Damast: A Visual Analysis Approach for Religious History Research

Max Franke

and Steffen Koch

University of Stuttgart, Germany

{ﬁrst.last}@vis.uni-stuttgart.de

Keywords:

Digital Humanities, Visual Analytics, Reproducibility, Provenance, Interactive Filtering, Conﬁdence.

Abstract:

Digital humanities (DH) combines research objectives in the humanities with digital data acquisition, process-

ing, and presentation methods. This work describes the development of a visualization approach in the ﬁeld of

DH to analyze the coexistence of institutionalized religious communities in Middle Eastern cities during the

medieval period. Our approach aims to support the entire process of data acquisition, storage, analysis, and

publication with interactive visualization. The support of the whole process enables a consistent concept for

the representation of conﬁdence, the collection of provenance information, and the implicit storage of gained

knowledge. Our concept empowers scholars to trace obtained results up to the veriﬁability of details in the

corresponding sources, facilitates collaborative analyses, and allows for the serialization of results and use in

corresponding publications. We also reﬂect on the beneﬁts, limitations, and lessons learned when applying

interactive visualization to the concrete tasks and with respect to data collection and publication of ﬁndings.

1 INTRODUCTION

In digital humanities (DH) projects data collection,

analysis, and publication of results are often consid-

ered separately. However, the data life cycle is rarely

a one-way trip: often, inconsistencies in the data are

only found during analysis. In such cases, considering

the data curation and its analysis as separate processes

can hinder the improvement of the data. Rather, for-

aging and sensemaking loops (Pirolli and Card, 2005)

should be supported in one place. Currently, ﬁrst ap-

proaches (Bors et al., 2019; Freire et al., 2008; Ra-

gan et al., 2016) try to make results more traceable

with the help of analysis provenance. Especially in

DH projects dealing with historical data, individual

sources only yield an incomplete perspective on his-

torical actualities. Here; considering multiple sources

is paramount to seeing the larger picture; where indi-

vidual biases of omission, exaggeration, and insincer-

ity can be identiﬁed and compensated for. A consis-

tent data schema, and the collection of historical and

analytical provenance data and conﬁdence measures

on individual pieces of historical evidence, are neces-

sary to support distant reading (J

anicke et al., 2015;

Moretti, 2005) on such collections.

We describe the outcome of a collaboration with

historians studying the peaceful coexistence of non-

https://orcid.org/0000-0002-4244-6276

https://orcid.org/0000-0002-8123-8330

Muslim religious groups under Muslim rule in the

medieval Middle East. In this collaboration, the histo-

rians collected evidence of the presence of individual

religious groups from textual sources. Their objective

was to collect and consider material from many differ-

ent sources to balance out the biased and incomplete

perspectives of individual historical sources.

Our contributions are the following: (1) With

Damast, we describe an interactive visual ap-

proach (Fig. 1) which supports our collaborators’

workﬂows from data entry over visual analysis to pub-

lication. By covering the whole workﬂow, results be-

come transparently reproducible and traceable on dif-

ferent levels: Filters that were deﬁned during visual

analysis can be revisited. Every visual mark in the vi-

sualization can be traced back to the source where his-

torians learned about it. Historians’ conﬁdence in the

ﬁnding and even the text passage where it originated

from are accessible in an interactive way. (2) We re-

ﬂect on feedback and results, and on the applicability

of our approach to other research questions and areas.

We discuss challenges arising from introducing repro-

ducibility into interactive analyses in DH projects. We

propose general visual and interactive workﬂows and

strategies that address those challenges.

Franke, M. and Koch, S.

Damast: A Visual Analysis Approach for Religious History Research.

DOI: 10.5220/0011609700003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP, pages 40-52

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Figure 1: The visual analysis component of Damast consists of multiple, resizable and rearrangeable, coordinated views.

Visible here are the (a) religion view, (b) map view, (c) location list, (d) timeline, (e) conﬁdence, and (f) sources view. The

tags and undated data views, as well as the settings pane, are stacked behind visible views and can be accessed on demand.

More information about the data represented under the mouse cursor is shown in (g) tooltips. A step of the example analysis

from Section 6.1 is shown: The advanced religion ﬁlter permits only evidence from places where at least two of the three

religious groups Church of the East, Syriac-Orthodox Church, and Armenian Church are present. The timeline ﬁlter limits

evidence to that present in the 8

century.

2 RELATED WORK

DH is a broad ﬁeld with a multitude of data ecosys-

tems, workﬂows, tasks, and requirements. We hence

limit our related work discussion to DH approaches

making use of data visualization techniques related to

the contributions we make. We also discuss visualiza-

tion approaches outside of the DH that share similar

tasks or design choices. Our work is related to pub-

lications on reproducibility and persistent storage of

results in visual data analysis, as well as tracking of

data provenance, which we also brieﬂy discuss.

Visualization and Uncertainty in the DH. Rees

et al. (2018) collect historical evidence about Jewish

communities in the Byzantine empire. They employ

comparable data collection strategies and encounter

similar issues with data interpretation and the rep-

resentation of historical vagueness as we do. The

challenge of uncertainty in the context of historical is

considered in multiple other works: Windhager et al.

(2018, 2019) discuss uncertainty in cultural heritage

collections, and Panagiotidou et al. (2021) talk about

implicit error in archaeological data. Martin-Rodilla

and Gonzalez-Perez (2019) suggest ways to represent

qualitative vagueness in DH data. Sacha et al. (2015)

highlight how dealing with uncertainties in an open

way in visual analytics fosters trust in the visualiza-

tion and the results. Similarly to these works, we have

found uncertainty in DH data to be multifaceted, tied

to provenance and interpretation, and hard to quan-

tify (Franke et al., 2019). We employ a strategy in-

volving six different, qualitative aspects of historical

conﬁdence that the historians apply to the data. We

bind conﬁdence tightly to the data and include it in

visual analyses as ﬁltering criteria.

Ther

on S

anchez et al. (2019) reﬂect on existing

taxonomies on uncertainty, and how to apply them to

the DH. They discuss how uncertainty and meaning

can change in the course of analysis, which, along-

side the unique combinations of data quality and vol-

ume in DH data discussed by Sch

och (2013), are ﬁnd-

ings we also observed in our collaboration. Lamqad-

dam et al. (2021) highlight unique challenges in how

data is viewed in the humanities, emphasize the near-

constant presence of uncertainty, and suggest guide-

lines for a closer cooperation between DH and visu-

alization. We experienced similar challenges, and can

conﬁrm the fruitfulness of a close collaboration.

Text analysis and visualization (Liu et al., 2019)

are part of many DH projects. Similar techniques are

used here, but source texts—like in our case—are of-

ten challenging (J

anicke et al., 2017) since they are

multi-lingual, diachronic, and heterogenous regarding

document type. We hence take a pragmatic approach

to automated text analysis by offering suggestions for

terms that have previously been annotated. We use

annotation to support data entry and for linking back

to the passages from visual representations.

Interactive Visualization. Our visual analysis ap-

proach is a multiple coordinated view (MCV) sys-

tem (Roberts, 2007; Wang Baldonado et al., 2000)

that facilitates brushing and linking (Becker and

Cleveland, 1987; Ward, 1994) as well as ﬁltering.

MCVs are a common choice when analyzing multi-

Damast: A Visual Analysis Approach for Religious History Research

variate data visually, but there are different ways to

realize interactive ﬁltering. A common approach is

to use Boolean or set-based operators to let analysts

ﬁlter the multivariate data, either by deﬁning valid at-

tribute values or by selecting concrete entries from the

visual representation. The constraint-based solution,

which we apply in our approach as well, comes in two

common variations depending on how expressive the

ﬁltering should be: Some approaches allow to cre-

ate an explicit ﬁlter representation that can be very

powerful in terms of combining ﬁlters. Examples for

this strategy are ﬁlter/ﬂow representations (Young and

Shneiderman, 1993), DataMeadows (Elmqvist et al.,

2008), and PatViz (Koch et al., 2011). Implicit ap-

proaches, such as FacetLens (Lee et al., 2009), often

use faceted approaches with a convention-based ﬁl-

ter combination. Our own solution falls into the im-

plicit category. Filtering operations formalize impor-

tant analysis steps, which can be stored to make them

traceable and reuse them for reporting.

The MCV visualization of our analysis ap-

proach (see Section 5.2) comprises a geographical

view, a timeline view, a hierarchical view depicting

religions and denominations, a source and tag view,

and one for depicting the conﬁdence of data aspects.

Geographical analysis plays a major role when ad-

dressing DH questions and especially in investigating

historical data. Visualizing data on maps presents sev-

eral challenges such as accurate placement and over-

drawing. Different strategies including aggregation

and placement strategies are typically combined to

alleviate these problems for automatic map creation,

unfortunately reducing the accuracy at the same time.

Many approaches deal with these problems. Closely

related to our tasks are the works by Castermans et al.

(2016) and J

anicke et al. (2013), which address these

challenges through aggregation and highlight the im-

portance of interactivity. We use cluster-based aggre-

gation and details on demand (Shneiderman, 1996),

but also offer a mode without aggregation for smaller

subsets of data. Liem et al. (2018) suggest a geospa-

tial visualization of uncertain data using GeoBlobs.

We offer the possibility to encode uncertainty by color

and to ﬁlter by it, and further address spatial and tem-

poral uncertainty explicitly in separate views.

Besides geographical aspects, time is an inher-

ently important aspect for historical observations.

Many visual approaches to represent temporal in-

formation exist (Aigner et al., 2011). We apply a

straightforward approach using stacked histograms in

a timeline to represent this dimension. Often space

and time are analyzed together (Andrienko and An-

drienko, 2006; J

anicke et al., 2013; Mayr and Wind-

hager, 2018) and our approach is no exception to this.

For depicting religious groups we use an indented

tree plot—visualization research offers many options

here (Schulz, 2011). The text sources our collabora-

tors study can contain many valuable data entities. To

depict these quantities and to make ﬁltering and se-

lection possible within them, we used stacked bars to

realize scented widgets (Willett et al., 2007). These

are used as part of interactive ﬁltering and selection.

Reproducible and Sustainable Visual Analysis.

Provenance (Xu et al., 2020) and reproducibility are

relevant for many domains. Liu et al. (2017) and

Beals and Mero

no-Pe

nuela (2020) discuss prove-

nance and shareable workﬂows in the DH. Ragan

et al. (2016) classify provenance in visual analytics

by type and by purpose. In their framework; our ap-

proach covers the Data, Visualization, and Interaction

types of provenance information; as well as Insight to

a degree. Our approach also covers four of the six

purposes they list: Replication, Collaborative com-

munication, Presentation, and Meta-analysis. Action

recovery is also considered in the sense that we sup-

port recovery and history of visual analysis states.

This is similar to the bookmarking of visualization

state discussed by Heer and Shneiderman (2012) and

Park et al. (2021), or to the playback mentioned by

egas and Wattenberg (2006). Other work includes

explicit provenance graphs (Corv

o et al., 2021; Groth

and Streefkerk, 2006; Stitz et al., 2016) or uses in-

teraction logging (Bors et al., 2019; Guo et al., 2016;

Stitz et al., 2019). In our approach, data provenance

is considered part of the data, and reproducibility and

strong backwards connections through the data acqui-

sition and analysis workﬂows are supported, enabling

both close and distant reading (J

anicke et al., 2015;

Moretti, 2005). We employ a MCV approach and

record applied selections and ﬁltering constraints to

make these operations traceable. Our approach can

also export analysis results as a textual report, which

is similar to the documents with embedded visualiza-

tions presented by Latif et al. (2018), although our

approach focuses more on the serialized data itself.

3 BACKGROUND

The visualization approach described in this work is

the outcome of a joint DH project involving histori-

ans and computer scientists. The concrete Dhimmis

& Muslims project (Weltecke et al., 2022b) had the

goal to help investigate the coexistence of institution-

alized religious groups in the Middle East of the Mid-

dle Ages. At that time, other religious groups, such

as Christians and Jews, were allowed to peacefully

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

coexist under Muslim rule. In their research, our col-

laborators work mainly with printed or hand-written

texts. These sources are both primary sources from

the time period of interest, and literature that sum-

marizes, discusses, and evaluates historical ﬁndings

at a later date. This source material originates from

many different languages, authors, and writing sys-

tems, which makes digitization via optical character

recognition (OCR) difﬁcult. The heterogeneity of the

source material further prohibits the extensive com-

putational support of the historians’ workﬂows.

One challenge the historians faced from the start

was that contemporary sources of this time did

not portray coexistence of religious communities

objectively, but rather focused on and even over-

embellished the role of the religious group they them-

selves were part of. So, a Muslim scholar reporting

about their travels might omit seeing a Christian con-

gregation in a city they visited; or, a Christian list of

bishops might claim a bishopric in a city to keep their

political foothold in that region, although there would

be no bishop there. The truth content of individual

statements from these sources must be evaluated by

domain experts in the face of context knowledge and

permanently recorded. As such, it can also be valu-

able to record historical ﬁndings that are evidently

false, appropriately labeled, to get the whole picture.

The historians wanted to collect data from many

different sources to support analysis of the coexis-

tence of religious groups. The main data entity they

collected was the historical evidence. Each piece of

historical evidence ties together a religious group, a

place, and a time span. It also contains additional in-

formation such as different aspects of conﬁdence (i.e.,

how trustworthy the information is), which source it

came from, and comments by the historian collect-

ing it. Our collaborators aimed to analyze evidence

from different sources collectively, considering the

truth content of each piece of evidence to get insights

on the presence and absence of religious groups in

different regions during different time periods.

4 REQUIREMENTS

In regular meetings with the historians throughout the

collaboration, we got an insight into their domain

and their usual, analog workﬂows. The historians,

in turn, learned about information visualization op-

tions and their respective possibilities and limitations.

We formulated and iteratively reﬁned requirements

for Damast to augment their existing workﬂows.

Uniﬁed Dataset (R1). To support their main re-

search question—coexistence of religious groups—

an overview on the source material is necessary. Dis-

tant reading (J

anicke et al., 2015; Moretti, 2005) of

multiple sources is key to getting a more complete,

unbiased picture. The historians hence required a

dataset into which they could collect historical pieces

of evidence from many different sources, with a

ﬂexible data model to align evidence from different

sources in a common, well-deﬁned structure.

Interactive Visual Analysis (R2). The historians

wanted to explore the data and test hypotheses. Such

analyses needed to be interactive and happen in a top-

down fashion. Different aspects of the data, such

as the geographical and temporal aspect, religious

group, but also the conﬁdence, should be visualized

in a way that allowed to understand relationships be-

tween the different aspects. Further, the visualization

should support ﬁltering on these aspects, which can

be combined to allow for speciﬁc, powerful queries

to support a multitude of research questions.

Data and Analysis Provenance (R3). Being able

to understand, at all times, where data comes from and

how much it can be trusted is of great importance for

our collaborators. Recording the historical sources of

entered data, as well as data editing history and anal-

ysis steps for later reproducibility, in the dataset (R1)

was therefore necessary. Historical data also includes

uncertainty, and domain and context knowledge come

into play when interpreting historical sources. The

historians required to record aspects of conﬁdence, as

well as free-text metadata, with the data to aid later

interpretation (Franke et al., 2019).

Publication of Results (R4). For publication of

ﬁndings within their ﬁeld, the historians required a

way to persist analysis results. Screenshots of the

visualization, limited in resolution and lacking in-

teractive means to supplement the overview-ﬁrst ap-

proach, could not feasibly convey the needed infor-

mation. Rather, they desired a textual representation

of the subset of data, listing all details and the prove-

nance (R3) of the historical information without need-

ing interactivity and suitable for supplemental mate-

rial in a publication. The historians also wanted to be

able to reproduce and share the state of an analysis.

Integrated, Interlinked System (R5). Damast

consists of multiple components with different roles,

chieﬂy interfaces for data entry and edit (R1) and for

visual analysis (R2). These components should be

Damast: A Visual Analysis Approach for Religious History Research

available in one place, and be connected; for instance,

newly entered data should appear in the visual anal-

ysis component immediately. Damast should support

navigation to relevant data in the data entry interface

or the visualization analysis component to understand

connections (R3) or to correct issues in the data.

Geographical Aggregation (R6). Many of the his-

torians’ research questions relate to geographical dis-

tribution and co-location. Hence, accurate geograph-

ical representation was an important requirement.

They were used to working with large, printed maps,

such as that by Pirker and Timm (1993) showing

Christian communities in the medieval Middle East.

We deemed an extension of such a representation to

more religious groups, and to updating data (R5), not

scalable especially considering overdrawing and la-

bel placement. Hence, the geographical representa-

tion would need to aggregate, and to use interactiv-

ity (R2) to provide more details for a smaller subset

of data. However, the aggregation should neither give

false impressions of variety nor uniformity.

5 APPROACH

We implemented Damast as a web-based approach

to support scholars in their data entry, editing, anal-

ysis, and publishing efforts. This facilitates naviga-

tion between components, improves consistency, and

reduces setup and conﬁguration efforts by the histo-

rians. Damast consists of multiple, interlinked (R3,

R5) components with speciﬁc functions. The main

components are the visual analysis component (R2)

for exploration and analysis of the data (Section 5.2),

the GeoDB-Editor for tabular data entry and geoloca-

tion of places, the annotator for data entry on digitized

documents (Section 5.3), and reports representing an

analysis result (R4) in textual form (Section 5.4).

5.1 Data Model

A task that plays an important role in many research

questions of the historians is to study when and where

which religious groups coexisted. Therefore, the re-

lational data model (R1) revolves around the central

entity of the historical evidence (see Section 3). A

piece of evidence ties together a place and a religious

group, but usually also a temporal component and one

or more historical sources. Additional tables exist to

store alternative toponyms for the places, and to link

places and persons to their representations in other

historical databases. The database also contains tables

used for grouping evidences (such as: “is evidence of

a monastery,” or “needs review”), and tables to sup-

port document annotation. Additional tables are used

to record editing events on evidences for provenance.

Besides the historical data itself, a focus in our

project has been to record data quality and provenance

metadata that the historians ascribe to the data (R3)

when entering it into the database. This includes in-

formation about each datum such as its trustworthy-

ness, the reasons for recording it in a particular way,

or context knowledge necessary to interpret it. To do

so, we added extra data ﬁelds to all tables, following a

strategy of treating the historical conﬁdence as part of

the data (Franke et al., 2019). This includes a free-text

comment ﬁeld for each data entity to record any addi-

tional information. We also introduced a measure of

conﬁdence with ﬁve distinct levels: false, uncertain,

contested, probable, and certain. The historians then

assign these levels of conﬁdence to the geographical

placement of each place; the conﬁdence in the attri-

bution of an evidence to a place, person, religion, and

precision of a time span; and the trustworthyness of

sources and interpretation of evidences. With conﬁ-

dence as part of the data, historians can now also vi-

sualize this aspect, and use it in a ﬁlter criterion, for

a more reﬂected visual representation of the histori-

cal data. Not only can every visual mark in the views

of our approach be traced back to its source interac-

tively, but the conﬁdence and remarks of the scholars

who interpreted the source can be revisited as well.

5.2 Visual Analysis Component

Figure 1 shows a screenshot of the visual analy-

sis component of Damast, which we realized with a

MCV visualization (Roberts, 2007; Wang Baldonado

et al., 2000). Each view shows a different aspect of

the data and can be interacted with separately (R2).

The views support brushing and linking (Becker and

Cleveland, 1987; Ward, 1994), meaning that selecting

a visual element in one view will highlight elements

representing the same data in all views.

The visualization also implements multi-faceted

ﬁltering (Hearst, 2006; Weaver, 2004). In the map

view, the data can be ﬁltered by deﬁning regions of

interest. Filtering in the timeline constrains the data

according to time, ﬁltering in the religion view with

respect to religious group, and accordingly for the

other aspects such as conﬁdence and sources. Fil-

ters within the same view lead to a union operation,

joining both sets that fulﬁll the constraints. Filters be-

tween different views intersect the set of sets adhering

to the constraints. With this very simple strategy, very

powerful analyses can already be made; for instance:

“Show me all pieces of evidence in the region around

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

today’s Damascus in a time range between 750 and

850 CE, where entries have the conﬁdence level ‘con-

tested’ or ‘uncertain’ with respect to religious attribu-

tion.” There are deviations from this default strategy

allowing for even more sophisticated ﬁlters that were

requested by our partners, such as the advanced reli-

gion ﬁlter described below. To further support explo-

ration, each ﬁlter step is stored as an individual action

to a history tree, and users can undo and redo steps.

The religious groups in the database are hier-

archically organized and visualized as an indented

tree visualization (Burch et al., 2010) in the religion

view (Fig. 1a). Checkboxes are used to ﬁlter by re-

ligious group, with the possibility of either a simple

union ﬁlter (any of the selected groups), or a more

complex union of intersections. The latter allows to

deﬁne a ﬁlter that only applies to a city (and that city’s

evidences) if all religious groups in one of the sets

appear in that city. This facilitates complex queries

of coexistence of religious groups, such as that de-

scribed in Section 6.1. A main factor for using the

indented tree was its compactness, allowing our col-

laborators to allocate as much space as possible in the

MCV layout for the map view. The uniform nodes of

the indented tree also support labeling and color cod-

ing, making it easy to pick them for selection and ﬁl-

tering. At the same time we do not overemphasize one

religious group over the others. With the large num-

ber of religious groups, adequate representation was

also a challenge. Despite the drawbacks, we settled on

color, with the rationale that it is an often-used encod-

ing in our collaborators’ maps, which they were used

to interpret and distinguish. Using color also allowed

us to encode religious denomination consistently in

all views. We store the color for each religious group

as part of the data. This ensures consistency over time

and across media. For a better distinction, Damast

also offers a false-color mode, which spreads out the

color coding as much as possible based on the set of

religious groups visible (Fig. 2d). This mode is espe-

cially helpful with a small set of religious groups with

very similar colors, such as in the example analysis

described in Section 6.1, but lacks the consistency.

The map view (Fig. 1b) shows the geospatial as-

pect of the data. It takes a central place in the visu-

alization to support the map-based working method

of the historians. We decided aggregation would be

necessary, but iterated over the strength and strategy

of aggregation multiple times to ensure truthful, non-

misleading representation (R6). The ﬁnal design con-

sists of complex glyphs representing clusters of cities

in the map, where the clusters are constructed in a way

that ensures glyphs will not overlap. These represent

the religious groups in up to four pie charts (Figs. 2a

Figure 2: The map glyph design for the aggregation level

of religious groups (a) and for a smaller subset of data with

lower aggregation (b). The unclustered mode of the map (c

and d) uses smaller glyphs and is appropriate for analyses

of smaller subsets of the data, such as with the three groups

shown here. A false-color mode (d) can be used for color-

coding instead of using the colors stored in the database.

and 2b) representing the religious groups within by

colored slices. The slices are too small to convey

exact quantities, but help to understand the hetero-

geneity within glyphs. The religious groups are ag-

gregated by the hierarchy used in the religion view

as well. The depth level in the hierarchy chosen for

aggregation is the deepest one possible such that no

more than four aggregation groups remain. In most

cases, this results in the four top-level groups in the

hierarchy (Christianity, Islam, Judaism, and “Other

religions”) to be used (Fig. 2a), but for smaller sub-

sets of the data, aggregation might be on a lower hi-

erarchy level (Fig. 2b). The level of aggregation is

chosen globally for all glyphs. This ensures a rep-

resentation that does not suggest higher diversity in

less dense areas, where less aggregation would be re-

quired (R6). Damast also has an option to disable

the clustering, instead showing smaller glyphs with

each religious group represented by a small colored

circle, with one glyph for each place (Figs. 2c and 2d).

This option is more useful to view smaller subsets of

the data, at higher map zoom levels, because over-

laps are possible here. Still, it is a beneﬁcial addi-

tion for tasks where visual estimation of the number

of cities matching certain criteria, and their geospa-

tial extent, are of interest. For speciﬁc analyses by

the historians, the map also provides additional layers

visualizing religious diversity, or a density estimation

of evidences. Scholars can ﬁlter by geospatial loca-

tion by specifying one or more polygons in the map.

The cities visualized are also listed separately in the

location list view (Fig. 1c), where scholars can also

search for cities, or ﬁlter for a set of cities.

The timeline view (Fig. 1d) shows a timeline of

evidences, aggregated by religious group. To sup-

port the historians’ main research question of coex-

istence, the timeline is shown qualitatively, meaning

that it only shows whether there is evidence for the

religious group at each point in time, or not. Al-

Damast: A Visual Analysis Approach for Religious History Research

Figure 3: The qualitative (left) and quantitative (right)

modes of the timeline. In the qualitative mode, only the

presence or absence of a religious group is shown. In the

quantitative mode, a stacked histogram shows the number of

evidences for each religious group over time. An overview

timeline at the bottom can be used to zoom and pan.

ternatively, a quantitative stacked histogram of num-

bers of evidence per religious group per year can be

shown (Fig. 3 left). A smaller overview is shown

at the bottom, so that scholars can also zoom into a

smaller time span. Evidence can be ﬁltered by time

by selecting a time interval in the timeline itself.

Missing and incomplete data is a phenomenon fre-

quently occurring during data entry, when not all data

has been entered into the system yet. But, for histor-

ical data in particular, often data is still missing after

data entry is complete. Still, incomplete data entries

can be helpful in showing a better picture to schol-

ars (Eaton et al., 2003; Franke et al., 2019; Song and

Albers Szaﬁr, 2018). Therefore, we decided to make

missing data explicit in our visualization for the ge-

ographical location, and for missing temporal infor-

mation of partial historical evidence. Thus, places in

the database for which we do not know their loca-

tion are listed separately in the location list, and the

undated data view visualizes the count of evidences

without temporal information separately as a stacked

bar chart, grouped by main religious group.

The conﬁdence view (Fig. 1e) shows a matrix of

checkboxes, one row for each level of conﬁdence, and

one column for each aspect of conﬁdence. Next to

each checkbox, the evidence count for this combina-

tion of aspect and level is shown. This view is itself

a multi-faceted ﬁlter, where each column shows a dif-

ferent aspect of the data. The color mapping in the en-

tire visualization, which by default encodes religious

afﬁliation, can be switched to encode level of conﬁ-

dence (Fig. 4). The aspect of conﬁdence used in this

mode can be selected in the conﬁdence view.

The source view (Fig. 1f) lists the historical

sources the visualized evidence was extracted from.

The distribution of religious groups per source is vi-

sualized as a horizontal, normalized stacked bar chart;

and overall distribution of evidences on the sources

by a separate horizontal bar chart. This represen-

tation ﬁts the compact, row-based list of sources

well. A similar tag view shows tags available in

the database, and both views can be ﬁltered by using

Figure 4: The visual analysis component in conﬁdence

mode, where coloring and aggregation do not depend on

religious afﬁliation, but on an aspect of conﬁdence.

checkboxes. With the source view, users can quickly

get an overview of the sources used for the creation of

the evidence, or on how complete they are with regard

to the current state of knowledge in the ﬁeld. This

information is necessary to understand whether state-

ments can be made at all about certain time periods

and regions. The integration of sources allows their

sequential comparison to understand which author re-

ports on which religious groups and to what extent

such reports differ in terms of contained evidence.

Tooltips provide additional information about the

data represented by visual elements in all views. For

instance, hovering over a place name in the loca-

tion list shows the place’s geographical information,

location conﬁdence, comment, names in other lan-

guages, and references in other databases such as

Syriaca.org (Vanderbilt University et al., 2014) and

DARE (Ahlfeldt, 2015). Or, when hovering over a

map glyph, the evidences represented in that glyph is

shown (Fig. 1g) in a level of detail depending on the

number of evidences and the available space.

The visual analysis component can be switched

between showing ﬁltered data (the default) and all

data, where evidence that does not ﬁt the current ﬁl-

ters is included, but represented darker and less satu-

rated in color. Damast further offers controls to store

and load a visualization state, including ﬁlters and

view conﬁgurations, to the ﬁle system. Storing a re-

producible state of the visual analysis allows to share

ﬁndings with colleagues (R4), to pause and resume or

branch an analysis session, or to see data entry evolu-

tion for a speciﬁc analysis scenario at a later date.

5.3 Data Entry

Damast provides two core facilities for adding and

editing historical data. The ﬁrst is a table-based

editor, the second an annotation interface for work

in digitized and OCRed historical source material.

The data entry facilities also provide a convenient

place to intercept data edits and record the appropri-

ate provenance data (R3). Both facilities offer vi-

sual support to data entry: The map in the GeoDB-

Editor (Fig. 5 right) shows the geographical location

of entered places and makes inconsistencies apparent

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

Figure 5: A table and the map of the GeoDB-Editor. Se-

lecting a row in a table will show related information in

dependent tables. Entries can be added, deleted, and edited.

Here, information about Halab, today’s Aleppo, is shown.

to domain experts. By utilizing satellite imagery as

map material, historians could also in some cases ﬁnd

and verify the location of ruins of historical settle-

ments. In the annotator, data entry and the represen-

tation of evidences (red links in Fig. 6) are visually

represented too. Additionally, entered data is imme-

diately visible (R5) in the visual analysis component,

so historians can use the components in tandem to vi-

sually verify entered data and identify missing data.

GeoDB-Editor. The main facility for the historians

to enter data is the GeoDB-Editor (Fig. 5). Here,

entries can be added, deleted, and edited in tabular

form. The geographical location of places can also be

viewed and edited directly in a map. Tables that are

referenced from others show entries relating to the se-

lected row in the former table. For example, after se-

lecting the place Aleppo, evidence tuples for Aleppo

are shown in the evidence table, and a new evidence

entry would be associated to the place Aleppo. This

ties well into the historians’ workﬂow, as they often

enter multiple entries relating to the same place, or

multiple time spans for the same evidence. Drop-

down menus with possible entities’ names improve

usability and reduces mistakes during data entry.

Annotator. The annotator (Fig. 6) works on digi-

tized and OCRed historical text sources. Scholars an-

notate entities in the text, and associate them to either

a place, a religious group, a person, or some tempo-

ral information in the database. The annotations can

Figure 6: The annotator shows the document on the left

and the annotation editor on the right. Text passages can

be annotated and linked to entities; namely places, persons,

religious groups, or time spans. These representations are

then grouped to form evidences, visualized as red links.

then be linked together to form evidences (Fig. 6 left).

Suggestions for annotations are also calculated auto-

matically from existing place, religious group, and

person names in the database; and from existing an-

notations in the same document. For example, the En-

glish term “maphrian” used in some sources refers to

a rank of church ofﬁcial that only exists in the Syriac-

Orthodox Church. After annotating this text passage

once with that religious group, other occurrences of

“maphrian” will be suggested as potential candidates

for new annotations referring to the Syriac-Orthodox

Church, and can simply be selected to create the anno-

tation. Entering evidence into the database using the

annotator means that scholars can go back to relevant

text passages when inspecting a visual representation

of it in the visual analysis component (R3, R5).

5.4 Persisting Analysis Results

Exploratory analysis in Damast usually involves

drilling down into subsets of the data relevant to the

current research question. Damast supports sharing

such results between scholars as well as revisiting of

an analysis at a later time (R4). For one, the state of

the visual analysis component (i.e., the current set of

ﬁlters, the viewpoint of the map component and some

settings) can be downloaded at any time. Such a state

ﬁle can then be loaded into the visual analysis com-

ponent, recreating the respective state.

The historians required a way to persist analysis

ﬁndings in a print-friendly format, containing all data

and provenance in a static, non-interactive way (R4).

Damast therefore offers to produce a textual report on

a speciﬁc subset of data. A report includes metadata

stating the report’s source, the ﬁlters used to generate

it, and summary information about its contents. The

metadata also references the DOI for the version of

the dataset used to generate it, as well as the version

of Damast. This ensures that the same report—and

the visualization state it stemmed from—can be re-

produced later on. The report lists the pieces of ev-

idence individually; as well as the places, religious

groups, and persons referenced in these evidences.

The historical sources the evidences were obtained

from are listed (R3). All references to other entities

within reports are cross-referenced; for instance, in

Figure 7: An evidence (left) and place entry (right) in the

HTML report. Entities in the report are cross-referenced.

Damast: A Visual Analysis Approach for Religious History Research

an evidence in place Sumaysat (Fig. 7 left), the place

is linked and referenced, and likewise the evidence is

linked in the place summary (Fig. 7 right). Reports

can be viewed directly in Damast as a web page, or as

a PDF version that can be included as an appendix in a

publication. Each report gets a unique identiﬁer under

which the report can be retrieved indeﬁnitely. Using

the identiﬁer, it is also possible to go directly to the

visual analysis component in that state (R5, R3).

5.5 Workﬂows

The historians explore and restrict a multitude of data

facets in their analysis tasks. Damast supports this

with interlinked visual components (R2, R5) repre-

senting these facets. A number of frequent workﬂows

ﬁt into the foraging and sensemaking loops presented

by Pirolli and Card (2005). The historians enter data

from hard-copy source material using the GeoDB-

Editor, and from digitized source material using the

annotator (Section 5.3). These workﬂows ﬁt into the

read & abstract and schematize processes of the for-

aging loop. Using the powerful ﬁlters of the visual

analysis component (Section 5.2), the historians ex-

plore the available data, and might notice patterns or

irregularities from which they form hypotheses. Sim-

ilarly, the visual analysis component can be used to

quickly check hypotheses the historians have already

formed. These workﬂows ﬁt into the build case and

search for support processes in the sensemaking loop.

The historians might then want to share their ﬁndings

with peers, and create a report (Section 5.4) to persist

their analysis steps and the resulting data. This can be

seen as presentation, ﬁtting into the tell story process.

Damast supports the top-down processes pre-

sented by Pirolli and Card through its emphasis on

data and analysis provenance (R3) and facilitates the

retracing of steps via closely linked components (R5).

One workﬂow ﬁtting into the larger reality/policy

loop is the detection of missing or incorrect data dur-

ing exploratory visual analysis. The historians can

then re-trace the visualized data to the database, and

even back to the original sources, and subsequently

ﬁx and improve the data. Another top-down work-

ﬂow is to revisit an earlier analysis after more data

had been entered into the database.

6 USE CASE AND FEEDBACK

We worked in close collaboration with historians for

more than three years. During this time, both the data

entered and the functionalities of Damast evolved

and improved incrementally to support the scholars

in their research. In this section, we showcase an ex-

ample analysis that was made possible by this work,

describe our collaborative efforts, and highlight anec-

dotal success stories and feedback.

6.1 Example Analysis

One speciﬁc research question by our collaborators

is how the coexistence of three Christian groups—

the Church of the East, the Syriac Orthodox Church,

and the Armenian Church—evolved from the 7

the 12

century. Speciﬁcally, they are interested in

which cities more than one of these groups could be

found during this time. To ﬁlter for cities with at least

two of the groups present, they use the advanced ﬁl-

ter of the religion view with three sets (see Fig. 1a).

In the timeline (Fig. 1d), they then ﬁlter for a time

span, here the 8

century. The map (Fig. 1b) and lo-

cation list (Fig. 1c) show them the cities relevant to

their research question. Using the unclustered map

mode (see Figs. 2c and 2d) and moving the time-

line ﬁlter forwards in small increments, they can also

see the evolution of the coexistence over time. For

example, they see that coexistence is at its highest

around 1000 CE, and that the set of cities where those

religious groups coexisted slowly moves east over

time. Using the tooltips and the links back to the

data and sources, they can also retrieve more infor-

mation. With the conﬁdence view scholars can check

whether disputed evidences need to be considered,

while the source view indicates where the data was

retrieved from. Through reports, the ﬁndings can be

transparently shared with colleagues. This shows just

one exemplary analysis session. Depending on the

task and research question at hand, our collaborators

would use a different subset of the functionalities.

6.2 Observations and Feedback

The database grew from a few hundred pieces of his-

torical evidence in 2017 to over 10 000 in late 2022.

Our collaborators consider the data collection itself a

valuable outcome of the project. The historians used

lower levels of conﬁdence for data that needed review

by a peer and increased the levels after review. This il-

lustrates their collaborative workﬂow, which we could

support with possibilities for powerful ﬁltering (R2),

tracking conﬁdence and provenance (R3), and linking

data exploration and editing facilities (R5).

Feedback from the historians revealed many sit-

uations in which the interactive visualization and its

powerful ﬁlters (R2), as well as the integration and

linking with data entry (R5), helped them improve

their data. For instance, they found cases of dupli-

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

cate data entry visually, could retrace those to the rel-

evant entries in the GeoDB-Editor, and correct them.

In other cases, they noticed mis-entered data, such as

the wrong time span for an evidence, or a copy-paste

error in the coordinate for a place, visually and could

quickly amend the entries. We also received positive

feedback for the speed and comfort of data entry re-

garding the sanity checks and automatic suggestions

and completions in the GeoDB-Editor.

A challenge in our collaboration was to properly

support very speciﬁc research questions and work-

ﬂows by individual historians. In our regular meet-

ings, we discussed such issues and observed our col-

laborators’ workﬂow. We were often able to ﬁnd sit-

uations where tedious or error-prone tasks could be

greatly improved by visual interactive support. Other

speciﬁc research questions required complex logical

ﬁlters to facilitate analyses like that described in Sec-

tion 6.1. We extended the ﬁltering possibilities pro-

vided by Damast accordingly. We found that adding

such specialized features posed a risk of making the

approach too complex to learn and to use, or too in-

consistent in its behavior. Damast aims to counteract

that risk by deﬁning a behavioral archetype regarding

brushing and linking, and the multifaceted ﬁltering,

that all new features needed to adhere to; and by mak-

ing specialized features opt-in. Extensive documenta-

tion of behavior and features helped here as well, both

as a reference and to on-board new collaborators.

The historians stressed that they have already got-

ten various new insights, and also were looking for-

ward to incorporating and analyzing even more data

with Damast. One example insight concerns the plu-

rality of religious groups in cities of the medieval

Middle East. Previously, our collaborators’ consen-

sus had been that this plurality was always given. But

now, they could see that there were indeed highs and

lows in plurality and coexistence. Another hypothesis

they had already formed, but were not able to conﬁrm

previously, was that the bishopric seats of Christian

groups migrated from more rural areas into the cap-

itals in the 10

century. Previously, testing this hy-

pothesis would have required close reading of multi-

ple historical sources, and collection and organization

of partial ﬁndings. With the uniﬁed dataset contain-

ing evidences from many sources (R1), distant read-

ing with powerful ﬁltering (R2)—in this case the re-

ligion and timeline ﬁlter, alongside the tag ﬁlter for

the Bishopric tag—meant they could now conﬁrm the

hypothesis quickly and visually. A presentation of

Damast at the 53

Deutscher Historikertag in 2021

also received a large audience and positive resonance

including requests to use and adapt Damast for other

datasets and respective research questions.

7 DISCUSSION

We believe that Damast produced valuable outcomes

on different levels. During the collaboration, we faced

some challenges and limitations with our approach.

In the following, we discuss the lessons learned and

how our approach could be applied in other contexts.

7.1 Results and Limitations

The success of our approach can be ascribed, in part,

to the previous work of our collaborators, who had al-

ready put thought into what data to collect, and how

to formalize it. They were also already working vi-

sually, mostly with printed maps, and so the use of

interactive visual analysis was easy to introduce. Our

success was also facilitated by the nature and volume

of the data relevant for our collaborators’ research

questions: The complete dataset size is about 50 MB,

which still permits transfer of the entire dataset to the

visual analysis component, and hence to do a top-

down analysis with multi-faceted ﬁltering. For larger

datasets that could no longer be fully loaded in such

a way, the order in which ﬁlters were applied would

become essential for the reproduction of analysis re-

sults. In such a case, interaction logging, as proposed

by KnowledgePearls (Stitz et al., 2019) and Story-

Facets (Park et al., 2021), would become necessary.

To support the speciﬁc research questions of our

collaborators, we also had to ﬁnd bespoke visual so-

lutions. Consequently, we sacriﬁced some intuitive-

ness and ease of use in exchange for more expressive

visual querying. This also lead to a multitude of func-

tionalities, which individual workﬂows only utilize a

subset of. Damast now requires some practice to use,

and we had to write extensive documentation to sup-

port new users. For any sufﬁciently complex area of

research, visual support facilities will have their own

complexity. Damast in its current form is restricted

to the analysis of textual evidences for religious com-

munities, but we argue that the general concepts and

strategies applied in our approach are still extendable

to other areas of research that rely on historical ﬁnd-

ings in source texts. We also had to ﬁnd compro-

mises between best practices in visual analysis, and

the workﬂows and technologies familiar to the histo-

rians, for example when deciding on color hue as an

encoding for religious afﬁliation (see Section 5.2).

While we increased the transparency and trace-

ability of analysis results, additional aspects need to

be considered for complete reproducibility. To as-

sess the results, it is necessary to understand on which

information the ﬁndings are based. To achieve this

transparency, we published the dataset to a long-

Damast: A Visual Analysis Approach for Religious History Research

term data repository (Weltecke et al., 2022a) and en-

sured that the used sources are made transparent in

our analysis environment. We published the code of

Damast (Franke and GitHub contributors, 2022) to

ensure that interested researchers can assess the visu-

alizations themselves, and to make the code available

for reuse. In generated reports, we specify the ver-

sion of the software and the version of the data from

the long-term repository. Coupled with the linking

between results, visual analysis, and data in our ap-

proach (R5) this means that not only the same report

and analysis results can be reproduced, other scholars

can even go back to the source references. With that,

even the interpretation of the source material can later

be reproduced and understood by other scholars (R4).

7.2 Applicability Beyond the Use Case

Since data creation was an inherent part of the project,

our collaborators were aware that mistakes in this step

would reﬂect in the visual analysis. Hence, they had

a critical stance to what was visualized, which moti-

vated the support of tracking back visual artifacts to

the data and the sources to help verify all data aspects

depicted in the visual representations (R3). Further,

the historians in our project work with textual source,

some of which were even available as digitized texts.

These circumstances facilitated the collaboration and

contributed signiﬁcantly to the project outcome.

Despite these project speciﬁcs, we believe that

some of the lessons learned, approaches used, and

workﬂows developed can be transferred to other DH

projects concerned with the spatio-temporal aspects

of entities described in textual sources. Conﬁdence

and incomplete or biased data sources are typical

traits of historical data and are relevant in many DH

research endeavors to improve the visual analysis.

Projects working with appropriately formalizable data

could achieve a similar coupling of sources, data,

and its evaluation and correction based on exploratory

analyses. One collaborator already proposed to use

Damast for the spatio-temporal analysis of monastic

orders in medieval Europe. With similar high-level

research questions and data schemas, Damast could

be reused here virtually unchanged.

7.3 Lessons Learned

A repeating point of discussion in our collaboration

was the level of abstraction and aggregation used in

interactive visualization. We initially underestimated

the information density possible on printed, semi-

automatically created maps, which our collaborators

were familiar working with (see R6). The use of in-

teractive visualization and an overview-ﬁrst approach

to support the lower resolution of computer screens

was, therefore, unusual to them. Over time, we found

good compromises in our approach that could sup-

port both historians more comfortable with traditional

workﬂows, and those wishing to double down on the

gains of interactive visual analysis. The difference in

resolution also became a driving force in the develop-

ment of the reporting functionality and the publication

of analysis results (R4) in a static, serialized and non-

aggregated manner. A core objective of our approach

is to unite the entire data life cycle in one place. We

could observe the beneﬁts that arose from visually ac-

companying data entry: Outliers or errors could be

noticed earlier, and the overall iterative improvement

of the data situation was accelerated. Repeated feed-

back from the historians indicates that seeing the data

quality and quantity improve in real time motivated

them and gave them a sense of accomplishment.

We also found that the historians used the facili-

ties to annotate data with free-text comments exten-

sively. They entered additional information about the

entity, how they interpreted it, why they included it,

and more. This metadata gives a deeper insight into

the data and provides an additional way to identify

data that needs to be reviewed. Other approaches pro-

vide facilities for dataset annotations (Shrinivasan and

van Wijk, 2008), but our pragmatic approach already

offers many beneﬁts with little setup cost.

The close collaboration in our design study was a

driving factor in its success, which matches the ﬁnd-

ings of Bradley et al. (2018). Regular meetings and

workshops helped convey domain knowledge in both

directions and accelerated the iterative development.

8 SUMMARY AND OUTLOOK

Damast enables historians to explore and analyze the

coexistence of religious groups in cities of the me-

dieval Middle East. Our approach covers the interac-

tive visual analysis as well as the manual formaliza-

tion and assessment of such data and its relation to the

sources it was retrieved from. By storing these rela-

tions and keeping track of analysis steps; Damast fa-

cilitates collaboration, makes ﬁndings traceable, and

supports scholars in publishing ﬁndings with serial-

ized textual reports. With the linked components

we contribute to a better reproducibility of insights

gained with the help of DH methods that we believe

is applicable beyond our concrete design study.

Future work could consider situations where

overview-ﬁrst approaches are prohibitive due to data

size and offer more sophisticated analysis opera-

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

tions including advanced support for data compari-

son. Other backends including knowledge graphs and

additional data acquisition procedures could also be

incorporated into our approach.

ACKNOWLEDGMENTS

This work has been funded and supported by the

Volkswagen Foundation as part of the Mixed Meth-

ods project “Dhimmis & Muslims”.

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IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications