000 Informatik, Wissen, Systeme
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Digital qualifiziertes Personal ist die Grundlage für eine zukunftsfähige Wissenschaftslandschaft. Die Omnipräsenz von digitalen Daten und Informationen, von Tools für Kommunikation und Kollaboration stellt nicht nur Wissenschaft und Forschung vor neue Herausforderungen, sondern die gesamte Gesellschaft. Überall bedarf es digitaler Kompetenzen, um mit den Möglichkeiten der digitalen Transformation bewusst und mündig umgehen zu können. Hier bietet die Informationswissenschaft gemeinsam mit der Informatik als Basiswissenschaft für alle Wissenschaften Ansätze und Lösungen, so zum Beispiel im Bereich Data Science und Data Stewardship. Basierend auf einem Diskussionspapier (Attallah et al., 2020) widmeten sich 19 Expert*innen im Kontext eines Interview-Podcasts der AG 6 „Digital qualifiziertes Personal“ der Allianz der Wissenschaftsorganisationen diesem komplexen Thema. Die 15 durchgeführten (Doppel-) Interviews bildeten die Basis für die Datengrundlage und wurden explorativ und softwaregestützt qualitativ analysiert. Ziel war die ergebnisoffene, aber möglichst umfängliche Abbildung und Analyse der von den Expert*innen angesprochenen Handlungsfelder, um unter anderem folgende Forschungsfragen beantworten zu können: Welche Kompetenzen werden für die digitale Qualifikation in den Wissenschaftsorganisationen benötigt? Welche Möglichkeiten und Verantwortlichkeiten für eine Kompetenzvermittlung gibt es? Welchen Beitrag können die Informationswissenschaften leisten?
In November 2022, the US company OpenAI launched its chatbot ChatGPT built on top of its large language models (LLMs). “This chatbot has redefined the standards of artificial intelligence, proving that machines can indeed ‘learn’ the complexities of human language and interaction”, urteilte Forbes in May 2023.
As new trends constantly emerge worldwide, such as ChatGPT, it is necessary to approach the promotion of these tools to the broader population in an appropriate manner. New technologies and artificial intelligence tools simultaneously offer enhanced functionality but also the potential for misuse, such as plagiarism. Therefore, in the home countries of the authors, various discussions have been initiated at numerous universities and other organisations over the years regarding the advantages and disadvantages of these tools and how to best utilize them. It will be important for university teachers to actively integrate tools such as ChatGPT into their teaching and to critically reflect on the necessary competences for the use of such a chatbot. To achieve such learning objectives, the first step is to understand how ChatGPT works and how it can be used. In the following, the focus is on one such application, namely how ChatGPT can be used in cultural marketing.
From shock to shift
(2023)
We present a multi-dimensional, multi-level, and multi-channel approach to data visualization for the purpose of constructive climate journalism. Data visualization has assumed a central role in environmental journalism and is often used in data stories to convey the dramatic consequences of climate change and other ecological crises. However, the emphasis on the catastrophic impacts of climate change tends to induce feelings of fear, anxiety, and apathy in readers. Climate mitigation, adaptation, and protection—all highly urgent in the face of the climate crisis—are at risk of being overlooked. These topics are more difficult to communicate as they are hard to convey on varying levels of locality, involve multiple interconnected sectors, and need to be mediated across various channels from the printed newspaper to social media platforms. So far, there has been little research on data visualization to enhance affective engagement with data about climate protection as part of solution-oriented reporting of climate change. With this research we characterize the unique challenges of constructive climate journalism for data visualization and share findings from a research and design study in collaboration with a national newspaper in Germany. Using the affordances and aesthetics of travel postcards, we present Klimakarten, a data journalism project on the progress of climate protection at multiple spatial scales (from national to local), across five key sectors (agriculture, buildings, energy, mobility, and waste), and for print and online use. The findings from quantitative and qualitative analysis of reader feedback confirm our overall approach and suggest implications for future work.
Tracing and Telling
(2023)
Cultural collections have transcended the boundaries of physical showcases and storage cabinets, evolving into intricate digital webs of interconnected data. The research project Restaging Fashion aimed to expand the possibilities for experiencing the linkages in digitized cultural heritage, focusing on the visualization of collection holdings related to the history of garments. Through graph-based narratives, the project investigated the complex interplay between the linear – and at times tentative – art historical discourses and the non-linear explorations of associated cultural heritage data. The resulting visualizations have been designed with the aim to allow for curated and dynamic exploration of the collection along research essays and their interrelated graphs.
To Classify is to Interpret
(2023)
Taxonomy building is a task that requires interpreting and classifying data within a given frame of reference, which comes to play in many areas of application that deal with knowledge and information organization. In this paper, we explore how taxonomy building can be supported with systems that integrate machine learning (ML). However, relying only on black-boxed ML-based systems to automate taxonomy building would sideline the users’ expertise. We propose an approach that allows the user to iteratively take into account multiple model’s outputs as part of their sensemaking process. We implemented our approach in two real-world use cases. The work is positioned in the context of HCI research that investigates the design of ML-based systems with an emphasis on enabling human-AI collaboration.
This contribution explores the instructive tensions between art-historical research and semantic data modeling. While the potential of LOD-applications is obvious in terms of standardization and precision some aspects of research may not be formally represented. Therefore we propose to bridge LOD and full text descriptions through information visualization.
Journalism has become more data-driven and inherently visual in recent years. Photographs, illustrations, infographics, data visualizations, and general images help convey complex topics to a wide audience. The way that visual artifacts influence how readers form an opinion beyond the text is an important issue to research, but there are few works about this topic. In this context, we research the persuasive, emotional and memorable dimensions of data visualizations and illustrations in journalistic storytelling for long-form articles. We conducted a user study and compared the effects which data visualizations and illustrations have on changing attitude towards a presented topic. While visual representations are usually studied along one dimension, in this experimental study, we explore the effects on readers' attitudes along three: persuasion, emotion, and information retention. By comparing different versions of the same article, we observe how attitudes differ based on the visual stimuli present, and how they are perceived when combined. Results indicate that the narrative using only data visualization elicits a stronger emotional impact than illustration-only visual support, as well as a significant change in the initial attitude about the topic. Our findings contribute to a growing body of literature on how visual artifacts may be used to inform and influence public opinion and debate. We present ideas for future work to generalize the results beyond the domain studied, the water crisis.
This article investigates data journalism epistemology through Michel Foucault’s definition of power. The growing demand for data-savvy reporters with computational skills has been proven to shift the newsrooms’ culture in media companies across the globe. Previous research has documented journalists’ shift towards a data-centred epistemology and the increasingly important role of computation and data-driven practices in newsrooms. By focusing on inexperienced journalists as they mobilise data journalism for the first time, this research openly discusses its epistemology as a form of Foucauldian power: which epistemological tensions – individual or structural – emerge among newcomers when reporting extensively with data? The article presents the case of a newly formed data journalism team at taz, a German cooperative media company. It is based on qualitative data collected through action research, namely six in-depth interviews, auto-ethnographic notes and self-reflective inquiries. Data are framed through Foucault’s concepts of power and will to truth. Results show the discord between data journalism epistemology and its practical implementation. Despite prioritising democratic access to data, technologies and non-hierarchical forms of cooperation, data journalism remains largely dependent on traditional methods and culture.