In her inaugural lecture, Sabine Niederer presents visual methodologies that take into account the contemporary state of digital images and demonstrates how visualizations may be put to use for collaborative research.
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The visual representation of Information System (IS) artefacts is an important aspect in the practical application of visual representations. However, important and known visual representation principles are often undervalued, which could lead to decreased effectiveness in using a visual representation. Decision Management (DM) is one field of study in which stakeholders must be able to utilize visual notations to model business decisions and underlying business logic, which are executed by machines, thus are IS artefacts. Although many DM notations currently exist, little research actually evaluates visual representation principles to identify the visual notations most suitable for stakeholders. In this paper, the Physics of Notations framework of Moody is operationalized and utilized to evaluate five different DM visual notations. The results show several points of improvement with regards to these visual notations. Furthermore, the results could show the authors of DM visual notations that well-known visual representation principles need to be adequately taken into account when defining or modifying DM visual notations.
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In this paper we present visual methodologies attuned to the networked nature of digital images. First, we describe approaches to image research in which images are not separated from their network, but rather studied 'en groupe'. Here, we contrast approaches that treat images as data, and those that regard images as content. Second, we focus on the production of images for digital research, presenting three of their functions: a) the creation of diagrams that facilitate collaboration in interdisciplinary research teams; b) the use of visualizations for cross-platform image analysis; and c) designing images for public participation. Most importantly, such visualizations are not used to form the esthetic culmination of analytical work, but are rather functional tools for digital research that serve parts of the entire research process, from its formulation and operationalization to the engagement of a broader public.
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For environmental governance to be more effective and transformative, it needs to enhance the presence of experimental and innovative approaches for participation. This enhancement requires a transformation of environmental governance, as too often the (public) participation process is set up as a formal obligation in the development of a proposed intervention. This article, in search of alternatives, and in support of this transformation elaborates on spaces where participatory and deliberative governance processes have been deployed. Experiences with two mediated participation methodologies – community art and visual problem appraisal – allow a demonstration of their potential, relevance and attractiveness. Additionally, the article analyzes the challenges that result from the nature of these arts-based methodologies, from the confrontational aspects of voices overlooked in conventional approaches, and from the need to rethink professionals’ competences. Considering current environmental urgencies, mediated participation and social imaginaries still demonstrate capacities to open new avenues for action and reflection.
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Studying images on social media introduces several challenges that relate to the size of datasets and the different meaning-making grammars of social visuality; or as aptly pointed out by others in the field, it means ‘studying the qualitative on a quantitative scale’. Although cultural analytics provides an automated process through which patterns can be detected in large numbers of images, this methodology doesn’t account for other modalities of the image than the image itself. However, images circulating social media can (and should) be analyzed on the level of their audience as the latter is co-creating the meaning of images. Bridging the study of platform affordances and affect theory, this paper presents a novel methodology that repurposes Facebook Reactions to infer collective attitudes and performative emotional expressions vis á vis images shared on the large Syrian Revolution Network public page (+2M). We found visual patterns that co-occur with certain collective combinations of buttons, displaying how socio-technical features shape the discursive frameworks of online publics.
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This study investigates how pre-service Teachers of English in Bilingual Streams’ (TEBs) perceptions of plurilingualism are elicited through carrying out small-scale research with learners. It builds on previous studies showing positive relations between teacher education and shifts in pre-service teachers’ predispositions towards plurilingual education, particularly when opportunities for critical reflection on the interplay between course- and field work is emphasised. TEBs (N=6) were introduced to visual and spoken data collection methods consisting of language mapping and focus group interviews during coursework and administered these during fieldwork. Spoken and written research reports were analysed deductively using language ideologies adapted from Ricklefs (2023). Results show all participants have a positive disposition to plurilingualism on completion of the course and fieldwork, particularly in relation to valuing plurilingualism as a potential resource in CLIL. Implementing multimodal research methods makes linguistic variation visible and draws out learner experiences. This helps TEBs make connections between their own beliefs and experiences, and those articulated by their learners and in their placement schools. This approach builds on the dynamic nature of the interaction between teacher beliefs and practices and confirms that critical reflection can play a key role in shaping TEBs’ dispositions towards plurilingualism and plurilingual pedagogies.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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Mapping Microplastics was a first exploratory workshop (part of the Entrepreneurship Program at HvA) with researchers and entrepreneurs to map how the public discourse on microplastics develops on Twitter.The preparation of this workshop followed a hybrid approach in three steps: preliminary mapping, joint interpretation and annotation, map redesign and feedback. The preliminary mapping was performed by designing a query to collect tweets around the topic of Microplastics. To perform the data collection and preliminary analysis we used TCAT (Twitter Capturing and Analysis Toolset), a tool developed by the Digital Methods Initiative at the University of Amsterdam. A set of four maps was designed to address different sub questions through different visual models: networks of hashtags and users, image grids organized by time and frequency, alluvial diagrams and lists of most interacted with tweets. These maps were used in a joint interpretative hybrid session: the visual material was printed and sent to each partner. With the facilitation of designers and researchers, entrepreneurs annotated the printed maps in parallel online sessions.
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In order to be successful in today’s competitive environment, brands must have well-established identities. Therefore, during the branding process it is necessary to attribute personality traits and visual elements that best represent the desired identity of the brand. With the recent advances in communication, scholars have analyzed how different visual elements (e.g., logo, typography, color) can visually represent the desired brand personality. However, these elements are typically analyzed separately, since few studies show the association of personality traits with the set of visual elements of the brand (the well-known “visual identity”). Therefore, this work aims to develop a methodological framework that allows the design of visual identity based on the Dimensions of Brand Personality, by assigning a set of visual elements (colors, typographies, and shapes) to each dimension (Sincerity, Excitement, Competence, Sophistication and Ruggedness) suggested by Aaker in 1997. Through a quanti-quali approach, the associations suggested in the proposed framework were duly tested through the application of a questionnaire to a sample of consumers, to gather information about their perceptions. Preliminary results suggest that the proposed framework can successfully generate the desired brand personality perception in consumers, according to the design elements used for the creation of the visual brand identity.
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Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity and retweet counts - to complement the automated assessment of social media images; doing justice to both the visual elements of an image and the contextual elements encoded by networked publics that co-create meaning.
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