Showing the results of the project Revealing design (Zichtbaar slimmer): Data physicalization for the 21st Century
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Voor het project Zichtbaar slimmer, workshops datafysicalisatie en het vak HBO-ICT vak datavisualisatie zijn we op zoek gegaan naar interessante datafysicalisaties. Hiervoor hebben we een collectie van datafysicalisatie voorbeelden -het tastbaar maken van data- in kaart gebracht, waaronder een Information Physicalisation bord op Pinterest - https://www.pinterest.com/mekanis/information-physicalizationDeze bevat ook een Design canvas: Wat maakt een goede datafysicalisatie?Er blijkt namelijk veel behoefte aan voorbeelden om te helpen bij een beter begrip van datafysicalisatie en de praktische (ontwerp)aanpak.Datafysicalisatie draait om fysieke 3D representaties die je kunt beleven en aanraken, en gaat dus een stap verder dan datavisualisatie op plat 2D papier. Het tastbaar maken van informatie kan leiden tot meer inzicht (bijvoorbeeld hoeveel suikerklontjes er in bepaalde etenswaar zit), wat vervolgens weer kan leiden tot kritische discussie of gedragsverandering.
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This work explores the potential of doing collaborative data physicalization for discussing (un)sustainable practices. For this purpose, it draws on experiences from several data physicalization workshops during the period of 2018-2022, conducted in Amsterdam, The Netherlands, which were available to mostly inexpert groups of people, including almost a hundred primary school students. This paper particularly focuses on a recent held dataphys workshop with over 20 adult participants, such as including international students and climate activists. Based on learner reports (self-assessment questionnaires) (N=20), and observations, it was found that the process of making data physicalizations in workshop and educational settings can be beneficial for engaging in collaborative creative and critical discussion of (un)sustainable practices. Particularly, the participants positively indicated to have learned from the dataphys workshop on a 5-point Likert scale and agreed that it enabled (1) critical thinking, (2) data understanding, (3) creativity, (4) collaboration, and (5) awareness of (un)sustainable practices. This paper presents the workshop format, including ingredients such as live cartoon capturing, and challenges in realizing such value in the context of sustainability, such as including a wider public, the conscious use of data and materials, and discussable effective outcomes.
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Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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The Technical Manual for the digital evaluation tool QualiTePE supports users of the QualiTePE tool in creating, conducting and analysing evaluations to record the quality of teaching in physical education. The information on the General Data Protection Regulation (GDPR) instructs users on how to anonymise the data collection of evaluations and which legal bases apply with regard to the collection of personal data. The technical manual for the digital evaluation tool QualiTePE and the information on the General Data Protection Regulation (GDPR) are available in English, German, French, Italian, Spanish, Dutch, Swedish, Slovenian, Czech and Greek.
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Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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Following the rationale of the current EU legal framework protecting personal data, children are entitled to the same privacy and data protection rights as adults. However, the child, because of his physical and mental immaturity, needs special safeguards and care, including appropriate legal protection. In the online environment, children are less likely to make any checks or judgments before entering personal information. Therefore, this paper presents an analysis of the extent to which EU regulation can ensure children’s online privacy and data protection.
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Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.
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The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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