Visual Analytics of Bibliographical Data for Strategic Decision Support

of University Leaders: A Design Study

Paul Rosenthal

, Nicholas H. M

uller

and Fabian Bolte

Institute of Computer Science, University of Rostock, Rostock, Germany

Faculty of Computer Science and Business Information Systems, University of Applied Sciences W

urzburg-Schweinfurt,

urzburg, Germany

Department of Informatics, University of Bergen, Bergen, Norway

Keywords:

Bibliographical Data, Stream Graph, Decision Support, Design Study.

Abstract:

As responsibilities about documentation of work in conjunction with an increase in third-party-funding for

universities have been shifting over the last decade, new tools for the inspection and reporting of data are

increasingly requested for strategic decision making. Therefore, we present a design study that aims to craft

a stream visualization for the easy to use and easy to understand display of publications across university

institutions. A formal design process was performed and led to a web-based visualization prototype of the

available university data sets. The used visualization techniques, counting methodology, highlighting prac-

tices, and interaction paradigms are discussed and presented in detail. The design study was completed by

an informal evaluation procedure within the ranks of strategic decision making staff. It turned out, that the

developed expert tool allows to identify connections for future projects. In addition, it enables management to

recognize promising departments or to apply support where it is needed.

1 INTRODUCTION

The societal role of universities as institutions to

gather and distribute knowledge has begun to change

over the last decade. Whereas scientiﬁc research is

still a very relevant attribute, the importance of mone-

tary funding has increased dramatically. Stating this,

also the role of publications in academia is about to

change from a method of communication and recog-

nition into an instrument of academic steering for

the measurement of academic success. In addition,

a promising publication record is not just relevant

for tenured professors but it is increasingly becom-

ing relevant as an entry-criteria into academia in gen-

eral (Riegraf, 2018).

Although this has been true for decades already,

the questions of university decision makers have be-

come much more complex. At the beginning, it was

sufﬁcient to count publications or probably distin-

guish between monographs, journal articles, and con-

tributions to proceedings. Nowadays, questions in-

clude much more complex dimensions. Here, it typ-

ically not only sufﬁces to include impact measure-

ments of the publication venue but also university-

speciﬁc dimensions have to be considered, like an in-

terdisciplinary set of authors or the inclusion of exter-

nal authors.

Compared to ﬁnancial data and independent from

the scientiﬁc discipline, publications are usually the

best maintained parts of any department’s website,

getting refreshed as soon as the contribution is pub-

lished. In addition, these numbers are in most cases

also available in a multitude of public repositories like

Google Scholar or other discipline-speciﬁc deriva-

tives.This does not only allow for an adequate as-

sessment of the raw publication potential, but also

indicates ongoing and developing connections across

interdisciplinary boundaries. By assessing the au-

thors of the publications, any university administra-

tion could easily identify connections to other univer-

sities, countries, or even connections to industry part-

ners. An easy to use, easy to interpret visualization of

these aspects when looking at publication data, could

be key to generate new cooperations.

Rosenthal, P., Müller, N. and Bolte, F.

Visual Analytics of Bibliographical Data for Strategic Decision Support of University Leaders: A Design Study.

DOI: 10.5220/0007396302970305

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), pages 297-305

ISBN: 978-989-758-354-4

297

2 USE CASE

In the following, we present a design study that

speciﬁcally deals with this general challenge and in-

troduces an easy to use solution for a practical use

case. The work has emerged as part of the strategic

readjustment of a medium-sized university of about

10,000 students and around 2,000 staff members in

relation to the ongoing challenges of academic poli-

tics in Germany. The respective university consists of

eight faculties with nearly 200 research departments.

In order to maintain viable information about pub-

lications, the university’s library runs an internal data

base of all university publications. To update this

list of publications is mandatory for all departments

and is being collected since 2008. The collection cur-

rently consists of signiﬁcantly more than 20,000 pub-

lications. For each publication, the following relevant

data are collected:

• title

• author names and afﬁliation

• type of publication

• name of journal, book, or proceedings

• year of publication.

The data base is supported by an ontology of the

university bodies, structuring the authors and research

groups to the different institutes and departments.

Here it is important to note, that research groups may

belong to several institutes or even two or more de-

partments.

Since such data is collected in a majority of uni-

versities around the world and similar questions re-

garding the importance of publications arise, the tack-

led use case and the further considerations can be

generalized. This is even true for use cases outside

academia, where a holistic overview over publications

data is needed for steering strategic decisions.

Main stakeholders in our tackled use case are the

university’s strategic deciders, speciﬁcally the presi-

dent, the vice presidents, the deans, and the managing

employees. The main objective of the project was to

develop a prototype with the following communicated

features:

• It relies on the internally collected bibliographic

data and the incorporated university structure.

• The prototype provides an instant visual output

and allows for intuitive interaction through non

computer specialists.

• University’s decision makers can utilize the pro-

totype to gain operational insights and support

strategic decisions.

• The prototype should be implemented as a web

service with minimal requirements and can easily

be transferred in the future into a service parallel

to the web-based bibliographic data base.

3 REQUIREMENTS ANALYSIS

Relying on the aforementioned project deﬁnition, a

team of three researchers began the design study by

analyzing the design requirements. The team con-

sisted of a senior researcher in media psychology, a

senior researcher with background in requirements

analysis and visual analytics design, and a junior re-

searcher in the ﬁeld of interactive visualization and

data processing.

In a ﬁrst phase, a group of six permanent contact

persons, representing all relevant stakeholders, was

recruited and agreed to support the project throughout

the different phases. The members of this core-user

group were individually interviewed on how to inter-

pret the project objectives in a semi-structured way.

Our main goal of this approach was to extract the key

features, the users wanted to inspect, how these relate

to each other, and how these contribute to the strate-

gic decisions. Additionally, key features of the future

prototype and show stoppers had to be identiﬁed.

In summary it turned out, that the core users had

the following very similar requirements:

R1: Besides the pure number of publications, users

need to inspect fractions of certain types (journal

article, book,. . . ) of publications.

R2: Users need to quantify the publications with re-

spect to interdisciplinarity and involvement of ex-

ternal partners.

R3: The users need to quantify the publication data

in development over the time dimension.

R4: The users need to compare all data with respect

to the university structure (departments, institutes,

research groups).

R5: The tool, its navigation, and all used visual en-

codings need to be intuitively understandable for

the core users without any form of instruction

manual or a training period.

4 STATE OF THE ART

Professionally establishing their own bibliographic

data base is not a new approach for universities. Still,

this is not common for most academic institutions.

Even less common is the strategic analysis of such

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298

data for the long-term steering of resources - a novel

idea for the academic institution we chose for our ap-

plied research. Instead, most of the time the publica-

tion data is merely used as a pledge in ﬁnancial nego-

tiations with the state.

Informal and non-representative investigations

among international university decision makers have

revealed that this is also true for many other uni-

versities. If such bibliographic data bases exist and

a strategic analysis is desired, universities currently

have to rely on standard tools since no specialized

visualization software is readily available. In most

cases, this is done by manual analysis, using tools

ranging from pure spreadsheets to sophisticated vi-

sualization suites. Although these tools offer a wide

range of feature richness, their common drawback

is that they are not made for the instantaneous us-

age by novices. Even visualization experts are often

initially overwhelmed by the huge amount of visual-

ization possibilities, making a spontaneous inspection

session with deciders impossible.

5 RELATED WORK

Nowadays, there exist plenty of general collections of

bibliographic data. Even topic-speciﬁc and curated

collections, like Vispubdata.org for the IEEE Visual-

ization publications (Isenberg et al., 2017), rise more

and more. In addition, the analysis of such data col-

lections gets increasingly more comfortable with the

growing number of analysis tools, like for example

SurVis (Beck et al., 2016).

Apart from such general-purpose techniques,

many different approaches have been presented for

speciﬁc use cases. For example, Van Eck and Walt-

mann (van Eck and Waltman, 2014b; van Eck and

Waltman, 2014a) visualize bibliometric networks on

the author level. Although the presented CitNetEx-

plorer allows for detailed inspection of author con-

nections, it seems to be not so easy to incorporate the

special meta structures of universities and answer the

relevant questions. This also applies to several other

graph-based approaches for visualizing bibliometric

data (Bornmann and Haunschild, 2016; Brandes and

Pich, 2011; Newman, 2004; Sallaberry et al., 2016).

On the meta level, Fung et al. (Fung et al., 2016)

investigate the effectiveness of three different biblio-

graphic network visualization techniques and reveal

the weaknesses and strengths of node-link diagrams,

adjacency matrices, as well as botanical tree visual-

izations. This work concentrates on the individual de-

velopment of each researcher, including the individ-

ual publication network. Comparisons between sev-

eral different researchers are hard to obtain.

Concentrating more on the chronological aspect,

Heimerl et al. (Heimerl et al., 2016) visualize publi-

cations in a stream graph. The presented visualiza-

tion is enriched by an excerpt of the content and de-

tailed bibliographical information. Similarly, J

anicke

et al. (J

anicke et al., 2016) visualize speciﬁc data of

the life of musicians. The authors incorporate several

additional dimensions like life events, connections, or

confession into a stream graph.

On the other hand, Wu et al. (Wu et al., 2013)

present a novel visualization technique for analyz-

ing the individual career paths of academic persons.

The visualization includes a stream graph representa-

tion of the publications, superimposed with speciﬁc

events in the respective scientiﬁc path ways. Fol-

lowing these lines, Wang et al. (Wang et al., 2018)

present the tool ImpactVis to visualize the impact of

researchers through bibliographic data. The authors

concentrate on the in-depth analysis of the individual

literature records of researchers and their detailed ci-

tation connections to other researchers.

In consequence, this development of new visual-

ization techniques has also brought up a huge amount

of literature surveys. For example, Mascarenhas et

al. (Mascarenhas et al., 2018) perform an extensive

literature review on university-industry cooperations

and their effectiveness for promoting innovation in in-

dustry and research.

Comprehensive surveys over the ﬁeld of visualiz-

ing bibliographic data were recently presented by Liu

et al. (Liu et al., 2018) and Federico et al. (Federico

et al., 2017). Although there are many interesting

ideas to help with the challenges of our tackled use

case, none of the presented approaches is out-of-the-

box suited for fulﬁlling the compiled requirements.

6 DESIGN PROCESS

Since none of the inspected related approaches or ex-

isting tools was matching the design criteria, the de-

sign of a new expert tool was desired. As proof of

concept, the development of the prototype was pur-

sued on basis of a dump of the original data base for

the time period 2009–2014.

6.1 Data Preprocessing

Initially, the existing data was investigated in terms

of data quality. It turned out, that also a profession-

ally maintained data set contained the same problems

which all manually entered data sets have. Most of

Visual Analytics of Bibliographical Data for Strategic Decision Support of University Leaders: A Design Study

299

the problems could be broken down to missing or in-

correct values as well as spelling errors. As a pre-

processing step for this design study, all singular or

oddly appearing values were manually inspected and,

if needed, corrected by inspecting the publication.

Also, duplicates were semi-automatically discovered

and manually removed from the pool.

On basis of the, now reliably “cleaned”, data set,

it was time to investigate whether or not all the re-

quirements from Section 3 could be reliably backed

by data. The requirement R1 should be easy to ob-

tain, since the needed data is directly included in the

data set. This also applies to R3 and R4. Since R5

forms no requirement in terms of the data, it also has

no consequences for the preprocessing step. In terms

of R2, some preprocessing was involved to obtain the

needed data. In an automated process, all authors of

each publication were categorized with respect to the

university structure. Afterwards, a publication was

additionally marked in the following way as:

institute internal, if only authors from the same in-

stitute contributed to the publication.

department internal, if all authors of the publica-

tion belong to the same department, but at least

two different institutes are involved.

interdisciplinary, if authors of at least two different

departments are involved but no external authors.

external, if the publication features at least one ex-

ternal author.

For the strategic thoughts of the investigated core

users, this intersection-free partition was important

and explicitly desired. However, this can differ if

other focus points are tackled in the related use cases.

It is also important to note, that in the structure of

the investigated university, research groups may be-

long to different institutes or even two or more depart-

ments. With respect to this structural feature, publi-

cations were for example marked as interdisciplinary

if authors from at least two research groups were in-

volved and one research group belonged to at least

one department the other research group did not be-

long to.

6.2 General Procedure

In general, the user-centered part of the design study

was performed in an iterative fashion on basis of rapid

prototyping. The six members of the stakeholder

group were initially confronted with simple sketches

of design ideas. Feedback was steadily integrated into

the development of new prototypes, leading to a ﬂow

of artifacts with increasing complexity, functionality,

and closeness to the practical working prototype.

Although the communication with the users was

mainly based on digital artifacts, initial sketches were

done with pen and paper in sketching sessions (Green-

berg et al., 2011; Walny et al., 2015), allowing for

more freedom and creativity. This changed over time

for more preciseness to digital pictures and pseudo-

animations. In the late iterations, web-based mock-

ups and prototypes were used, implemented on ba-

sis of Data-driven Documents (D3) (Bostock et al.,

2011), as also the ﬁnal prototype.

6.3 Layout

All experts agreed that the ﬁnal tool should rely on

keyboard and mouse as interaction mechanisms and

should use standard monitors as display devices (R5).

Still, we also tried to keep in mind mobile friendli-

ness, should future requirements arise in this direc-

tion. However, this also heavily inﬂuenced our deci-

sion for a standard two-dimensional and rectangular

layout of the desired design.

Since the two main dimensions for nearly all prac-

tical questions and usage scenarios are the number

of publications and the time dimension (R3), these

two also emerged in the design process as dimensions

to map to the two-dimensional design space. Hav-

ing ﬁxed this setting, there still do exist many differ-

ent visualization methods for time-oriented discrete

data. A survey was recently presented by Brehmer et

al. (Brehmer et al., 2017). However, after very short

discussion it was clear, that the optimal setting was

to put the time dimension from left to right, the nat-

ural dextrograde process ﬂow for Europeans, and the

number of publications to the vertical axis.

This design decision and a thorough review of vi-

sualization methods for time-oriented data (Aigner

et al., 2007; Aigner et al., 2011) let to the concept

of visualizing the amount of publications in a stream

graph (R1, R3), as proposed by Havre et al. (Havre

et al., 2000; Byron and Wattenberg, 2008). With this

concept there are at least two additional degrees of

freedom involved, the type of interpolation between

the discrete years and the the interpolation with re-

spect to the publications axis. Interpolating the time

axis is basically trading accuracy for visual appeal. A

comparison of the three supported approaches (con-

stant, linear, and polygonal interpolation) is presented

in Figure 1. Since none of the approaches is optimal

for all different usage scenarios, the prototype allows

for switching between them, with polygonal interpo-

lation being the default setting.

Also for visualizing the publications axis, there

are several different possibilities, from which we have

identiﬁed three possibilities that have their eligibility

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Constant Interpolation Linear Interpolation Polygonal Interpolation

Figure 1: Different types of interpolation between the number of publications per year. While the constant interpolation gives

the most correct representation of the discrete data set, the polygonal interpolation produces the most pleasing visualization.

In most cases, the linear interpolation was favored as a good balance between both extrema.

Bottom Baseline Centered Baseline Filled Space

Figure 2: Different types of visual indication of the number of publications.

for the different questions to answer. Figure 2 shows

the three different types. Putting the baseline at the

bottom of the screen and scaling the number of publi-

cations constantly over the years allows for the most

precise judgment of the overall number of publica-

tions and its development. In contrast, centering the

number of publications for each year at the middle

of the screen, minimizes distortions of the boundary

streams and makes inspection of individual streams

easier. From our core users this visualization was

also rated the most pleasing and in most cases useful.

Consequently, it was set as the default representation.

When only questions of relative comparisons over the

years have to be answered, it is also possible to stretch

the whole set of publications per year over the full de-

sign space, resulting in a relative stream graph.

6.4 Colors

Apart from the placement in space, color and bright-

ness denote the most visible visual cues in a visual-

ization. Consequently, the discussion of colors was a

long phase of the design process. Although there are

several surveys on the different color maps and their

special features (Silva et al., 2011; Zhou and Hansen,

2016), it was very hard to proﬁt from these insights

in terms of our use case. As part of the university’s

corporate identity, all departments were given individ-

ual and speciﬁcally deﬁned colors. So it was not un-

expected when all deciders unanimously agreed that

these colors had to be used throughout the whole visu-

alization for publications belonging to the respective

departments (R4).

Under these restrictions, the only color features

left to change and use for other visualization purposes

were the saturation and transparency. Since it was

stated that saturation should, again thanks to corpo-

rate identity, only be changed in small portions, we

chose to use this visual cue to deduct different re-

search groups or institutions within a department from

each other. An example visualization is shown in Fig-

ure 3.

Figure 3: Using alternating saturation for indicating the dif-

ferent research groups of a department.

Changing transparency on the other hand gives the

chance of encoding continuous values in addition to

the already provided time dimension and number of

publications. This is a feature, the core users very

much liked in our feedback rounds. In our design,

we provide the possibility to encode all different frac-

tions, like the fraction of interdisciplinary papers, to

the transparency value of each stream (R1). This gives

the user an instant relativization of the number of pub-

lications, as illustrated in Figure 4.

All other visual encodings like additional sym-

bols, hatching, shadows, or three-dimensional effects

were refused by the core users as too complicated, too

cluttering, or not being self-explanatory enough.

Visual Analytics of Bibliographical Data for Strategic Decision Support of University Leaders: A Design Study

301

Figure 4: Mapping a third value, ideally a fraction percent-

age to the transparency, allows for generating insights just

within one glance.

6.5 Counting

In terms of the different color codings, one challenge

came into play, which is often occurring if data is not

uniquely assignable. Each publication may include

several and a different number of authors, institutes,

or departments. Consequently, the question arises

how to count this one publication with respect to the

different structural entities. This aspect is discussed

in great detail by Perianes-Rodriguez et al. (Perianes-

Rodriguez et al., 2016).

After long discussions of strategic goals and their

match to this question, the users decided to count each

publication once per author, such that a publication

with six university authors is represented six times

in the visualization. This procedure speciﬁcally pro-

motes publications with multiple authors, postulating

that all authors also really contributed to the publi-

cation. In addition, the fractions of external authors

were added to the parts of the university authors to

not penalize such publications.

In terms of institutes and departments, the same

strategy was applied. If a publication has two au-

thors from one department and three from another

one, the departments got the respective share of two

and three publication counts. In the case of research

groups belonging to multiple structural organizations,

these contributions were equally split among the de-

partments or institutes.

6.6 Interaction

The last very important step in our design process was

the interaction. An early inclusion of interaction into

the design process was achieved by constructing fake

interactions already on the pen-and-paper level. Hav-

ing said this, most of the desired interactions were al-

ready deﬁned at very early stages of the design pro-

cess. Since the core users were very conservative in-

terface users and the ﬁnal tool had to be very intu-

itive (R5), the interaction possibilities turned out to

be quite conservative as well.

As ﬁrst interaction method, the movement and

zooming of the visualization is operated by the well-

known mouse operation (click and move, moving the

mouse wheel). An even more important feature was

the possibility for detailed inspection and exploration

of the data. This was ensured with a mouse-over func-

tionality of the prototype. Once the mouse is hover-

ing over a research group at a speciﬁc point in time,

all available data is indicated in a tool tip, as illus-

trated in Figure 5. In addition, the respective stream of

the research group is highlighted in the stream graph

(R3). Switching the different modes of coloring and

superimposing the data with characteristic fractions

was implemented using simple menus with descrip-

tive symbols and text.

Figure 5: The presented tool features a mouse-over effect.

For each research group the mouse pointer is currently over,

all available data at the respective point in time is indicated

in a tool tip and the stream of this research group is high-

lighted in the visualization.

Another, but also very important feature for the

core users, was the ability to interactively sort and ﬁl-

ter the data (R1). For this interaction function, dif-

ferent interaction techniques were desired and im-

plemented. Clicking at speciﬁc streams in the vi-

sualization selects or deselects them. An interactive

list shows the IDs of the currently selected research

groups. In addition, a selection or deselection is also

possible by manually entering the IDs. This feature

was speciﬁcally asked by the deans who know their

department IDs very well. However, also in the uni-

versity administration this feature was rated as being

very handy if a speedy analysis of a speciﬁc univer-

sity consortium is desired. Once the selection is ﬁnal,

the visualization can be ﬁltered down to the selection

by pressing the ENTER-key or the right mouse but-

ton. An illustration of the ﬁltering is shown in Fig-

ure 6. The same keys also switch off the ﬁlter again.

Clicking on empty screen space or hitting the ESC-

key clears the current ﬁlter list.

Regarding the ﬁltering, it was also explicitly de-

sired to implement short cuts for the most important

ﬁlters, like the departments. These are implemented

as buttons in the top left corner of the tool and al-

low for instant ﬁltering of a set of departments or the

instant selection of the research groups of whole de-

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302

Figure 6: The presented tool features the ﬁltering up to in-

dividual research groups. In the shown visualization, three

research groups from different departments were selected.

partments. Using an additional click at the top of the

respective time column it is also possible to sort the

currently visible streams with respect to the absolute

number of publications in this year. An illustration of

this feature is given in Figure 7.

2011 2012

Figure 7: Illustration of the sorting feature. Clicking the

column of a speciﬁc year, allows to sort the visible streams

with respect to the number of publications in this year.

As keyboard and mouse interaction might in the

future be replaced by other techniques, it was impor-

tant to keep the chosen metaphors as open as possible.

In the speciﬁc case it is clear that a switch to a touch

interface would be instantly possible without major

changes to the interaction concept.

7 EVALUATION

The following describes our preliminary evaluation

of the visualization prototype for detailed publica-

tion data inspection across faculties and departments.

We used the aforementioned method of visualized

publications to allow for a detailed inspection with

one German university and discussed the outcome

with different departments and the general university

administration. Since third-party funding is getting

more important to keep universities aﬂoat, the gen-

eral feedback to our publication overview during our

ongoing informal interviews is that of great interest in

the various methods of visual analysis.

Especially in the case of decision making and

strategic guidance, the comparison of different as-

pects of the publication data allowed for a more intu-

itive and reliable interpretation of which department

would beneﬁt from strategic intervention in the case

of producing research requests or to acquire other

forms of project funding. Since many insights are

closely coupled to additional data and have to be seen

in much broader context, it was hard to extract de-

scriptive examples.

One very impressive example was given by a dean

during the evaluation sessions. The dean discovered

during the interaction sessions just on his own the vi-

sualization depicted in Figure 8. Having this compari-

son of all research groups over time with total number

of publications and additional encoding of interdisci-

plinarity of the publication enabled him to generate

an in-depth insight into the publication behavior of

his department at a single glance.

During the evaluation phase, there were several

other of these light-bulb moments among all partic-

ipating deciders. In addition it was observed, that the

display of external publications provides a novel form

of easily and readily being able to identify persons to

contact in the case of other research and development

projects. In summary, they all agreed that the proto-

type allowed for insights that were previously barely

possible or just with signiﬁcant extra efforts. In con-

sequence, the project was recommended to be further

pursued and developed to a usable standard tool.

8 CONCLUSIONS

In this design study, we have analyzed the use case

of visualizing bibliographic data to support univer-

sity decision makers. The use case was researched

in detail, following a rigorous requirements analysis.

A visualization design on basis of the requirements

was created in an iterative process, resulting in a new

form of visualization for publications at universities.

Compared to only analyzing ﬁnancial data, the con-

structed interactive tool gives valuable additional in-

sights and allows for in-depth inspection through de-

cision makers. This leads to new opportunities for

strategic decision-making at universities, since inter-

disciplinary work as well as internal and external re-

search connections are readily available and easy to

identify when using our tool. This fact was conﬁrmed

by an informal user study with real expert users.

The ﬁndings of the requirements analysis, the de-

sign process and the evaluation can be generalized to

other similar use cases. At the moment, our visualiza-

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303

Figure 8: Visualization of all research groups of one individual department. For each research group, the transparency is

chosen to represent the fraction of interdisciplinary papers.

tion is based on internal publication data. However,

due to its ability to rework many other forms of input

data, it can be easily extended to other sources of lit-

erature repositories. Furthermore, an additional layer

of signiﬁcance could be added by integrating impact

factors, publication venues, and other dimensions into

the representation.

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