Terms like ‘big data’, ‘data science’, and ‘data visualisation’ have become buzzwords in recent years and are increasingly intertwined with journalism. Data visualisation may further blur the lines between science communication and graphic design. Our study is situated in these overlaps to compare the design of data visualisations in science news stories across four online news media platforms in South Africa and the United States. Our study contributes to an understanding of how well-considered data visualisations are tools for effective storytelling, and offers practical recommendations for using data visualisation in science communication efforts.
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White paper Media Future Week Almere Het jaar 2011 werd door Neelie Kroes (Europees Commissaris ‘Digitale Agenda’) afgesloten met de presentatie van een nieuwe Europese richtlijn die ervoor moet zorgen dat overheden meer data voor hergebruik ter beschikking stellen. Open data is volgens Kroes 'goud waard'. Ook binnen de Nederlandse overheid groeit de aandacht voor open data. In de door het rijk geïnitieerde wedstrijd ‘Apps voor Nederland’ (http://www.appsvoornederland.nl/) konden deelnemers mobiele applicaties ontwikkelen met beschikbare overheidsdata. Onder die ontwikkelaars waren eerstejaars studenten Communication & Media Design van de Hogeschool Utrecht. Zij hebben eind vorig jaar enthousiast gepionierd en succesvolle concepten bedacht die gezien de overeenkomsten met de ‘prijswinnende applicaties’ (http://www.appsvoornederland.nl/nieuws/winnaars‐apps‐voor‐nederland‐bekend) zeker kans van slagen zouden hebben. Zie bijvoorbeeld de web‐based app ‘iHBO’ (http://www.appsvoornederland.nl/apps/ihbo) van Rein Krijgsman, Robert van Assenbergh en Pjotr Sen. Deze applicatie biedt aankomend studenten, ouders en decanen informatie op maat om een weloverwogen en goed geïnformeerde studiekeuze te kunnen maken. Het was volgens de studenten leuk en leerzaam om nieuwe dienstverlenende
The traffic safety of cyclists is under pressure. The number of fatalities and injuries is increasing, and the number of single-bicycle accidents is on the rise. However, from a traffic safety perspective, the most concerning trend is the growing number of incidents between motorized vehicles and cyclists. In addition to infrastructural solutions, such as more segregated and wider bike lanes, both industry and government are exploring technological developments to better safeguard cyclist safety. One of the technological solutions being considered is the use of C-V2X communication. C-V2X, Cellular Vehicle-to-X, is a technology that enables short-range signal exchanges between road users, informing them of each other's presence. C-V2X can be used, for example, to alert drivers via dedicated in-car information systems about the presence of cyclists on the road (e.g. at crossings). Although the technology and chipsets have been developed, the application of C-V2X to improve cyclist safety has not yet been thoroughly investigated. Therefore, HAN, Gazelle, and ARK Infomotives are researching the impact of C-V2X (on cyclist safety). Using advanced simulations with a digital twin in an urban environment and rural environment, the study will analyze how drivers respond to cyclist presence signals and determine the maximum penetration rate of ‘connected’ cyclists. Based on this, a pilot study will be conducted in a controlled environment on HAN terrain to validate the direction of the simulation results. The project aligns with the Missiegedreven Innovatiebeleid and the KIA Sleuteltechnologieën, specifically within application of digital and information technologies. This proposal aligns with the innovation domain of Semiconductor Technologies by applying advanced sensor and digital connectivity solutions to enhance cyclist safety. The project fits within the theme of Sleuteltechnologieën en Duurzame Materialen of the strategic research agenda of the VH by utilizing digital connectivity, sensor fusion, and data-driven decision-making for safer mobility solutions.
In the past decades, we have faced an increase in the digitization, digitalization, and digital transformation of our work and daily life. Breakthroughs of digital technologies in fields such as artificial intelligence, telecommunications, and data science bring solutions for large societal questions but also pose a new challenge: how to equip our (future)workforce with the necessary digital skills, knowledge, and mindset to respond to and drive digital transformation?Developing and supporting our human capital is paramount and failure to do so may leave us behind on individual (digital divide), organizational (economic disadvantages), and societal level (failure in addressing grand societal challenges). Digital transformation necessitates continuous learning approaches and scaffolding of interdisciplinary collaboration and innovation practices that match complex real-world problems. Research and industry have advocated for setting up learning communities as a space in which (future) professionals of different backgrounds can work, learn, and innovate together. However, insights into how and under which circumstances learning communities contribute to accelerated learning and innovation for digital transformation are lacking. In this project, we will study 13 existing and developing learning communities that work on challenges related to digital transformation to understand their working mechanisms. We will develop a wide variety of methods and tools to support learning communities and integrate these in a Learning Communities Incubator. These insights, methods and tools will result in more effective learning communities that will eventually (a) increase the potential of human capital to innovate and (b) accelerate the innovation for digital transformation
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
