriented set-typed prosopographical research perspective.
data, and as such for the navigation and visual reasoning -
6.2 Process and project visualization
especially in the early stage of an exploration process.
While actor trajectories have been featured and visualized
as consistent lines so far, these life paths can obviously
also be parsed and segmented according to biographically
meaningful units of a finer temporal granularity. This
allows to visualize and annotate single processes or pro-
jects, whose pursuit is strongly structuring and guiding
individual behavior - also if nothing else (e.g. no move-
ment or interaction) is visible from another visualization
perspective. Practical means to visualize projects or
processes derive from the separation of (colored) seg-
ments, tick marks, or annotations, which could be applied
in a nested temporal structure, signifying long-term work
Figure 9: Space-time cube representations can help to or life phases, mid-range projects or procedures, and basic
preserve and translate mental maps and visualization actions or events (Figure 11).
perspectives (adapted from Bach et al., 2016).
6. Discussion
With regard to the visualization framework outlined so
far, we discuss interesting options for further develop-
ment.
6.1 Prosopographical data visualization
Going beyond single trajectories, the outlined framework
is open for more complex analyses to be undertaken with
bigger prosopographical datasets. Prosopography is the
domain for studying biographies as seen from a collective Figure 11: Options of process and project visualization,
perspective (Keats-Rohan, 2007). Historians deal with a building on temporal activity patterns.
wide variety of social collectives – such as organizations,
religions, art schools, political entities, conflicts, or 6.3 Sentiment visualization
movements of innovation. For their analyses, the pro- Along with the visualization of biographical project and
posed framework can also be adapted to map the temporal work cycles, we consider the visualization of sentiment
development of groups as sets. data (whether of individual actors or within actor net-
Figure 10 enumerates different visual patterns, works) to be of high interest for future approaches. With
which - in combination - can map all the complex devel- increasing options to also extract sentiment data from
opments of historical groups or collective entities. As a textual sources, rich and qualitative biographical accounts
method for aggregated representation, prosopographical will allow the visualization of emotional stages phases, or
or collective set visualizations can complement the dis- chapters of life, related to critical events, like success or
play of line-like, individual trajectories in geographic or defeat, as well as stages of illness, recovery, thriving, and
relational space-times. many more (cf. Kucher et al., 2017).
72
6.4 Uncertainty visualization bine textual data with a graphic representation is to actu-
ally tell a story sequentially and incrementally on a textual
In the more general context of history and humanities data
basis, while zooming and panning to selections of a
collections, we see a specific need to handle questions of
space-time path, as it is already offered for
data quality and uncertainty in a reflected manner. Criti-
two-dimensional representations by tools like Story-
cal questions of data provenance and quality necessarily
MapJS2 or ESRI storyteller.3
arise from the investigation of historically fragmented and
often disputed data sources. In this context, the deliberate
6.7 Automated vs. qualitative visualization
representation of uncertainty measures can help to bring
transparency, awareness and trust into the collective To further foster and enable control and curation of
interpretation process (Sacha et al., 2016). largely automated natural language processing endeavors
– but also for the means of a qualitative complementation
6.5 Mapping controversies of these highly complex procedures – we consider options
Differences and debates about data, sources and repre- for manual input and data curation to be an essential
sentations are all the more likely when experts and future feature. This will aid to the existing options for data
scholars are working in distributed or even competing development and enrichment, but also enable shorter
settings of multilateral data curation and interpretation. modelling cycles by starting to generate structured biog-
Aside from the options to collaboratively and consensu- raphy data from scratch. For this purpose, we consider
ally enrich visual representations of historical figures, we either options for manual data creation (e.g., by a simple
consider it relevant to also make different scholarly event-based spreadsheet notation), or direct spatiotem-
standpoints and interpretations available and visible. This poral drawing functionalities to be of high practical value,
would allow to utilize the outlined framework not only to which will allow to generate biography visualization –
communicate agreed-upon results, but also to motivate and quantitative or structured data – from existing expert
and support the collective critical editing, revising and knowledge, which has not been codified or formalized in
annotating of biographical knowledge graphs. As such, any other context so far.
competing interpretations could be studied, compared and
taught on a visual basis, and historiographical controver- 7. Conclusion
sies could be made productive (Marres, 2015). With this paper we discuss the creation of structured data
from biographical texts, and advanced options of their
6.5 Visual storytelling visual analysis. The outlined visualization framework
Given the increasingly advanced options for the largely firstly provides visual-analytical access to complex biog-
user-driven exploration of biography data by the means of raphy data, as well as visual reasoning support on an
multi-perspective visualizations, we consider it specifi- overview and detail level. Secondly, it offers multiple
cally interesting to merge these representation techniques perspectives to generate richer and non-reductionist
with narrative or author-driven representation techniques portraits of the available data. Finally, it aims to consid-
(Segel & Heer, 2010) to tell life stories, e.g. of national erately support scholar’s information integration by uti-
cultural heroes. Storytelling then could enrich the analyt- lizing space-time cube representations. In addition to
ical systems with sequential guidance for the purpose of challenges arising from the ongoing effort of implemen-
scholarly communication, the pedagogy and teaching tation and evaluation, we suggest to focus on a number of
realm, but also for data-driven journalism and public objectives for future research (see sec. 6) to enable a more
knowledge communication (Mayr & Windhager, 2018). complex and synoptic understanding of the life and work
of historical individuals.
6.6 Integrating close & distant reading
As for its application, the outlined framework can be 8. Acknowledgements
productively used as an interface connected to structured
data collections, or as an interface visualizing textual data This research was supported by a grant from the Austrian
via automated natural language processing pipelines. In Science Fund (FWF), project number P28363-G24.
this context it seems essential, to offer access to textual
source data in parallel to visual representations. This
allows to study and “close-read” a source text in compar- 9. References
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StoryMapJS: https://storymap.knightlab.com/
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