@article{krautter_operationalisierung_2023,
	title = {Operationalisierung},
	rights = {Creative Commons Attribution Share Alike 4.0 International},
	doi = {10.17175/WP_2023_010},
	journal = {Zeitschrift für digitale Geisteswissenschaften – {ZfdG}},
	author = {Krautter, Benjamin and Pichler, Axel and Reiter, Nils},
	date = {2023-06-21},
	langid = {german},
	note = {Working Paper 2 der Zeitschrift für digitale Geisteswissenschaften},
	year = "2023",
}

@article{executable_books_community_2021_2561065,
  author    = {{Executable Books Community}},
  title     = {Jupyter Book},
  year      = 2021,
  publisher = {Zenodo},
  journal   = {Zenodo},
  version   = {v0.10},
  doi       = {10.5281/zenodo.2561065}
}

@article{hattie2007,
  title     = {The {{Power}} of {{Feedback}}},
  author    = {Hattie, John and Timperley, Helen},
  year      = {2007},
  journal   = {Review of Educational Research},
  volume    = {77},
  number    = {1},
  pages     = {81--112},
  publisher = {American Educational Research Association},
  issn      = {0034-6543},
  doi       = {10.3102/003465430298487},
  abstract  = {Feedback is one of the most powerful influences on learning and achievement, but this impact can be either positive or negative. Its power is frequently mentioned in articles about learning and teaching, but surprisingly few recent studies have systematically investigated its meaning. This article provides a conceptual analysis of feedback and reviews the evidence related to its impact on learning and achievement. This evidence shows that although feedback is among the major influences, the type of feedback and the way it is given can be differentially effective. A model of feedback is then proposed that identifies the particular properties and circumstances that make it effective, and some typically thorny issues are discussed, including the timing of feedback and the effects of positive and negative feedback. Finally, this analysis is used to suggest ways in which feedback can be used to enhance its effectiveness in classrooms.},
  langid    = {english},
  language  = {en}
}

@article{kulhavy1977,
  title     = {Feedback in Written Instruction},
  author    = {Kulhavy, Raymond W.},
  year      = {1977},
  journal   = {Review of Educational Research},
  volume    = {47},
  number    = {2},
  pages     = {211--232},
  publisher = {American Educational Research Association},
  issn      = {0034-6543},
  doi       = {10.3102/00346543047002211},
  langid    = {english},
  language  = {en}
}

@inproceedings{neuroth2025,
  title     = {Das {{QUADRIGA-Datenkompetenzframework}} Als {{Basis}} F{\"u}r Die {{Entwicklung}} von {{Lehr-}} Und {{Lernressourcen}}},
  booktitle = {{{ISI}} 2025},
  author    = {Neuroth, Heike and Petras, Vivien and Schnaitter, Hannes and Seltmann, Melanie and Walter, Paul},
  address   = {Chemnitz},
  year      = {eingereicht}
}

@misc{noauthor_supported_nodate,
	title = {Supported {Connectors}},
	url = {https://help.tableau.com/current/pro/desktop/en-us/exampleconnections_overview.htm},
	abstract = {Follow the link below for information on  how to
connect to your specific data},
	language = {en-us},
	urldate = {2025-05-16},
}

@misc{noauthor_big_nodate,
	title = {Big {Data} {Pipelines}},
	url = {https://plotly.com/dash/big-data-for-python},
	abstract = {Dash Enterprise supports turnkey connections to popular backends in Python – Vaex, Dask, Datashader, RAPIDS, Databricks (PySpark), Snowflake, and Postgres.},
	language = {en},
	urldate = {2025-05-16},
}

@misc{noauthor_managing_nodate,
	title = {Managing {Data} {Sources} {\textbar} {Dash} for {Python} {Documentation} {\textbar} {Plotly}},
	url = {https://dash.plotly.com/dash-enterprise/managing-data-sources?de-version=5.7},
	abstract = {Data sources allow you to share data stored in various cloud providers with other licensed users.},
	urldate = {2025-05-16},
}

@misc{noauthor_vizql_nodate,
	title = {{VizQL} {Data} {Service}: {Extend} {Your} {Data} {Beyond} {Visualizations}},
	url = {https://www.tableau.com/blog/vizql-data-service-beyond-visualizations},
}

@misc{noauthor_tableau-plattformarchitektur_nodate,
	title = {Tableau-{Plattformarchitektur}},
	url = {https://help.tableau.com/current/blueprint/de-de/bp_server_architecture.htm},
	abstract = {Tableau Server bietet Ihren Benutzer eine komplette moderne Analytics-Plattform},
	language = {de-de},
	urldate = {2025-05-16},
}

@misc{noauthor_20_nodate,
	title = {20 {Beispiele} für interaktive {Power} {BI}-{Dashboards}},
	url = {https://zephyrnet.com/de/20-Beispiele-f%C3%BCr-interaktive-Power-BI-Dashboards/},
}

@misc{noauthor_gies_2021,
	title = {Gieß den {Kiez} - {Nutzungsdaten} - {GovData}},
	url = {https://www.govdata.de/suche/daten/giess-den-kiez-nutzungsdaten},
	abstract = {Gieß den Kiez ist ein Projekt des CityLAB Berlin das die Berliner Stadtbäume vor dem Vertrocknen schützen soll. Auf einer Karte werden dabei über 625.000 Str...},
	language = {de},
	urldate = {2025-03-28},
	month = jun,
	year = {2021},
}

@article{wood_diabetes_2019,
	title = {Diabetes {Mobile} {Care}: {Aggregating} and {Visualizing} {Data} from {Multiple} {Mobile} {Health} {Technologies}},
	volume = {2019},
	issn = {2153-4063},
	shorttitle = {Diabetes {Mobile} {Care}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568104/},
	abstract = {As the appeal and use of mobile health (mHealth) technologies continues to grow, where does mHealth fit into clinical practice? This article explores the approach and obstacles encountered when integrating mHealth data into existing clinical frameworks and explores data visualization design tradeoffs. Specifically, this paper discusses the successes and challenges that arose when using commercial mHealth technologies, synthesizing multiple mHealth device data, and tailoring visualizations based on iterative feedback from type II diabetes mellitus patients. This research aims to influence the development of patient portals within electronic health records by understanding and addressing the challenges involved in acquiring, interpreting, and displaying this data set. In particular, we need to ensure that the presentation of these data is accessible and understandable by diverse populations.},
	urldate = {2025-05-16},
	journal = {AMIA Summits on Translational Science Proceedings},
	author = {Wood, Eleanor and Yang, Qing and Steinberg, Dori and Barnes, Angel and Vaughn, Jacqueline and Vorderstrasse, Allison and Crowley, Matthew and Henriquez, Craig and Streicher, Martin and Bass Blue, Daniel and Choi, Susie and Shaw, Ryan J.},
	month = may,
	year = {2019},
	pmid = {31258972},
	pmcid = {PMC6568104},
	pages = {202--211},
}

@article{walker_tools_2016,
	title = {Tools for {Interactive} {Visualization} of {Global} {Demographic} {Concepts} in {R}},
	volume = {4},
	issn = {2164-7070},
	doi = {10.1007/s40980-016-0029-1},
	abstract = {Data visualization is a core component of the demographer’s workflow, as visualizations are essential to communicate the findings of demographic research. Recent advances in interactive data visualization have made it easier to produce dynamic web-based graphics in a variety of computing environments, including R, a popular tool for demographers. This article illustrates how to produce interactive charts and maps of demographic data in R using Plotly and Shiny, two frameworks for web-based visualization. Data for the examples come from idbr, a new R package to download demographic indicators from the US Census Bureau’s International Data Base.},
	language = {en},
	number = {3},
	urldate = {2025-05-16},
	journal = {Spatial Demography},
	author = {Walker, Kyle E.},
	month = oct,
	year = {2016},
	keywords = {Applied Demography, Data and Information Visualization, Data visualization, Demography, Global, Population and Demography, R, Social Indicators, Spatial Demography, Statistical Software},
	pages = {207--220},
}

@misc{vasundhara_data_nodate,
	title = {{Data} {visualization} {view} {with} {tabelau}},
	url = {https://www.mukpublications.com/resources/sma%20v25-1-18-final.pdf},
	author = {Vasundhara, S.},
	year = {2021},

}

@incollection{odonnell_interaktive_2020,
	address = {Wiesbaden},
	title = {Interaktive {Datenvisualisierung} statistischer {Daten}},
	isbn = {9783658295622},
	abstract = {Datenvisualisierungen begegnen uns täglich. Die Menge an Daten in Unternehmen, Behörden und auch in der Wissenschaft steigt stetig an. Die Visualisierung dieser Daten gewinnt damit zunehmend an Bedeutung. Das vorliegende Kapitel beschreibt anhand anschaulicher Beispiele Grundsätze, Methoden und Werkzeuge zur (interaktiven) Datenvisualisierung, insbesondere statistischer Daten. Zunächst werden Beispiele für statische und interaktive Datenvisualisierung vorgestellt. Im Anschluss werden Grundregeln der Visualisierung erläutert und Werkzeuge vorgestellt, die sich für die Erstellung von (interaktiven) Visualisierungen eignen. Hierbei gehen die Autoren insbesondere auf frei verfügbare Werkzeuge ein.},
	language = {de},
	urldate = {2025-05-16},
	booktitle = {Interaktive {Datenvisualisierung} in {Wissenschaft} und {Unternehmenspraxis}},
	publisher = {Springer Fachmedien},
	author = {O’Donnell, Daniel and Zimmer, Frank},
	editor = {Kahl, Timo and Zimmer, Frank},
	year = {2020},
	doi = {10.1007/978-3-658-29562-2_4},
	pages = {67--93},
}

@misc{krishnan_research_nodate,
	title = {Research {Data} {Analysis} with {Power} {BI}},
	url = {https://ir.inflibnet.ac.in:8443/ir/bitstream/1944/2116/1/24.pdf},
	author = {Krishnan, Vijay and Bharanidharan, S and Krishnamoorthy, G},
}

@inproceedings{khedr_interactive_2021,
	title = {Interactive {Visualization} for {Statistical} {Modelling} through a {Shiny} {App} in {R}},
	doi = {10.1109/ICDABI53623.2021.9655841},
	abstract = {The importance of analytics and visualization tools has been growing over the last decades to handle big data which steamed from all aspects of life. The focus of this paper was on visualization as a crucial tool in presenting complex raw data and modelling results to provide easy-to-understand actionable information that facilitate decision-making. However, limited research distinguished between “data visualization” and “model visualization”, which has been clearly made in this paper. Furthermore, this paper aimed to shed light on the importance of interactive visualizations to compliment statistical data modelling using R and Shiny for its advanced capabilities. Specifically, a methodology has been proposed based on a hybrid development lifecycle that adopts the Agile Software Development Lifecycle and the Data Analytics Lifecycle. Finally, by presenting a case study to model the dynamics of COVID-19, it was found that R and Shiny alongside the proposed hybrid development lifecycle significantly reduced the amount of time required to build visually interactive applications. The reported results highlighted the effectiveness of the adopted approach in assisting and guiding researchers and developers in building interactive applications that leverage Big Data Analytics.},
	urldate = {2025-05-16},
	booktitle = {2021 {International} {Conference} on {Data} {Analytics} for {Business} and {Industry} ({ICDABI})},
	author = {Khedr, Ahmed and Hilal, Sawsan},
	month = oct,
	year = {2021},
	keywords = {Analytical models, Big Data, Buildings, COVID-19, Data Analytics, Data analysis, Data visualization, Interactive Applications, R, Shiny, Smoothing methods, Visualization},
	pages = {332--337},
}

@misc{denglishbi_bi-losungsarchitektur_nodate,
	title = {{BI}-{Lösungsarchitektur} im {Kompetenzzentrum} - {Power} {BI}},
	url = {https://learn.microsoft.com/de-de/power-bi/guidance/center-of-excellence-business-intelligence-solution-architecture},
	abstract = {Was Sie beim Entwerfen und Entwickeln einer stabilen BI-Plattform beachten sollen},
	language = {de-de},
	urldate = {2025-05-16},
	author = {{denglishbi}},
}

@misc{davidiseminger_data_nodate,
	title = {Data sources in {Power} {BI} {Desktop} - {Power} {BI}},
	url = {https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-data-sources},
	abstract = {See the available data sources in Power BI Desktop, how to connect to them, and how to export or use data sources as PBIDS files.},
	language = {en-us},
	urldate = {2025-05-16},
	author = {{davidiseminger}},
}

@book{heinicker_anderes_visualisieren_2024,
	title = {Anderes {Visualisieren} - Zur Kritik der {Datengestaltung}},
	doi = {10.14361/9783839474822},
	isbn = {9783839474822},
	shorttitle = {Anderes {Visualisieren}},
	language = {de},
	publisher = {transcrpit Verlag},
	author = {Heinicker, Paul},
	year = {2024},
}

@article{freyberg_visualisierung_2023,
	title = {Visualisierung},
	rights = {Creative Commons Attribution 4.0 International},
	doi = {10.17175/wp_2023_014},
	journal = {Zeitschrift für digitale Geisteswissenschaften – {ZfdG}},
	author = {Freyberg, Linda},
	date = {2023-05-25},
	langid = {german},
	note = {Begriffe der Digital Humanities. Ein diskursives Glossar, Working Paper 2 der Zeitschrift für digitale Geisteswissenschaften},
	year = {2023},
}

@article{schmidt_blick_2020,
	title = {Auf einen Blick verständlich - oder auch nicht},
	url = {https://science.orf.at/stories/3203132/},
	journal = {ORF Science},
	author = {Schmidt, Johanna},
	date = {2020-12-05},
	langid = {german},
	year = {2020},
	urldate = {2026-06-19},
}

@article{greussing_datenvisualisierung_2019,
	title = {Datenvisualisierung vom Publikum her denken},
	url = {https://medienwoche.ch/2019/11/05/datenvisualisierung-vom-publikum-her-denken/},
	journal = {Medienwoche},
	author = {Greussing, Esther},
	date = {2019-11-05},
	langid = {german},
	year = {2019},
	urldate = {2026-06-13},
}

@book{dabbas_interactive_2021,
	title = {Interactive {Dashboards} and {Data} {Apps} with {Plotly} and {Dash}: {Harness} the power of a fully fledged frontend web framework in {Python} – no {JavaScript} required},
	isbn = {9781800560352},
	shorttitle = {Interactive {Dashboards} and {Data} {Apps} with {Plotly} and {Dash}},
	abstract = {Build web-based, mobile-friendly analytic apps and interactive dashboards with PythonKey FeaturesDevelop data apps and dashboards without any knowledge of JavaScriptMap different types of data such as integers, floats, and dates to bar charts, scatter plots, and moreCreate controls and visual elements with multiple inputs and outputs and add functionality to the app as per your requirementsBook DescriptionPlotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways. Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it. Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them. By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.What you will learnFind out how to run a fully interactive and easy-to-use appConvert your charts to various formats including images and HTML filesUse Plotly Express and the grammar of graphics for easily mapping data to various visual attributesCreate different chart types, such as bar charts, scatter plots, histograms, maps, and moreExpand your app by creating dynamic pages that generate content based on URLsImplement new callbacks to manage charts based on URLs and vice versaWho this book is forThis Plotly Dash book is for data professionals and data analysts who want to gain a better understanding of their data with the help of different visualizations and dashboards – and without having to use JS. Basic knowledge of the Python programming language and HTML will help you to grasp the concepts covered in this book more effectively, but it’s not a prerequisite.},
	language = {en},
	publisher = {Packt Publishing Ltd},
	author = {Dabbas, Elias},
	month = may,
	year = {2021},
	note = {Google-Books-ID: b\_gqEAAAQBAJ},
	keywords = {Computers / Data Science / Data Modeling \& Design, Computers / Data Science / Data Visualization, Computers / Data Science / General},
}

@misc{attali_shiny_2020,
	title = {Shiny - {Persistent} data storage in {Shiny} apps},
	url = {https://shiny.posit.co/r/articles/build/persistent-data-storage/},
	abstract = {Shiny is a package that makes it easy to create interactive web apps using R and Python.},
	language = {en},
	urldate = {2025-05-16},
	journal = {Shiny},
	author = {Attali, Dean},
	month = nov,
	year = {2020},
}

@misc{arkharov_power_2024,
	title = {Power {BI} {Architecture} - {Explained} with {Diagrams} \& {Examples}},
	url = {https://blog.coupler.io/power-bi-architecture/},
	abstract = {Explore the Power BI architecture with this complete guide. Learn about the core components and how they work together as a reporting platform.},
	language = {en-US},
	urldate = {2025-05-16},
	journal = {Coupler.io Blog},
	author = {Arkharov, Denys},
	month = may,
	year = {2024},
}


@article{Janes2013Effective,
 author = {Janes, Andrea and Sillitti, Alberto and Succi, Giancarlo},
 year = {2013},
 month = {01},
 pages = {17-24},
 title = {Effective dashboard design},
 volume = {26},
 journal = {Cutter IT Journal}
}

@article{sarikaya2019,
author = {Sarikaya, Alper and Correll, Michael and Bartram, Lyn and Tory, Melanie and Fisher, Danyel},
title = {What Do We Talk About When We Talk About Dashboards?},
year = {2019},
issue_date = {Jan. 2019},
publisher = {IEEE Educational Activities Department},
address = {USA},
volume = {25},
number = {1},
issn = {1077-2626},
doi = {10.1109/TVCG.2018.2864903},
abstract = {Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.},
journal = {IEEE Transactions on Visualization and Computer Graphics},
month = jan,
pages = {682–692},
numpages = {11}
}