musiXplora: Visual Analysis of a Musicological Encyclopedia
Richard Khulusi
1
, Jakob Kusnick
1
, Josef Focht
2
and Stefan J
¨
anicke
3
1
Image and Signal Processing Group, Leipzig University, Leipzig, Germany
2
Museum of Musical Instruments, Institute for Musicology, Leipzig University, Leipzig, Germany
3
Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
Keywords:
Biography Visualization, Music Visualization, Digital Humanities.
Abstract:
Making large sets of digitized cultural heritage data accessible is a key task for digitization projects. While
the amount of data available through print media is vast in humanities, common issues arise as information
available for the digitization process is typically fragmented. One reason is the physical distribution of data
through print media that has to be collected and merged. Especially, merging causes issues due to differences
in terminology, hampering automatic processing. Hence, digitizing musicological data raises a broad range
of challenges. In this paper, we present the current state of the on-going musiXplora project, including a
multi-faceted database and a visual exploration system for persons, places, objects, terms, media, events, and
institutions of musicological interest. A particular focus of the project is using visualizations to overcome
traditional problems of handling both, vast amounts and anomalies of information induced by the historicity
of data. We present several use cases that highlight the capabilities of the system to support musicologists in
their daily workflows.
1 INTRODUCTION
Traditional musicology has developed into a field
with several sub-domains. They differ by their main
object of interest and also by their view on the data.
Examples are instrument making, dealing with the
physical production and often also restoration of in-
struments, or organology (Tresch and Dolan, 2013),
concentrating on methods of research, teaching, and
documentation of instruments. Further sub-domains
are prosopographical analysis, focusing on the per-
sons associated with musicology (biographical re-
search) rather than instruments. Additionally, some
musicologists focus on inspecting developments of
and influences on places important for music his-
tory. Thus, in musicology, many different types of
entities are encountered. Typically, these are classi-
fied as persons, (musical) objects, institutions, places,
terms, events, media, and titles. This range of enti-
ties, combined with different sub-domains, leads to a
vast amount of musicology data to be handled. Espe-
cially, use cases and research questions with restricted
focuses subdivide the field into well-researched and
less-researched fragments of data. Tools trying to
connect those data fragments are few, because tra-
ditional musicological approaches either do not need
to get a comprehensive picture of the whole musico-
logical knowledge—knowing in which location an in-
strument was produced being of less interest for a re-
storer compared to the three-dimensional instrument
model—and handling the vast data is hardly possible
by traditional means.
In this paper, we give give an overview of an interdis-
ciplinary collaboration between visualization scholars
and musicologists, aiming to link the different frag-
ments of musicological knowledge and offering them
to the broad public with the help of an online ex-
ploration tool—the musiXplora—, supported by dif-
ferent visualizations to allow usage for both experts
with specific research questions at hand as well as for
casual users interested in browsing musicological in-
formation. A screenshot of the system is shown in
Figure 1. As part of the digital humanities, we also
want to highlight development of a digital tool in the
field of musicology, leading to both, new needs and
interests in research of The Musicology as a single
and comprehensive field, and also possibilities aris-
ing through deploying computer science technology
and digitization, especially for accessing and linking
vast amounts of data.
2 RELATED WORK
Throughout the last two decades, a multitude of visua-
76
Khulusi, R., Kusnick, J., Focht, J. and Jänicke, S.
musiXplora: Visual Analysis of a Musicological Encyclopedia.
DOI: 10.5220/0008977100760087
In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theor y and Applications (VISIGRAPP 2020) - Volume 3: IVAPP, pages 76-87
ISBN: 978-989-758-402-2; ISSN: 2184-4321
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: The musiXplora showing different visualizations of instrument makers in the German city of Markneukirchen.
lization projects for musicology have been con-
ducted (Khulusi et al., 2020). Through these, dif-
ferent sub-domains and entities of musicology have
been analyzed with the aid of visual tools. Some
projects focus on a prosopographical inspection of
persons. While generic visualization tools for per-
sons (Leskinen et al., 2017) are used to show
domain-independent features such as networks (Lu
and Akred, 2018; Miller et al., 2012; Gleich et al.,
2005; Vavrille, 2017; Crauwels and Crauwels, 2018),
places (Doi, 2017) or temporal data (Andr
´
e et al.,
2007), musicological research often requires domain-
specific, contextual information. For example, re-
garding musical instruments (objects), typical visu-
alizations use three-dimensional data, gained through
CT (Borman and Stoel, 2009; den Bulcke et al., 2017;
Hopfner, 2018) or other techniques (Heller, 2017;
Konopka et al., 2017) to generate volume or surface
renderings (Tuniz et al., 2012). But the sub-domain of
organology does not only focus on the physical prop-
erties of instruments, other research questions are di-
rected toward contextual metadata like dates, places,
and ownerships of instruments, usually not processed
by these tools. Not only do single sub-domains suffer
from a too strict focus on a small set of information
for a specific kind of entity, but also from compre-
hensive and domain-wide inspection of data not being
provided. First ground building work for enabling a
more global resource of musicological knowledge has
been done by the German research project BMLO.
2.1 BMLO
Short for Bavarian Musician Lexicon Online (Bay-
erisches Musiker Lexikon Online) (Focht, 2006), the
project consists of a database and a website. A
group of musicological redactors crawled different
analog and digital resources (manually) and put them
into a standardized and scalable relational data base,
suitable for an automatic processing. Due to the
long history of musicology—and missing standards—
a (semi-)automated processing of data is mostly only
possible after manual preprocessing. The data col-
lected in this process includes the seven categories
defined by the musicologists of the project: persons,
objects, places, institutions, terms, media, and events.
In the active years of the BMLO project (2004–2014)
the number of persons in the data set rose to around
30,000, while other categories (institutions, terms,
media, ...) were mostly only drafted. Nevertheless,
a frontend offers a search interface and list-like pre-
sentation of the collected data, enriched with pic-
tures crawled through Wikimedia. Next to typical bi-
ographical data, entities also list their identifiers on
different domain-related websites, like VIAF
1
, GND
2
or Q
3
(Wikipedia’s/Wikimedia’s identifier), building
a linkage between the BMLO and other online knowl-
edge sources. In 2014, the BMLO project moved
from Munich to Leipzig University and was extended
to the musiXplora project, abolishing the focus on
Bavaria and collecting generally available data of mu-
sicology. A further focus of the project was the inclu-
sion of various interactive visual interfaces aiming to
support a broad range of musicological research tasks.
1
www.viaf.org
2
www.dnb.de/DE/Professionell/Standardisierung/
GND/gnd node.html
3
www.wikidata.org
musiXplora: Visual Analysis of a Musicological Encyclopedia
77
3 DATA
Like the BMLO, the musiXplora database (Focht,
2019) consists of the seven facets of musicology,
divided into different repositories and is editori-
ally maintained in the Musikinstrumentenmuseum der
Universit
¨
at Leipzig
4
. Each repository appears in its
own color, used in the header, on Single Result Pages
as well as in the visualizations. Through the usage of
the BMLO data, challenges from the former project
were also inherited. These include the work with his-
torical, uncertain data, a vastness with potential for
distant-reading, but challenges for searching, filter-
ing and finding of specific entities, and the missing
of standards for general terms of musicology.
3.1 Musici—Persons
Musicologists may be interested in persons, associ-
ated in creating music—like musicians, composer or
singers—, but also in people with professions like
music instrument makers or restorers. The people
associated with music are collected in the Musici
(person) repository. Persons m
1
, m
2
, ..., m
n
, with n >
32, 000 offer a multitude of metadata for each musi-
cian m
i
:
1. m
Name
i
—Different written variants for first and
last name, as well as additional names like
pseudonyms or maiden name
2. m
Date
i
—Temporal Data - Life and work years in a
range of [0,2019] A.D.
3. m
Con f ession
i
—List of Confession(s)
4. m
Gender
i
—Gender(s)
5. m
MusicalPro f essions
i
—List of musical professions
6. m
OtherPro f essions
i
—List of non-musical professions
7. m
Branch
i
—List of types of the branch(es) that em-
ployed the person like state, court or military
8. m
c
i
—List of musical institutions c
j
, ..., c
k
the per-
son belonged to
9. m
l
i
—List of places l
j
, ..., l
k
divided by type [Birth,
Death, Work, ...]
10. m
IDs
i
—List of IDs used in related repositories of
musicological knowledge [VIAF, GND, Q, ...]
11. m
Links
i
—List of links using the IDs to reference
other resources containing important data of the
person
4
https://mfm.uni-leipzig.de/
3.2 Casae—Institutions
Unions of persons are captured in the Casae reposi-
tory (Institutions), such as opera houses, court orches-
tras or festivals. For each institution c
1
, ..., c
n
informa-
tion, including the following, are given:
1. c
m
i
—List of members m
j
, ..., m
k
2. c
Name
i
—Name of the institution
3. c
Date
i
—Founding and closing date
4. c
l
i
—List of locations l
j
, ..., l
k
While, compared to the persons, the range of meta-
data is rather low, the connectivity from institutions
to persons and locations leads to great possibilities
for distant reading analyses of the institutions itself or
more general trends between multiple ones (Khulusi
et al., 2019).
3.3 Loci—Places
While not directly related to music, places are a fur-
ther aspect of interest for musicologists. As seen
above, places are for example included in data sets
of persons and institutions. Especially for centuries
prior to globalization and fast traveling possibilities,
different places have been centers of agglomerations
(see Section 5.2). The information for places l
1
, ..., l
n
,
with n > 76, 000, includes production centers for spe-
cific instruments or centers of performance, for exam-
ple at courts. Like the repositories above, given infor-
mation includes l
Name
i
, l
c
i
, l
IDs
i
, l
Links
i
, and l
m
i
. Further,
topological information is given hierarchically with
l
Hierarchie
i
, linking e.g. l
i
Germany to l
x
Europe as
parent and to the German states l
y
, ..., l
z
as children.
More information is given with e.g.: l
Coordinates
i
—in
the form of longitude and latitude coordinates—or
with linkage to events l
e
i
.
3.4 Baccae—Objects
A further important part of music and musicological
research are the objects needed for or produced by
music (Baccae), including instruments and composi-
tions. b
1
, ..., b
n
have properties about the label b
Name
i
,
a categorization leading to a Res-entry r
j
, describing
textual data b
Description
i
and events b
Events
i
linking an
object to places l, persons m, temporal data b
Date
i
and
type of event b
Type
i
.
3.5 Res—Terms
The repository Res includes terms of musicology.
While normally dictionaries exist that help in under-
standing terms and offering descriptions, musicology
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
78
has the unique issue that a lot of terms are uncer-
tain, thus, leading to challenges for automated pro-
cessing (Khulusi et al., 2020; Kusnick et al., 2020).
This follows from changes in the meaning of terms
throughout the time and almost no endeavors to stan-
dardize terminology. While in certain centuries a spe-
cific string instrument may have had ve strings, it
may have been overhauled in later years and adopted a
six-string construction, still keeping its original name.
This leads to the need for more context information
to analyze written musicological information and (es-
pecially for automatic processing) issues in merging
different data sources. Due to these issues, that are
still aspects of the present research, the Res repos-
itory tries to collect different descriptions under the
same name. For this purpose, r, as the set of r
1
, ..., r
n
,
contains r
Variants
i
—labels in different languages or
synonyms—, describing elements r
Description
i
, lists of
objects of this kind r
b
i
and links to other resources
holding information about this kind of term r
Links
i
.
3.6 Catalogus—Media and Titles
While Baccae contains physical objects, the digital
representatives of objects (books’ metadata, objects’
3D data or contents of CDs or Books) are collected in
Catalogus. While this repository is to be named in the
collection of repositories, it does not have a sufficient
state of research to be further discussed in this paper.
3.7 Eventa—Events
As seen before, Eventa entries e
1
, ..., e
n
offer connec-
tions between the different facets. Each event e
i
con-
tains information about its type e
Type
i
, temporal com-
ponent e
Date
i
, associated person e
m
i
or object e
b
i
and
place e
l
i
.
4 FEATURES
With the goal to offer a digital knowledge base for
musicology, the musiXplora has different core fea-
tures, taking inspiration from traditional sources and
enriching them with computer science technology.
These goals—or tasks—fit into Munzner’s Task Clas-
sification Scheme (Munzner, 2009).
4.1 Searching
The most obvious feature is offering a faceted search,
enabling a multitude of ways to find entities. Mun-
zner’s second task category Searching categorizes
Figure 2: Four different example searches for persons. Dif-
ferent kinds of data (token, ID or date based) and different
logic operators can be used.
such tasks by the awareness of target and its location.
The musiXplora supports all four subtasks. On the
one hand, a known entity can be found with the use
of the specific entity’s x
n
features x
f
n
(like x
Name
n
or
x
ID
n
), called Lookup or Locate. On the other, a search
rather detached from a specific entity can be started
by searching for feature-value pairs with high recall
like the century of birth or profession (Exploring or
Browsing). Locate and Explore are defined with an
unknown location of the target. For our search, this
is reflected by a search not resulting in a single en-
tity x
i
but a set of results x
1
, ..., x
n
, with n > 1. For
such scenarios, the next section will provide further
information (see Section 4.2).
Additionally, for the search-ability, wildcards and
logic operators are included, helping in defining rel-
evant parts of values (e.g. ranges of dates or groups
of entities). A so-called Simple Search is offered as
default. This matches the inserted search tags to a
predefined reverse index containing all IDs to each
possible tag of an entity. This reverse index allows
a quick search and high recall, while the precision
is rather low, as tags may be ambiguous (a tag may
be the name of a person or a location). For cases
where the user needs a high precision or knows ex-
actly what to look for, a combination of type and to-
ken can be searched for. In simple cases, this may be
First Name and Wolfgang. Different inputs may also
be combined. A search for First Name: Wolfgang%
and Place of Work: Vienna gives 20 results (compared
to 243 for just Wolfgang). Also, logical operators like
AND (space), OR (Pipe) and NOT (Tilde) are possi-
ble and wildcard operators (percentage mark for mul-
tiple chars and underscore for a single char) are of-
fered. Figure 2 shows four different example searches
by users of the musiXplora. Different use cases exist,
where this kind of search is not sufficient to find the
entity needed. May it be caused by only uncertain
knowledge of the entity (the name only being phonet-
ically known, but not in the written form), only vague
knowledge or an indirect search interest. This leads
musiXplora: Visual Analysis of a Musicological Encyclopedia
79
Figure 3: A timeline of working times (darker blue), en-
riched with living time (lighter blue). Different shapes indi-
cate temporal uncertainties in the data.
to searches having high recall and low precision (e.g.
a search only by the place of work, often resulting
in multiple thousands of results). In analog dictio-
naries, this problem also exists, but on a lower scale,
as such directories mostly return a much smaller data
set and do not offer interactivity to follow up on such
a search. This is caused by limited space and also
by most directories being specialized on a specific
set of entities (e.g. musicians or instrument makers).
Hence, with a wider set of data this problem is more
crucial. To tackle this issue already encountered in the
BMLO, distant- and meso-reading visualizations are
included in the musiXplora, helping in distinguishing
the entities returned in a search.
4.2 Visualizations, Overview and Filter
These visualizations depict the distribution and range
of features for all found entities. The inclusion of
those is a real improvement of the musiXplora in con-
trast to the rather dictionary-like implementation of
the BMLO and hold great value for the users of the
system. In general, all visualizations follow Shnei-
derman’s Mantra (Shneiderman, 1996) and offer a
full overview as default and means for filtering the
data set on demand. For the above-mentioned search
of First Name: Wolfgang% and Place of Work: Vi-
enna the following visualizations will be offered. For
temporal data, a timeline is used, dynamically adjust-
ing to the range of dates and stacking entities above
each other. These timelines communicate the uncer-
tainty of dates with shapes (arrow-like for time ranges
and straight edges for time stamps) and a differen-
tiation between lifetime (low saturation) and docu-
mented years of work (high saturation) as seen in Fig-
Figure 4: Glyph based map, showing different kinds of
places with different colors. Heavily overlapping circles are
aggregated to pie charts. The digit indicates the number of
aggregated entities.
Figure 5: Different examples of pie charts and sunbursts to
grasp the distribution of features in the search result.
ure 3. Geo-spatial data is shown in a glyph based map,
where the different types of places—like the place
of birth/death/work/... for persons and the construc-
tion/restoration/changing ownership/... for objects—
are coded with different colors. The problem of over-
lapping by giving a full overview is met with ag-
gregating glyphs. To combine glyph and color ap-
proaches, each glyph aggregating more than one en-
tity is depicted as a pie chart, showing the distribution
within. Figure 4 presents dozens of places of work in
Europe, with a couple in America and a single occur-
rence in the Middle-East for our example search.
We also use pie charts’ ability to convey distribu-
tions to show more general feature distributions like
seen in Figure 5. For this, multiple pie charts are
added as standalone visualization at the bottom of the
page. For persons m
i
, ..., m
j
, these may be different
m
MusicalPro f essions
n
, m
NonMusicalPro f essions
n
, m
Institutions
n
,
m
Con f ession
n
, m
Gender
n
and others. Similar, for the other
repositories, different pie charts show distributions of
their specific features. For some of these features, hi-
erarchical information is given. All places have topo-
logical information upwards and downwards (earth -
continent - country - state - district - city - quarter)
placing them in a hierarchical context. Also, profes-
sions (e.g. Singer - Soprano/Alto/Tenor/...), Confes-
sions, and more are given hierarchically. As pie charts
do not allow for hierarchical information, we adopted
the sunburst plot for these features. Figure 5 shows
example pie charts for the search above and the mu-
sical professions as sunburst in the lower-left corner.
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
80
Figure 6: A force-directed graph with each person of the
result being initially placed on the center of the y-axis and
linearly distributed on the x-axis. Lighter nodes indicate
persons added as context and not being part of the actual
result.
Technically, the segments on the inner ring are dis-
tinct categories (singer, instrument maker, ...) and, if
available, child elements will span on an outer ring,
bearing the name (soprano, alto, tenor, ...) and the
amount of those sub-types. Although deeper hier-
archies are available, we only allow for up to three
subsections, as each subdivision reduces the available
space for labels drastically, which can also be seen in
Figure 5.
In the case of relational data, a network graph
with a force-directed layout is included (see Figure
6). Typical for this kind of visualization, dots rep-
resent entities and relations among them are shown
by connections. Color is used to communicate dif-
ferent types of relations. In contrast to the other vi-
sualizations, we add contextual data in the visualiza-
tion that is not included in the actual search result.
This helps in getting insight into an entity’s connec-
tivity. Such contextual information has the form of
additional nodes mc
0
, ..., mc
m
showing the first level
neighbors of the observed entities. Additional enti-
ties have a reduced saturation and clicking is deacti-
vated, which all other visualization elements provide
to select a single or a range of results for filtering. As
observable in the different figures above, some issues
with the visualizations still exist. Due to the usage of
multiple visualizations at once to give an exhaustive
overview of the search results, less space is available
for each single visualization. While this lowers the
level of detail (e.g. as visible in Figure 5 in which too
small labels are hidden), we tackle this problem by
enabling interactions like tool tips containing full in-
Figure 7: Single Result Page of Wolfgang Amadeus Mozart,
listing different name variants, dates and further biographi-
cal information (left) and visualizations for these (right).
Figure 8: Start Page Visualization of Musici showing the
persons with dates of birth or death at the current day.
formation (all labels for a hovered pie chart slice, all
places aggregated under a map glyph, ...). For tasks
requiring a more detailed inspection of results, we of-
fer alternative visualizations.
4.3 Finding
After the searching part, the user may click on a single
entity x
i
or filter until only a single one is left. This
can either be done in the result list or through one of
the visualizations. The right side of the page is now
changed to a Single Result Page (Figure 7). Here, all
available information listed in the data section (Sec-
tion 3) are shown in textual form.
Differently from the previous pages, we now de-
ploy more close-reading like visualizations, concen-
trating on the selected entity x
i
and offering a com-
prehensive overview. These visualizations are placed
next to the textual information as small visualization
previews. On demand, a user can click on each vi-
sualization to get a full view (seen later in the use
case Section 5). This presentation allows for more
details due to focusing on a single visualization at a
time, in contrast to multiple ones in the searching sec-
tion. While geo-spatial data of persons, places, and
objects are again shown on a glyph based map and
relations using a graph, novel visualizations are for
example deployed for institutions. To allow explo-
ration of temporal developments of an institution An
Interactive Chart of Biography (Khulusi et al., 2019;
Khulusi et al., 2018) is included for each institution.
This visualization links membership data of the in-
stitution to a time-axis and offers distant-, meso- and
close-reading views on the data, allowing it to be used
for a broad range of scenarios and research questions
regarding temporal developments of institutions. All
included close-reading visualizations differ not only
through the focus on a single entity but also in terms
of interaction. While their distant- and meso-reading
counterparts allow filtering and selecting for finding
entities of research interest, the Single Result Page
musiXplora: Visual Analysis of a Musicological Encyclopedia
81
focuses on linking entities database-wide. As an ex-
ample, all person’s locations m
l
i
link directly to their
representation in the Loci places directory, indicated
by a coloring of the label in the repository’s color
on mouse-over. Hence, quick navigation between the
repositories and entity category is enabled on a high
level of detail, which will be discussed in-depth in
Section 5.
4.4 Browsing
Besides Searching and Finding, an explicit Browsing
of the data is also offered. While this is less useful for
specific use cases and research inquiries, it offers in-
teresting and easy access to the data, especially useful
for casual users. To support this task, the start page of
each repository offers a Start Page Visualization, be-
fore a search can be performed. The visualized data
is selected according to the opened repository. For
persons, those are shown having the date of birth or
death at the current day (see Figure 8). For places, of-
ten searched entities are shown, or a random selection
for all other repositories. The different visualizations
include a force-directed graph layout for persons, ob-
jects, institutions and media, a tag cloud for terms, a
map for places and a timeline for events. If available,
entities are grouped, e.g. persons being grouped by
the type of anniversary, institutions by their location
(on a city level) and objects by their instrument type.
Figure 9: Single Result Page for Ludwig van Beethoven, as
only result for the string ”Beethoven” for a Simple Search.
5 USE CASES
During our collaboration with musicologists, we ob-
served their use and utilization of the musiXplora. For
this, multiple evaluation meetings where held, where
needs and general feedback of the main collaborating
musicologist were collected and discussed. Later, we
observed daily workflows of musicologists of the Mu-
sic Instruments Museum of Leipzig University and
how the musiXplora was used as a research tool. As
Figure 10: The network of Beethoven as visualization
(right), enriched with a categorized listing (left). Instrument
maker Nanette Streicher-Stein is selected and highlighted.
expected, the tool itself did not show a specific use
case of how it is used, but rather a multitude of dif-
ferent uses for the (different) experts. To elaborate on
this, we discuss three different use case types.
5.1 Information Lookup
The most basic use case observed by musicologists
working with the tool was a simple information
lookup, leading to a more specialized question. In
the discussed case, the musicologist wanted informa-
tion about Ludwig van Beethoven’s instrument mak-
ers and associated instruments. For this, the expert
started by querying the search interface. Through this
search, we only get a single result, as Ludwig van
Beethoven is the only person with the tag Beethoven
in the database. As a well-known person in musicol-
ogy, the resulting site shows a well-researched state
and gives access to a multitude of information as seen
in Figure 9.
In the next step, the researcher observed the social
network of Beethoven. Figure 10 shows the entire
network, accessed through clicking on the network
Figure 11: Single Result Page for the instrument maker
Nanette Streicher-Stein.
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
82
Figure 12: Baccae Single Result Page for a ”Ham-
merklavier”, produced by Streicher-Stein and located in the
Musikinstrumentenmuseum der Univerist
¨
at Leipzig (Music
Instrument Museum, Leipzig University).
graph preview on the right side of Figure 9. In this
close reading visualization, different colored edges
differentiate the various kinds of relations. Through
this, the user finds famous instrument makers like the
married couple Johann Andreas Streicher and Nanette
Streicher-Stein (highlighted) as part of Beethoven’s
network. A click on the latter leads us to the Sin-
gle Result Page of Streicher-Stein seen in Figure 11.
Next to the information about her relations, we also
have access to information like different name vari-
ants known for her, in this case, her other first names
Maria and Anna. Further, her professions list piano
player, piano and organ maker, and more. We also
get her informal and contemporaneous titles, like ”Di-
rector of the Piano-Factory”. Below the green bio-
graphical entries, we find the list of links to exter-
nal sources in red, including Wikipedia, Wikimedia,
BMLO, MusikerProfiling (J
¨
anicke et al., 2016), AKS
and further important sources for musicology. With
the help of the also listed Musical Instrument Muse-
ums Online (MIMO), we can query the centralized
database of instrument museums and their informa-
tion about Streicher-Stein with a single click. This
leads us to a list of instruments produced by her.
One of these instruments can be seen in the screen-
shot of the Baccae repository of the musiXplora in
Figure 12, presenting a brief overview of the instru-
ment with the hint that the object’s page is still un-
der construction (which is the cause why the instru-
ment is not yet directly linked on Streicher-Stein’s
page). Further, we can access a list of all instruments
by Streicher-Stein exhibited in European museums.
This list consists of only a dozen pianos and con-
tains only known and documented instruments made
by Streicher-Stein. While the actual number of pro-
duced instruments was without a doubt higher, the
number of instruments produced per city was signifi-
cantly lower than in the following centuries.
5.2 Interest Browsing
The prior use case on a famous instrument maker
prior to the 19th century arouse the musicologist’s in-
terest in the development of the profession. He looked
at the general state and development of instrument
makers appearances in the database and filtered for
only those that had a place of work in Vienna, which
includes Streicher-Stein (see Figure 13, top). For
higher precision, the expert then included only those
persons with the main place of activity in Vienna (see
Figure 13, bottom). An increase of the number of
instrument makers around the change of century can
be seen. Having musicological expertise, the expert
concluded that the beginning of the Industrial Revo-
lution (in Germany around 1815-1835) brought fun-
damental changes to the production, availability and
the demand of music instruments in Europe, causing
the beginning of a reshaping of the profession from
straight craftsmanship to mass production. Further,
the expert reflected hypotheses on abstract concepts
influencing musicology, although not directly linked
to it but rather to social or cultural aspects. Simul-
taneously to their existence, tools and means of vali-
dation and showing such concepts are missing in tra-
ditional musicology and a clear need for such tools
exists. In recent years, first visualization approaches
dealt with such abstract concepts like chauvinism or
historism (Khulusi et al., 2019) and how they are re-
flected in musicology.
After the browsing of instrument makers in Vi-
enna in the 18th century, the musicologist directed his
investigation towards the influence of the Industrial
Revolution on instrument making in Central Europe.
The hypothesis is an increased demand for mu-
sic instruments due to easier transportation means and
lower prices of instruments, caused by a combination
of easier accessibility of raw material and division
of labor (not all parts of the instruments were con-
structed by the same instrument maker). Figure 13
supports this assumption for Vienna, as the number
of instrument makers increased at first, which can be
seen as an indication of an increase in demand. Fur-
ther, a view on the instrument makers in Central Eu-
rope in general (Figure 19) shows that the rising trend
continues throughout this time. Hence, the visualiza-
tion of the data supports the hypothesis without refer-
ring to instruments, for which currently data is rarely
Figure 13: Timeline showing all instrument makers with
a general place of activity (top) or main place of activity
(bottom) in Vienna.
musiXplora: Visual Analysis of a Musicological Encyclopedia
83
Figure 14: Result for the search of all persons with the main
place of activity in F
¨
ussen or Mittenwald.
at disposal. Thus, using the occurrence of instrument
makers to deduct trends for the instruments turned out
to be a working strategy of the expert.
Next to such economic developments, the Indus-
trial Revolution brought a wide set of technological
advances shaping the every-day life. An example is
given with the invention and expansion of the train
and the train network. This led to a change in the local
production center’s location characteristics. Centers
of high influence and renown like Vienna lost their
standing if they did not ”jump on board” of those ad-
vances.
Especially traditional centers like F
¨
ussen and Mit-
tenwald in Bavaria suffered from a late connection
to the German and Central European train network.
In Figure 14, all persons whose main working place
was in at last one of these two cities are shown. The
temporal development shows that the production cen-
ters were in a growth phase that abruptly stopped
with the beginning of the Industrial Revolution in
Germany (ca. 1815-1835). Close to the German-
Austrian border, both locations were close to impor-
tant courts and cities at the time and their proxim-
ity to rivers (Isar and Lech, respectively, both join-
ing, the Donau) allowed early transportation means.
The 19th century decrease of instrument makers in
F
¨
ussen and Mittenwald, that is visualized, is accom-
panied with an increase of instrument makers in other
cities, e.g., Markneukirchen at the German-Czech
border. Markneukirchen suddenly developed to one
of the most important centers in Germany, produc-
ing all kinds of instruments and even shipping them
Figure 15: Top of the Single Result Page of Loci for
Markneukirchen.
Figure 16: Overview of all instrument makers with main
place of activity in Markneukirchen.
intercontinental. Figure 15 shows the Loci page for
Markneukirchen, giving us direct access to all persons
with either main place of activity (Figure 16) or gen-
eral place of activity in the city (Figure 17).
In Figure 18, the temporal development of the
three locations is juxtaposed. Instrument makers of
F
¨
ussen and Mittenwald are shown at the top, and the
ones having Markneukirchen as main place of activity
are shown at the bottom. Additionally, this transition
of instrument makers from former important centers
to new ones is supported by a view of all instrument
makers in Europe. It shows only a shallow differ-
ence in the total number of instrument makers (see
Figure 19). A nearly constant amount, paired with a
decrease in traditional centers, indicates a transition
of locations. In the second half of the 20th century,
a sudden drop in the number of instrument makers
is seen for Markneukirchen and Europe in general.
One reason is that fewer people are included in the
database in the past decades as people are mostly too
young to be having a meaningful impact on musicol-
ogy. Nevertheless, actual events also reinforced this
development. Economically, the advance of division
of labor and political nationalization in the German
Democratic Republic lead to a vanishing of instru-
ment makers’ names as a kind of brand.
Figure 17: Overview of all instrument makers with place of
activity in Markneukirchen.
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
84
Figure 18: Match up of all persons with the main place of
activity in F
¨
ussen or Mittenwald (top) or Markneukirchen
(bottom) over a synchronized time-axis.
5.3 Exploration
Casual users typically access a repository through its
Start Page Visualization (see Section 4.4). We take a
look at popular places in Loci, the location dictionary.
Figure 20 shows a few randomly selected places, from
users’ frequently accessed places. In the image, we
can see places in the USA, Russia, Ukraine, Czech
Republic, Germany, Italy, France, and Spain. Inter-
ested in the places of southern Germany, Swiss, Aus-
tria and Italy, we zoom in to get a more precise view
on the glyphs (Figure 21). From this selection, we ac-
cess the Single Result Page of Milan in northern Italy
(Figure 22). Next to the precise topological informa-
tion of Milan, located in Lombardy, we can see all
links to persons through their different kinds of activ-
ity in the city and a single institution being located
here. Next to the listing of entries associated with
the links, we can also switch to the Musici (person)
repository by selecting an activity of interest. To get
an impression on the people strongly belonging to Mi-
lan, we select the Main Place of Activity label with its
24 results.
Musici’s overview page of these 24 persons con-
sists of the visualizations seen in Figure 23. While
24 persons are too few to deduct general trends and
developments of groups of persons like in the sec-
ond use case, this overview may still hold informa-
tion of interest for a browsing task and show move-
Figure 19: Timeline of all instrument makers in Europe and
their temporal occurrence. Nearly constant state during the
years 1800 to 1930 supports hypothesis of transitioning of
production centers seen in Figure 18.
ments of people that used to work mainly in Milan.
The network graph of this search (see in the middle of
Figure 23) shows low connectivity for these persons.
Only the first component consists of more than one
person of the search result (two black nodes, while
gray ones indicate persons, not included in the result).
Indicated by the purple edge, both persons are of the
same kin. A click on the nodes shows us the informa-
tion that these two persons are rather unknown (only
a sparse amount of data is given) and that they had a
familial relationship (uncle and nephew). The last set
of visualizations consists of pie charts and sunbursts
(Figure 23, bottom). At first glance, we notice a quite
large percentage of employers (branch) of the instru-
ment making section. The sunburst of musical profes-
sions adds information about the actual kind of pro-
fessions by these persons. If we would be interested
in getting a full overview of all these instrument mak-
ers, we could click on the Instrument Making section
of the Branch sunburst or on one of the segments of
the Musical Professions sunburst to set a filter to only
those persons included in the categories. Other pos-
sible paths leading to further inspections could be for
example to view the persons in one of the three listed
institutions or persons having specific non-music re-
lated professions like statesman, painter or librarian.
Figure 20: Start Page Visualization for Loci, showing a ran-
dom selection out of the most user searched locations.
6 CONCLUSION
With the growing importance of digital methods in the
humanities, the amount of data digitally available is
likewise rising continuously (Windhager et al., 2018;
J
¨
anicke et al., 2015). Due to the nature of the field,
available data in musicology is typically fragmented
due to different, loosely connected sub-domains. A
single fragment bears a vastness of data but mostly fo-
cuses on a specific view on the field. Global trends in
musicology can only be hypothesized, and only a few
tools and projects give a comprehensive overview.
The musiXplora is an on-going project dealing with
collecting, standardizing and visualizing information
of seven different types of data defined by musicol-
musiXplora: Visual Analysis of a Musicological Encyclopedia
85
Figure 21: Zoomed in view on the Loci start page.
ogists. Due to close collaboration between visual-
ization scholars and musicologists, the tool does not
only make use of digital advantages of data storage
and the capability of visualization to make data eas-
ily accessible, but also guarantees correctness, qual-
ity, and relevance of the content for both, experts and
casual users. Different use cases showcase the abil-
ity of the system to be used for a wide set of research
questions and also enable different ways of access-
ing resources. Thus, the experts could validate a hy-
pothesis of the abstract concept of the Industrial Rev-
olution and its influence on music instrument makers
through visualizations of places and persons. Further,
we observed a large interest and fascination during
the usage of the tool and through on-going interdisci-
plinary discourses on the needs and interests of differ-
ent musicologists. While the tool is generally tailored
for the musicological data set, other projects using
this data (J
¨
anicke et al., 2016; Meinecke and J
¨
anicke,
2018) have already shown that an adaption to non-
musicological data is possible, which offers diverse
opportunities for future research.
Figure 22: Single Result Page of Milan, including all per-
sons and their activity in the city.
Figure 23: The overview visualizations for all persons with
main activity in Milan. From top to bottom: timeline, map,
network graph and collection of pie charts and sunbursts.
REFERENCES
Andr
´
e, P., Wilson, M. L., Russell, A., Smith, D. A., Owens,
A., and Schraefel, m. (2007). Continuum: Designing
Timelines for Hierarchies, Relationships and Scale. In
Proceedings of the 20th Annual ACM Symposium on
User Interface Software and Technology, UIST ’07,
pages 101–110, New York, NY, USA. ACM.
Borman, T. and Stoel, B. (2009). Review of the uses of com-
puted tomography for analyzing instruments of the vi-
olin family with a focus on the future. J Violin Soc
Am: VSA Papers, 22(1):1–12.
Crauwels, K. and Crauwels, D. (2018). musicmap:
The Genealogy and History of Popular Music Gen-
res from Origin till Present (1870-2016). https://
musicmap.info/ (Accessed 2019-06-24).
den Bulcke, J. V., Loo, D. V., Dierick, M., Masschaele,
B., Hoorebeke, L. V., and Acker, J. V. (2017). Non-
destructive research on wooden musical instruments:
From macro- to microscale imaging with lab-based x-
ray ct systems. Journal of Cultural Heritage, 27:78
87. Wooden Musical Instruments Special Issue.
Doi, C. (2017). Connecting music and place: Explor-
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
86
ing library collection data using geo-visualizations.
Evidence Based Library and Information Practice,
12(2):36–52.
Focht, J. (2006). Bayerisches Musiker-Lexikon Online.
www.bmlo.lmu.de/ (Accessed 2019-12-10).
Focht, J. (2019). musiXplora. www.home.uni-leipzig.de/
mim (Accessed 2019-12-10).
Gleich, M. D., Zhukov, L., and Lang, K. (2005). The world
of music: Sdp layout of high dimensional data. Info
Vis, 2005:100.
Heller, V. (2017). Methoden zur Untersuchung und Doku-
mentation der Geigen am Museum f
¨
ur Musikinstru-
mente der Universit
¨
at Leipzig; Dissertation.
Hopfner, R. (2018). Violinforensic. http:
//www.violinforensic.com (Accessed 2019-06-24).
J
¨
anicke, S., Focht, J., and Scheuermann, G. (2016). Interac-
tive visual profiling of musicians. IEEE transactions
on visualization and computer graphics, 22(1):200–
209.
J
¨
anicke, S., Franzini, G., Cheema, M. F., and Scheuermann,
G. (2015). On Close and Distant Reading in Digi-
tal Humanities: A Survey and Future Challenges. In
Borgo, R., Ganovelli, F., and Viola, I., editors, Eu-
rographics Conference on Visualization (EuroVis) -
STARs. The Eurographics Association.
Khulusi, R., Focht, J., and J
¨
anicke, S. (2018). Visual Ex-
ploration of Musicians and Institutions. Data in Dig-
ital Humanities 2018: Conference Abstracts, 2018
EADH.
Khulusi, R., Kusnick, J., Focht, J., and J
¨
anicke, S. (2019).
An Interactive Chart of Biography. In 2019 IEEE Pa-
cific Visualization Symposium (PacificVis), pages 257–
266.
Khulusi, R., Kusnick, J., Meinecke, C., Gillmann, C.,
Focht, J., and J
¨
anicke, S. (2020). A survey on visual-
izations for musical data. Computer Graphics Forum.
Konopka, D., Schmidt, B., Kaliske, M., and Ehricht, S.
(2017). Structural Assessment of Wooden Musical
Instruments by Simulation: Models, Validation, Ap-
plicability. Proceedings of the 4th Annual Confer-
ence COST FP1302 WoodMusICK - Preservation of
Wooden Musical Instruments Ethics, Practice and As-
sessment.
Kusnick, J., Khulusi, R., Focht, J., and J
¨
anicke, S. (2020).
A Timeline Metaphor for Analyzing the Relationships
between Musical Instruments and Musical Pieces. In
Proceedings of the 11th International Conference on
Information Visualization Theory and Applications
(IVAPP).
Leskinen, P., Hyv
¨
onen, E., Tuominen, J., et al. (2017). An-
alyzing and visualizing prosopographical linked data
based on biographies. In BD, pages 39–44.
Lu, S. and Akred, J. (2018). History of Rock in 100
Songs. https://svds.com/rockandroll/#thebeatles (Ac-
cessed 2019-06-24).
Meinecke, C. and J
¨
anicke, S. (2018). Visual Analysis
of Engineers’ Biographies and Engineering Branches.
LEVIA18 : Leipzig Symposium on Visualization in
Applications 2018 hrsg. von J
¨
anicke, Stefan; Hotz, In-
grid, 2018.
Miller, M., Walloch, J., and Pattuelli, M. C. (2012). Visual-
izing linked jazz: A web-based tool for social network
analysis and exploration. Proceedings of the Ameri-
can Society for Information Science and Technology,
49(1):1–3.
Munzner, T. (2009). A Nested Model for Visualization De-
sign and Validation. IEEE Transactions on Visualiza-
tion and Computer Graphics, 15(6):921–928.
Shneiderman, B. (1996). The Eyes Have It: A Task by Data
Type Taxonomy for Information Visualizations. In
Proceedings., IEEE Symposium on Visual Languages,
pages 336–343. IEEE.
Tresch, J. and Dolan, E. I. (2013). Toward a new organol-
ogy: Instruments of music and science. Osiris,
28(1):278–298.
Tuniz, C., Bernardini, F., Turk, I., Dimkaroski, L., Mancini,
L., and Dreossi, D. (2012). Did neanderthals play mu-
sic? x-ray computed micro-tomography of the divje
babe flute. Archaeometry, 54(3):581–590.
Vavrille, F. (2017). LivePlasma. http:
/www.liveplasma.com/ (Accessed 2019-06-24).
Windhager, F., Federico, P., Schreder, G., Glinka, K., D
¨
ork,
M., Miksch, S., and Mayr, E. (2018). Visualization
of Cultural Heritage Collection Data: State of the Art
and Future Challenges. IEEE Transactions on Visual-
ization and Computer Graphics.
musiXplora: Visual Analysis of a Musicological Encyclopedia
87