Creating User-Friendly Dashboards: From Concept to Implementation

Augmented reality (AR) has gained exceptional importance in supporting task performance. Particularly, in quality assurance (QA) processes in the automotive sector AR offers a diversity of use cases. In this paper we propose an interface design which projects information as a digital canvas on the surface of vehicle components. Based on a requirement analysis, we discuss design aspects and describe our application in applying the quality assurance process of a luxury automaker. The application includes a personal view on spatial information embedded in a guided interaction process as a design solution that can be applied to enhance QA processes.

Primate visual systems process natural images in a hierarchical manner: at the early stage, neurons are tuned to local image features, while neurons in high-level areas are tuned to abstract object categories. Standard models of visual processing assume that the transition of tuning from image features to object categories emerges gradually along the visual hierarchy. Direct tests of such models remain difficult due to confounding alteration in low-level image properties when contrasting distinct object categories. When such contrast is performed in a classic functional localizer method, the desired activation in high-level visual areas is typically accompanied with activation in early visual areas. Here we used a novel image-modulation method called SWIFT (semantic wavelet-induced frequency-tagging), a variant of frequency-tagging techniques. Natural images modulated by SWIFT reveal object semantics periodically while keeping low-level properties constant. Using functional magnetic resonance imaging (fMRI), we indeed found that faces and scenes modulated with SWIFT periodically activated the prototypical category-selective areas while they elicited sustained and constant responses in early visual areas. SWIFT and the localizer were selective and specific to a similar extent in activating category-selective areas. Only SWIFT progressively activated the visual pathway from low- to high-level areas, consistent with predictions from standard hierarchical models. We confirmed these results with criterion-free methods, generalizing the validity of our approach and show that it is possible to dissociate neural activation in early and category-selective areas. Our results provide direct evidence for the hierarchical nature of the representation of visual objects along the visual stream and open up future applications of frequency-tagging methods in fMRI.

Molecular biology experiments are generating an unprecedented amount of information from a variety of different experimental modalities. DNA sequencing machines, proteomics mass cytometry and microscopes generate huge amounts of data every day. Not only is the data large, but it is also multidimensional. Understanding trends and getting actionable insights from these data requires techniques that allow comprehension at a high level but also insight into what underlies these trends. Lots of small errors or poor summarization can lead to false results and reproducibility issues in large data sets. Hence it is essential we do not cherry-pick results to suit a hypothesis but instead examine all data and publish accurate insights in a data-driven way. This article will give an overview of some of the problems faced by the researcher in understanding epigenetic changes (which are related to changes in the physical structure of DNA) when presented with raw analysis results using visualization methods. We will also discuss the new challenges faced by using machine learning which can be helped by visualization.

Disease phenotypes can be described as the consequence of interactions among molecular processes that are altered beyond resilience. Here, we address the challenge of assessing the possible alteration of intra- and inter-cellular molecular interactions among processes or cells. We present an approach, designated as “Ulisse”, which complements the existing methods in the domains of enrichment analysis, pathway crosstalk analysis and cell-cell communication analysis. It applies to gene lists that contain quantitative information about gene-related alterations, typically derived in the context of omics or multi-omics studies. Ulisse highlights the presence of alterations in those components that control the interactions between processes or cells. Considering the complexity of statistical assessment of network-based analyses, crosstalk quantification is supported by two distinct null models, which systematically sample alternative configurations of gene-related changes and gene-gene interactions. Further, the approach provides an additional way of identifying the genes associated with the phenotype. As a proof-of-concept, we applied Ulisse to study the alteration of pathway crosstalks and cell-cell communications in triple negative breast cancer samples, based on single-cell RNA sequencing. In conclusion, our work supports the usefulness of crosstalk analysis as an additional instrument in the “toolkit” of biomedical research for translating complex biological data into actionable insights.

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Data visualization enables non-profits to transform complex program, fundraising, and operational data into clear, actionable insights. This Teaching Paper introduces eight practical visualization templates—bar charts, line graphs, pie charts, scatter plots, heat maps, dashboards, geospatial maps, and infographics—explaining their optimal use cases, recommended tools, and adaptation strategies for non-profit contexts. A case study illustrates how combining multiple templates can improve donor engagement reporting. Practical tips for tool selection, design clarity, and avoiding common pitfalls are included, equipping practitioners and scholars with structured methods to enhance decision-making, stakeholder communication, and transparency.

Abstract. This article explores the power of data visualization and its impact on decision-making processes. It begins by highlighting the importance of data visualization in making data more accessible and understandable, emphasizing how visual representations enhance comprehension, facilitate exploration, and support data-driven decision-making. Real-world scenarios are provided to showcase the significant impact of data visualization on decision-making processes. Case studies illustrate how organizations have leveraged visualizations to gain actionable insights and drive strategic outcomes. The article delves into the process of choosing the right visualization techniques, exploring different types of visualization techniques such as charts, graphs, maps, and interactive dashboards. Guidelines for selecting the right visualization method are provided, focusing on effectively conveying desired insights. The article “Data Visualization: Transforming Complex Data into Actionable Insights” emphasizes the need to consider factors such as data complexity, audience, and objectives when choosing visualization techniques. It encourages data analysts to adopt a thoughtful approach in selecting visualizations that best serve the purpose and maximize impact. Design principles for creating visually appealing and informative data visualizations are explored, and open source information is utilized to highlight best practices and common pitfalls to avoid. The article concludes by summarizing the importance of data visualization in transforming complex data into actionable insights. It emphasizes the need for data analysts to prioritize data visualization skills and enhance their visual storytelling abilities to effectively communicate insights, influence stakeholders, and drive meaningful outcomes. Overall, the article provides readers with knowledge, insights, and practical guidance to harness the power of data visualization and unlock the full potential of their data.

OBJECTIVE: To describe all stages of developing strategies and quality control in the Aging and Brain Working Study linked to the Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil – Brazilian Longitudinal Study of Adult Health), a prospective cohort of 15,105 civil servants (aged 35–74) followed up since 2008. METHODS: Based on the sample of participants alive at the beginning of the sample selection in September 2022 (n = 4,566) from ELSA-Brasil (Centro Investigação São Paulo), a representative sample of 2,165 individuals was calculated according to the following criteria and divided into two subsamples: (1) Sample of participants < 70 years old in Wave 3 (2017–2019) randomized according to cognitive trajectory from Wave 1 to 3, n = 1,670; (2) SuperAgers case-control sample, individuals aged ≥ 70 years in Wave 3, based on episodic memory in Wave 3, n = 495 participants (171 cases, 324 controls). The processes implemented to ensure quality control of information prior to data collection were selection of data collection instruments, training and certification of teams, preparation of a manual of definitions and standardized operating procedures, pilot studies, data collection logistics, harmonization of imaging protocols, and creation of an anti-noise device. Given the scope of the study, its multicenter nature, and the diversity of measures involved, this project required the implementation of effective quality assurance and control protocols, ensuring standardization, methodological consistency, and data integrity at all stages. RESULTS: After implementing the quality assurance and control processes, the final stages related to clinical and imaging data collection, management, and continuous monitoring of f ield activities were conducted, with weekly reports and statistical monitoring of the sample. CONCLUSIONS: The adoption of systematic stages of development and quality control was fundamental to ensure the production and reliability of information from brain neuroimaging studies.

The National Ecological Observatory Network (NEON) is the first-ever continental-scale ecological observatory and is designed to collect and provide open data to the scientific community for the next 30 years. NEON is at a very exciting moment in its history – after almost two decades of design and planning by committed and visionary scientists and engineers, the construction of 47 terrestrial and 34 aquatic field sites is nearly complete. At this moment, nearly 300 field scientists are out in deserts, grasslands, forests, and streams taking measurements that will turn into over 180, freely available, NEON data products. The collection and delivery of these data are designed to enable transformative research on US ecosystems at previously unimaginable spatial and temporal scales. Most ecologists, including myself, entered this field because of a fascination with the causes and complexities of ecological interactions. I began my career studying small mammals, plant reproductive strategies, and bird foraging behavior. I was thrilled to be in the field, recording the patterns of nature near my undergraduate institution in the rolling tallgrass prairies of eastern Kansas. My observations made their way from pencil to paper to high stacks of punch cards sent through large, noisy computers. Fast forward 30 years, and we now have access to large datasets and complex statistical models to synthesize ecological observations in the hope of achieving broad-scale understanding and ecological forecasts. NEON is an NSF-funded large research infrastructure project designed to provide a rich set of resources to scientists. As such, it is vital that members of the scientific community stay engaged throughout the project's lifetime. In the years since NEON was originally imagined, the perception of how closely ecologists have been involved has fluctuated, with some voicing the opinion that NEON was moving forward without sufficient scholarly input. My top priority, as NEON's new Director and Chief Scientist, is to improve and strengthen NEON engagement with the scientific community. To begin, we solicited feedback to inform a strategic plan to guide our engagement activities, not just at headquarters but across all our field sites. Recent feedback demonstrated strong interest in using NEON's data and infrastructure but also some important concerns that need to be addressed. For such a large public investment, I believe that it is essential to show the unique value of the NEON project. For example, rather than being redundant or competitive with other funded programs, NEON provides complementary and comprehensive data from the soils to the atmosphere from an extensive suite of field sites. The data products are publicly available and can be used in conjunction with additional observations or with ongoing or newly proposed experiments at those sites. These resources provide tremendous added value to our research capabilities for addressing novel and societally relevant questions. Feedback from the community has also revealed that many potential users have not searched through our currently available data products: indeed, 38% of respondents to a recent survey said that they did not know if NEON resources are easy to access or whether they are of high quality. In my short time at NEON, I have been impressed with the rigor of the quality assurance and quality control processes in place, starting from when data collection is planned, through implementation of detailed collection protocols, to processing and delivery. All along the path from field to portal, the data are validated and checked to ensure high quality for the user community. In response to feedback, we are actively developing detailed information to make those procedures more transparent to all users. I am excited to be in my new position because I am passionate about ecology, and I believe that NEON is a vast resource for the scientific community. The observatory will facilitate research and education by our community (and beyond) in ways that we have not yet imagined and will propel our knowledge forward. I envision NEON as a hub of innovative research synthesis on ecological responses to environmental change, and as a nucleus of ecological science for the next generation of ecologists. To repeat an often-cited phrase, whatever you thought about NEON, think again. This is a tremendous opportunity for the ecological community, and I invite you to join us at this exciting time. Visit the NEON website (www.neonscience.org) to learn more about ways you can engage and provide your feedback. We want to hear from you and support your exciting work…this is your observatory! SHARON K COLLINGE Chief Scientist/Observatory Director, National Ecological Observatory Network, Boulder, CO

Persons with disabilities face neglect and exclusion due to barriers such as stigma, inaccessible environments, limited access to education and employment, and weak legal protections. Women with disabilities experience compounded challenges from gender-based discrimination, particularly in healthcare, education, and employment. This study explores the socio-economic neglect and intersectional challenges faced by individuals with disabilities, focusing on women. Drawing from scholarly sources, it reviews disability inclusion policies, accessibility standards, and socio-cultural factors perpetuating marginalization. The research examines societal attitudes shaping the experiences of individuals with disabilities and highlights actionable insights for their empowerment and integration. Emphasizing the need for inclusive policies, improved accessibility, and increased societal awareness, the study advocates for stronger advocacy and targeted resources. By fostering inclusivity, individuals with disabilities, especially women, can thrive as equal and valued members of society.

Web accessibility is a critical aspect of inclusive digital experiences, ensuring equitable access for users with diverse abilities. Despite significant advancements, existing automated tools often fail to detect complex and context-specific accessibility issues. This research proposes an AI-powered semantic framework for comprehensive web accessibility evaluation, leveraging artificial intelligence to analyze and interpret web content with a focus on inclusivity and compliance with accessibility standards. The framework integrates semantic analysis to identify nuanced accessibility barriers, such as inadequate alt text, improper heading structures, and color contrast violations, which are often missed by traditional tools. By utilizing machine learning and natural language processing, the proposed solution aims to bridge gaps in guideline interpretation and enhance the detection of user experience challenges. Additionally, this research incorporates user feedback mechanisms and expert insights to continuously refine the framework, making it adaptable to evolving accessibility needs. Extensive testing across diverse web environments demonstrates the framework's ability to improve detection accuracy, reduce evaluation times, and provide actionable insights to developers. The proposed framework not only advances automated accessibility evaluation but also contributes to fostering a more inclusive internet by empowering developers and organizations to proactively address accessibility barriers.

In today’s rapidly evolving digital landscape, the explosion of data volume and complexity has necessitated the development of advanced processing frameworks that can scale effectively. Apache Spark emerges as a leading technology in big data processing, offering powerful in-memory computing capabilities and streamlined analytical operations. This paper explores the utilization of Spark for scaling data pipelines and performing comprehensive analytics across diverse datasets. By distributing computational tasks across clusters, Spark significantly reduces processing time and enhances real-time data analysis, making it indispensable for modern enterprises. The study details how Spark integrates with existing data architectures, supporting a broad spectrum of applications ranging from batch processing to interactive analytics. In addition, Spark’s robust ecosystem—featuring libraries for machine learning, graph processing, and stream analysis—enables organizations to address complex data challenges efficiently. Key case studies illustrate successful deployments, emphasizing improvements in performance, fault tolerance, and cost-effectiveness. The research further examines techniques for optimizing Spark configurations to achieve maximum throughput and minimal latency, highlighting best practices for resource management in both on-premises and cloud environments. Ultimately, this paper demonstrates that by leveraging Apache Spark, organizations can transform raw data into actionable insights, drive innovation, and maintain a competitive edge in a data-centric world. Furthermore, the framework’s scalability allows for seamless integration of emerging technologies, ensuring adaptability to evolving business requirements. Future research directions include enhancing security measures, reducing energy consumption, and further refining analytical accuracy. These advancements will continue to empower data scientists and engineers in solving intricate data challenges while setting industry benchmarks.

<p>This paper defines principles intended to serve as a basis for teaching engineering students how to endow the bridges they design with good visual qualities. These principles were developed as a means of addressing the current situation in bridge design, in which engineers generally receive no formal education in the visual aspects of bridge design and the vast majority of bridges create visual impacts that are at best mediocre. Educating future engineers to design bridges of high visual quality can be best accomplished by shifting the focus away from signature bridges intended to make grandiose visual statements and concentrating on more practical works, abandoning the</p><p>notion of bridges as works of art in favour of a more realistic emphasis on the craft of bridge design, and by insisting that real improvements in technology are incorporated in every bridge, enabling each bridge to perform its function better than its predecessors.</p>

Urban spaces possess diverse visual qualities that significantly impact comfort, aesthetics, and navigation. This paper introduces a novel approach towards classifying urban spaces based on their visual characteristics through isovist analysis. An isovist is the polygon representing the visible areas from a given vantage point. The geometrical attributes of the isovist polygon enables a quantitative measure of visual qualities in the urban setting. However, the potential for classifying urban spaces based on the geometrical attributes of isovist polygons remains largely untapped. This paper presents a methodology to systematically categorise urban spaces using isovists and their geometrical attributes. By aggregating ten dimensions of geometrical attributes through a Gaussian Mixture Model (GMM) clustering analysis, this workflow produces a classifier that categorises urban spaces into 10 distinct spatial types, each possessing unique visual and spatial characteristics. This method successfully captures intrinsic spatial typologies across diverse urban contexts and can reflect the values embedded in urban design schemes. By facilitating meaningful and discussions in urban planning and design, this research contributes to a deeper and numerical understanding of the spatial and visual aspects of urban design. Further research avenues include the extension of this methodology to 3D analysis and refining tessellation algorithms for improved computational efficiency and accuracy.

Data infrastructure and quality are vital organs influencing the health of any organizations and are essential for creating and delivering business insights. Due to this fact, stakeholders expect a flawless experience, real-time solutions and support. However, these expectations are normally too high for IT departments that are always immersed with various users' requests. The Big Data era and its analytics have put organizations in positions to predict and forecast users' needs to produce a great user experience. This is achieved through intuitive design, error-free coding and quality performance. There is increasing demand for better business analytics so as to enable organizations establish solid foundation by building out a data management ecosystem that delivers flexibility and performance required by cognitive solutions. The objective of this paper was to highlight how universities can leverage better analytics to process amount of continuously generated data from various sources and generate actionable insights needed to achieve academic business goals. This study followed an interpretive paradigm taking a case study of a South African university and employed semi-structured interviews. This study found that data value and the use thereof in the management of academic divisions of the university should be consistent with faculty business plans. In this way, business analytics is not haphazardly used but is strategically aligned with faculty plans and overall institutional strategic objectives and targets.