The Power of Information Visualization for Understanding the

Impact of Digital Media Projects

Mónica Silva

, Lersi Duran

, Sofia Bermudez

, Fábio Ferreira

, Oksana Tymoshchuk

Lídia Oliveira

and Nelson Zagalo

Department of Communication and Art, University of Aveiro, Portugal

Information and Communication Technology Services (STIC), University of Aveiro, Portugal

Keywords: InfoVis, Business Intelligence, Projects’ Dashboard, Students@DigiMedia.

Abstract: This study aims to understand the most effective way to present the results and impacts of research projects

in the field of Digital Media collected by the Digital Media Observatory. The focus is developing dashboards

using InfoVis tools and Business Intelligence to showcase a large volume of collected data, with a team of

Students@DigiMedia. Takes an exploratory approach with three main phases: researching available InfoVis

tools, creating sample dashboards using InfoVis tools, and implementing project dashboards using Power BI.

The team of students has developed dashboards that provide a clear, structured view of the project,

aggregating the following information: title, logo, objectives, keywords, funding, human resources, partners,

methodological procedures, scientific and technological products, publications, dissemination, recognition,

and SDGs. These dashboards provide interactive reports and visualisations to help researchers analyse, and

communicate project results. This study can help to improve the overall data presentation experience,

simplifying the analysis and knowledge-sharing process within the digital media research community.

1 INTRODUCTION

This article is part of Students@DigiMedia

, an

initiative created by the DigiMedia Research Centre

at the University of Aveiro to encourage student

participation in scientific research activities. This

study aimed to assist the Digital Media Observatory

(DigitalOBS) team at this Research Centre in

presenting the extensive data collected from 40

scientific projects conducted by the centre's

researchers over the past five years engagingly and

dynamically. The intention was to develop an

interactive and visually appealing platform that

would effectively showcase the wealth of knowledge

and discoveries generated by these projects.

Given the substantial information associated with

research projects, Information Visualization

https://orcid.org/0000-0002-5094-7281

https://orcid.org/0000-0002-6931-2577

https://orcid.org/0009-0009-4119-2567

https://orcid.org/0000-0001-8054-8014

https://orcid.org/0000-0002-3278-0326

https://orcid.org/0000-0002-5478-0650

https://digimedia.web.ua.pt/archives/14971

techniques were proposed to generate a graphical

dashboard model, to present a comprehensive set of

information effectively.

By utilizing a Performance Assessment Model

(PAM) (Tymoshchuk et al., 2024), the team were able

to identify the most relevant data for presentation and

aggregation of information. Selecting the data that

would best represent the project graphically within

each category of inputs, outputs, and impacts was

crucial.

By incrementing the model developed, we were

able to produce a standard model for presenting the

associated projects, assuming a visual report for each

one. This approach involved creating several

dashboards using various software applications

throughout development. Ultimately, a model was

developed and is currently being implemented on the

172

Silva, M., Duran, L., Bermudez, S., Ferreira, F., Tymoshchuk, O., Oliveira, L. and Zagalo, N.

The Power of Information Visualization for Understanding the Impact of Digital Media Projects.

DOI: 10.5220/0012545800003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 172-179

ISBN: 978-989-758-692-7; ISSN: 2184-4992

DigitalOBS website to present the results of scientific

projects.

2 THEORETICAL FRAMEWORK

In recent decades, funding for scientific research

projects has increased to promote innovation,

knowledge transfer, and the achievement of the

Sustainable Development Goals (Santos, 2022).

These projects aim to address complex social

problems through collaboration between different

fields and develop various technological and

scientific products. However, challenges arise in

evaluating and managing these projects, particularly

in terms of accountability and rigorous evaluation.

Traditional bibliometric indicators are being

questioned, and there is a need for qualitative

approaches to assess research quality. It is also

important to consider the temporal phases and the

long-term impact of research (Saenen, et al., 2019;

Santos, 2022).

The international scientific community proposes

fairer evaluation models that combine quantitative and

qualitative approaches. As research becomes more

collaborative and interdisciplinary, new evaluation

methods are necessary to capture the full impact and

value of scientific contributions. This includes practi-

ces such as data sharing, open science, and considera-

tion of societal impact (Djenontin & Meadow, 2018;

Frey & Widmer, 2009; Patrício et al., 2018).

Effective communication plays a crucial role in

fostering comprehension and advancement,

particularly within the academic sphere, while also

exerting influence on the corporate and business

sectors. Within the realm of the corporate and

business sector (Ansari, Barati & Martin, 2022;

Horttanainen & Virrantaus, 2004; Hepworth, 2016).

Data visualization refers to a collection of

methodologies that extract pertinent information

from extensive quantities of unorganized or diverse

data (Shneiderman, 1996; Plaisant & Shneiderman,

2022). Adopting information visualization techniques

facilitates the transformation of intricate data into a

more easily understandable format.

Applying efficient communication techniques faci-

litates the dissemination of research outcomes compre-

hensibly and easily accessible to the broader public

(Bacic & Fadlalla, 2016; Binder & Blettner, 2015).

From a developmental perspective, variants and

subtypes are generated through data and scientific

visualization techniques, such as cartographic

visualization, or within the realm of knowledge, such

as statistical visualization. Information Visualization,

(InfoVis), is a technique that facilitates and enhances

the examination of extensive datasets (Jiang, Hou &

Yang, 2023). The objective of this endeavour was to

facilitate users in researching, comprehending, and

scrutinizing data through a gradual and repetitive

approach to visual exploration (Sorapure, 2019;

Wolff et al., 2016).

The effective use of data visualization in a

proficient manner enables a complete evaluation of

research centre initiatives, facilitating the

communication of results in a visually captivating and

instructive manner. This phenomenon can potentially

enhance decision-making processes, optimize resource

allocation, and foster increased transparency among

both domestic and global players (Pinto, Raposo &

Ramos, 2012). Illustrate the collaborative associations

among researchers, internally inside the centre and

externally with external partners (Zhu et al., 2020).

InfoVis enables the presentation of performance

measurements using conventional visual

representations, such as line graphs, scatter plots, or

stacked bar charts. Data visualization techniques

facilitate the identification of trends, patterns, and

impacts within research project data (Andrienko et

al., 2021; Shirato, Andrienko & Andrienki, 2023).

The accessibility of visualized data to various

stakeholders is facilitated by utilizing online

platforms or software tools, which in turn allows for

secure data sharing and collaborative engagement

among academics (Lima, 2011).

The application of Artificial Intelligence (AI) and

Business Intelligence (BI) tools can facilitate

understanding the vast amount of data generated in

research projects, providing a comprehensive and up-

to-date view of the research's scope, influence, and

relevance (Khatuwal & Puri, 2022). Business data

visualisation is primarily used for communication,

information seeking, analysis, and decision

assistance, in contrast to other visualisation kinds and

uses (Zheng, 2018).

Microsoft Power BI is a BI platform that offers its

users a package of tools for aggregating, analysing,

and visualising large amounts of data from one or

more sources, allowing them to obtain relevant

insights from this data and thus help with decision-

making (Becker and Gould, 2019). Power BI has an

accessible and intuitive interface that allows users,

regardless of their knowledge of programming or

visualisation, to create interactive reports and

dashboards that can then be shared with other users or

published on the web. It also supports real-time data

updating, ensuring that the visualisations presented

show the most up-to-date information (Orts, 2004).

The Power of Information Visualization for Understanding the Impact of Digital Media Projects

173

Therefore, applying BI tools, such as Power BI

can help Research Centres understand the complex

results generated by various scientific projects,

facilitate data-based decision-making for future

projects and improve work efficiency.

3 METHOD

This study was conducted as part of the DigitalOBS,

which aims to monitor and analyse trends in digital

media, provide valuable information on the social

impact of media, and serve as a collaboration

platform for researchers, policymakers, and

stakeholders in the field of digital media.

The DigitalOBS team analysed the results and

impacts of research projects conducted by DigiMedia

researchers over the past five years to help define this

research centre's development strategies. To achieve

this, the DigitalOBS team created an analysis model

that examines crucial aspects such as funding

evolution, human resource composition, research

methodologies, scientific and technological results,

and dissemination of research findings (Tymoshchuk

et al., 2024).

 This model provides a structured approach for

evaluating scientific projects in Digital Media,

focusing on three main dimensions: input,

output, and impact.

 The “Input” dimension analyses the resources

allocated to the project, including funding,

human resources, new infrastructures,

collaborations, and characteristics of the

scientific area.

 The "Output" dimension consists of four main

sub-dimensions: methodologies, scientific and

technological products, publications, and

dissemination of activities.

 The third dimension, "Impact," focuses on the

long-term consequences of a project,

 considering economic, social, cultural,

environmental, political, and recognizable

impacts (Tymoshchuk et al., 2024).

This dimension aims to understand the variability of

the results of applied research. This model has been

developed to incorporate data analysis and

visualization tools, enabling researchers to present

data quickly and easily through interactive reports

and dashboards.

Due to the large volume of results generated by

the projects, the decision was made to explore the

potential of BI tools to analyse and visualize this data.

Therefore, the main goal of this study is to develop

dashboards that effectively present the key results of

research projects, integrating advanced BI

technology. These panels can help researchers

analyse, interpret, and communicate the results of

their projects interactively, engagingly, and quickly.

The study followed an exploratory approach that

encompasses three main phases:

i) Analysis of available InfoVis tools, evaluating

their characteristics, functionalities, and

suitability for the specific requirements of the

research centre.

ii) Construction of dashboard examples using

InfoVis tools (Figma), categorizing and

organizing the results and impacts of research

projects according to the developed analysis

model.

iii) Implementing project dashboards using BI

tools (Power BI), aims to help researchers

create and share interactive reports and

dashboards, simplifying the analysis and

communication of knowledge and improving

the overall data presentation experience.

This study significantly improves the overall data

presentation experience by integrating robust data

analysis and visualization tools, simplifying the

analysis and communication of knowledge within the

digital media research community.

4 RESULTS

4.1 InfoVis Tools – Analysis and

Selection

The first step involved clearly defining what

information must be communicated through InfoVis.

In this case, the goal was to effectively visualize the

results of DigiMedia research projects, thus providing

a valuable resource for the team and stakeholders.

The Observatory team had already surveyed 40

research projects conducted at this Research Centre

using the developed evaluation model. Therefore,

there was a strong need to present this information in

a clear, appealing, and interactive manner.

Considering the large amount of data and the project's

time constraints, it was decided to find an InfoVis tool

that would enable us to present this data quickly and

effectively.

The team went through an extensive evaluation

process, examining a wide range of tools and their

capabilities (Table 1).

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Table 1: Criteria by existing InfoVis tools and literature.

Cate

Tools

Deskto

software tools Power BI, QlikView

Libraries and programming

framework tools

D3.js, Matplotlib (Python),

lot2 (R)

Online tools Flourish, Plotl

, Geniall

Design tools Figma, Canva, Infogram,

Visme

The team then categorized the tools analyzed into

four distinct categories to facilitate the comparison

and selection process, allowing the team to make an

informed decision on the most suitable tool for the

project. This categorization was based on a

combination of existing literature on InfoVis tools,

and the subjective criteria established by the team,

(Table 1). According to the tools found, the next step

was the selection process. The work team decided to

use the online tool Genially, because it is

customizable, offers predefined templates and has a

free version available.

4.2 Dashboard Model Development

The initial drafts of the project dashboard were

created using Genially, as shown in Figure 1.

Figure 1: Drafts of dashboards created using Genially.

These drafts were carefully reviewed and

approved by the DigitalOBS team. However, while

creating the interactive prototypes, the researchers of

Students@DigiMedia realized that the free version of

Genially had some limitations. These included a

minimum number of allowed changes and the

absence of certain graphic and interactive features.

As a result, the team of Students decided to switch

to using the Figma tool instead. Figma is an intuitive

vector graphic design editor that provides a wide

range of image optimization capabilities. Using

Figma, the team successfully created interactive

panels for two noteworthy projects: Seduce 2.0 and

HiLives. It was important for the team to ensure that

these dashboards possessed a consistent visual

identity across both projects. The team chose this

deliberate approach to facilitate a quicker and more

effective comprehension of each project.

After conducting a comprehensive review and

adaptation exercise, the team identified the visual

models that were most suitable for effectively

communicating DigiMedia projects (see Figure 2 –

Seduce and Figure 3 - HiLives).

Figure 2: Visual model created using Figma, Seduce

project.

After careful consideration, the dashboard

displayed in Figure 3 was ultimately chosen as the

final model for presenting the data from the collected

projects. This designed dashboard was implemented

in Power BI, ensuring a practical and visually

appealing representation of the project data.

Figure 3: Visual model created using Figma, HiLives

project.

The dashboard prominently showcases the project's

title, objectives, and logo. The remaining information

is visually organized into three distinct blocks: inputs,

outputs, and impact. This arrangement allows for

highlighting key milestones and moments within each

project. As a result, stakeholders can easily monitor

and assess the progress and effectiveness of the

The Power of Information Visualization for Understanding the Impact of Digital Media Projects

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projects. The input block provides detailed data

regarding the resources that have been invested, such

as funding, human resources, and partners. The

outputs block focuses on the services and results

achieved, such as the scientific-technological

products developed, methodological procedures

implemented, publications, dissemination activities

and sustainable development objectives achieved.

Lastly, the impact block measures the long-term

effects and benefits of the projects, such as

improvements in society, the economy, or the

environment and acknowledgement.

4.3 Implementation by BI Tools –

Power BI

Within the scope of this work, Power BI was used to

develop a multi-page report focussing on crucial

aspects of the various projects developed by

DigitalOBS researchers, including details of the

projects carried out, human resources involved,

collaborations between institutions, methodologies

applied, and resulting publications. Each of these

pages is organised as a dashboard offering an

immediate summary of the data under analysis (Orts,

2004). Initially, the various DigiMedia labelled

projects' technical sheets were analysed to detect

patterns and define a structure for the file that would

feed the Power BI report. This source file consists of

an Excel file published online via SharePoint and

shared between DigiMedia members who have

permission to update it whenever new projects or

changes to existing ones appear.

Figure 4: Page structure of the report developed in Power

BI with the project's data.

Their structure is identical and in the majority of

cases is divided into three sections: a top bar

identifying the various pages of the report as well the

indication of which one is currently selected (A), a

small area with various filter options that allow to

segment the data presented (B) and finally a larger

area with various related visual elements that allow to

interact and gain insight from the data presented (C).

The report, developed in Power BI, consists of 11

pages in total. The first page is an aggregation of the

main numbers related to the various projects

developed by DigiMedia researchers. It is possible to

analyse in detail the origin of the number in question

by navigating to the page where it is desiccated.

It is also possible to create a dashboard that

displays the most critical information for each

project. This page was created using the project

presentation template developed in the previous

phase. Figure 5 shows the page summarizing the main

project numbers.

Figure 5: Summary page of the main projects.

The Power BI report effectively combines and

aggregates all the essential project data, providing a

comprehensive overview. It is a centralized hub,

allowing researchers and stakeholders to access

crucial information immediately. The remaining

pages are the following: Projects; Human Resources;

Collaborations; Scientific Area; Applied

Methodologies; Products; Publications;

Dissemination Activities; and Researchers.

Figure 6: Pages created by Power BI with the Project’s data.

Each Power BI dashboard provides a visual

representation of project data, national or

international level, based on the type of partnership.

The partners' locations are displayed on a map,

illustrating their geographical distribution through

dots (Figure 6).

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Regarding the scientific domain, word clouds are

useful for efficiently summarising material and

identifying the most prominent concepts within

scientific fields and keywords. Additionally, each

project is assigned a specific number of Sustainable

Development Goals (Figure 7), which enables the

team to have a more comprehensive understanding of

the primary focus areas.

Figure 7: Pages created by Power BI with Scientific Areas

data and SDG’s.

In Figure 8 we can see a more detailed table of the

methodologies applied in the projects, such as the

type of scientific research, the type of approach taken,

the instruments applied, the target audience and at

what geographical level the projects were

implemented.

Figure 8: Pages created by Power BI with Applied

Methodologies data.

Digital media projects, whether national or

international, typically involve developing diverse

scientific and technological products. Hence, the

team has recognised the significance of categorising

the product menu based on the type of product. This

can be visualised either through a horizontal bar

graph or a project-specific list (Figure 9).

Figure 9: Pages created by Power BI with product data.

Scientific publications play a crucial role in the

success and progress of research centre projects in

terms of being the primary means of disseminating

research findings to the wider scientific community.

They allow researchers to share their discoveries,

methodologies, and insights with other experts in the

field. Peer-reviewed publications enhance the

credibility and reliability of research (Figure 10). So,

publications contribute significantly to the academic

recognition of researchers and the research centre

itself. The number and impact of publications are

often used to indicate the research centre's

productivity and reputation within the scientific

community. Collaborations and partnerships often

arise from shared research interests and

complementary expertise demonstrated through

published work. Therefore, published research can

lead to the development of new technologies or

applications. This knowledge transfer from academia

to industry can result in innovations that benefit

society and contribute to the research centre’s impact.

Figure 10: Representation of Seduce2.0 project

publications created using Power BI.

Dissemination Activities serve to enable the

transmission of knowledge from the research centre

to diverse stakeholders, encompassing other

researchers, policymakers, industry experts, and the

general public. This guarantees that the research

findings are readily available and practical for a wider

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177

audience beyond the academic community. Research

centres can foster and enhance connections with other

research institutions, universities, and organisations

by disseminating research outputs through

conferences, workshops, and publications (Figure

11). Networking is crucial for cultivating a

cooperative research environment. Additionally, they

enhance the enduring significance and influence of

research findings by assuring their ongoing relevance.

It contributes to ensuring the creation of a sustainable

legacy for the research centre.

Figure 11: Pages created by Power BI with Dissemination

Activities data.

Furthermore, efforts have been made to ensure the

accessibility and responsiveness of the platform,

making it compatible with various devices and screen

sizes. Users can easily click on any topic of interest

to dive deeper and explore more detailed information.

Additionally, the dashboards offer indicators side by

side, facilitating data-driven decision-making.

The quality of data directly impacts the excellence

of study findings. Accurate, dependable, and correct

data are essential for producing strong discoveries,

publications, and contributions to the scientific

community. Research centres focus heavily on data-

driven decision-making. Accurate and relevant data is

essential to make informed and evidence-based

decisions, whether it's for planning future initiatives,

allocating resources, or developing strategies. Data

enables research centres to establish a baseline for

their discoveries by comparing them to current

knowledge and evaluating their findings

concerning previous studies.

Data play a crucial role in the scientific method,

exerting influence over research’s calibre, rigour, and

significance. This study is in line with other studies

(Szołtysik, 2017; Zheng, 2018) that reveal the

importance of applying visualisation information

with BI tools, where is possible to convert

unprocessed data into valuable and practical

information for human consumption.

5 CONCLUSIONS

This study focuses on developing dashboards using

InfoVis and BI tools to effectively present research

project results and impacts.

The DigitalOBS team collaborated with students

from the Department of Communication and Art,

participating in the Students@DigiMedia initiative.

The students' involvement not only provided them

with valuable hands-on experience in data

visualization but also brought fresh perspectives and

innovative ideas to the project. Overall, this

collaboration between the DigitalOBS team and the

Students@DigiMedia initiative demonstrated the

power of interdisciplinary collaboration in research

investigation.

Together, the team created a graphic model using

InfoVis techniques to effectively communicate a

comprehensive set of information related to the

results of scientific projects. The integration of

InfoVis tools and AI technology has greatly enhanced

the presentation of data and can facilitate knowledge-

sharing and decision-making processes.

This study developed a Power BI report, each

dedicated to a specific aspect of the research projects.

By providing researchers with intuitive and visually

appealing tools to present their project results, this

study has the potential to enhance collaboration,

facilitate data-driven decision-making, and accelerate

the advancement of research in the digital media field.

The findings of this study not only benefit

DigitalOBS by allowing analysis of the research

project's outcomes but also showcase the potential of

InfoVis and AI tools in the academic and research

context.

ACKNOWLEDGEMENTS

This work is financially supported by national funds

through FCT – Foundation for Science and

Technology, I.P., under the project

UIDB/05460/2020.

REFERENCES

Andrienko, N., Andrienko, G., Miksch, S., Schumann, H.,

Wrobel, S. (2021). A theoretical model for pattern

discovery in visual analytics. Visual Informatics, 5, 23-

42.

Ansari, B., Barati, M., Martin, E.G. (2022). Enhancing the

usability and usefulness of open government data: A

comprehensive review of the state of open government

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

178

data visualization research. Government Information

Quartely, 39(1), 1-15.

Bacic, D., Fadlalla, A. (2016). Business information

visualization intellectual contributions: An integrative

framework of visualization capabilities and dimensions

of visual intelligence. Decision Support Systems, 89,

77–86.

Becker, L. T., Gould, E. M. (2019). Microsoft power BI:

Extending excel to manipulate, analyze, and visualize

diverse data. Serials Review, 45(3), 184-188.

Binder, H., Blettner, M. (2015). Big data in medical

science--a biostatistical view. Dtsch Arztebl Int.,

112(9), 137-142.

Djenontin, I. N. S., Meadow, A. M. (2018). The art of co-

production of knowledge in environmental sciences and

management: lesson from international practice.

Environmental Management, 61, 885-903.

https://doi.org/10.1007/s00267-018-1028-3

Frey, K., Widmer, T., (2009). The role of efficiency

analysis in legislative reforms in Switzerland. In 5th

ECPR General Conference, Potsdam, Germany

https://www.zora.uzh.ch/id/eprint/26718/8/Frey1.pdf

Hepworth, K. (2016). Big Data Visualization: Promises &

Pitfalls. Communication Design Quarterly, 4(4), 7-19.

Horttanainen, P., Virrantaus, K. (2004). Uncertainty

evaluation of military terrain analysis results by

simulation and visualization. In Proc. 12th Int. Conf. on

Geoinformatics, 474-480.

Jiang, T., Hou, Y., Yang, J. (2023). Literature Review on

the Development of Visualization Studies (2012–

2022). In The 3rd IEEE International Conference on

Electronic Communications, Engineering Proceedings,

38(1), 1-10.

Khatuwal, V. S., Puri, D. (2022, April). Business

Intelligence Tools for Dashboard Development.

In 2022 3rd International Conference on Intelligent

Engineering and Management (ICIEM) (pp. 128-131).

IEEE.

Lima, M. (2011). Visual Complexity – Mapping Patterns of

Information. Princeton Architectural Press.

Orts, D. (2004). Dashboard development and deployment.

Bellevue: Noetix Corporation.

Patrício, M. T., Alves, J. E., Alves, E., Mourato, J., Santos,

P., Valente, R. P. (2018). Avaliação do desempenho da

investigação aplicada no ensino superior politécnico:

Construção de um modelo. Sociologia, Problemas e

Práticas, (86), 69-89. https://journals.openedition.org/

spp/4053

Pinto, M., Raposo, R., Ramos, F. (2012). Comparison of

Emerging Information Visualization Tools for Higher

Education. In 16

International Conference on

Information Visualization, 100-105.

Plaisant, C., Shneiderman, B. (2022). Lightning and

Thunder: The Early Days of Interactive Information

Visualization at the University of Maryland. In IEEE

Computer Graphics and Applications, 42(4), 103–113.

Saenen, B., Morais, R., Gaillard, V., Borrell-Damián, L.

(2019). Research Assessment in the Transition to Open

Science: 2019 EUA Open Science and Access Survey

Results. European University Association.

Santos, J. (2022). On the role and assessment of research at

European Universities of Applied Sciences. Journal of

Higher Education Theory and Practice, 22(14).

Shirato, G., Andrienko, N., Andrienko, G. (2023).

Identifying, exploring, and interpreting time series

shapes in multivariate time intervals. Visual

Informatics, 7, 77-91.

Shneiderman, B. (1996). The eyes have it: a task by data

type taxonomy for information visualizations. In

Proceedings of IEEE Symposium on Visual Languages,

336–343.

Sorapure, M. (2019). Text, Image, Data, Interaction:

Understand Information Visualization. Computers and

Composition, 54, 1-16.

Szołtysik, M. (2017). Processes of Creating Infographics

for Data Visualization. In: Goluchowski, J.,

Pankowska, M., Linger, H., Barry, C., Lang, M.,

Schneider, C. (eds) Complexity in Information Systems

Development. Lecture Notes in Information Systems

and Organisation, vol 22. Springer, Cham.

https://doi.org/10.1007/978-3-319-52593-8_11

Tymoshchuk, O., Silva, M., Oliveira, L., Zagalo, N. (2024).

From Data to Insights: Research Centre Performance

Assessment Model (PAM). In Proc. 26

International

Conference on Enterprise Information Systems. (in

press).

Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U.,

Kortuem, G. (2016). Creating an Understanding of Data

Literacy for a Data-driven Society. The Journal of

Community Informatics, 12(3), 9-26.

Zheng, J. G. (2018). Data Visualization for Business

Intelligence. In Munoz, J. M. (ed.), Global Business

Intelligence, Chapter 6. Taylor & Francis.

Zhu, S., Sun, G., Jiang, Q., Zha, M., Liang, R. (2020). A

survey on automatic infographics and visualization

recommendations. Visual Informatics, 4, 24-40.

The Power of Information Visualization for Understanding the Impact of Digital Media Projects

179