You are here: Building an online interactive map application

As new map applications have increased in popularity the opportunities for gathering geographic data have increased as well. The difficulty that interactive user-driven map applications have is the motivation for user participation. People have become more comfortable contributing to forums, blogs, and sites driven by user content, but user-driven map sites have been slow to cultivate a large amount of user-contributed data. Focusing on a small geographic area can increase user participation within interactive map applications. The design and implementation of an online map applications focused on a small geographic area is presented. The site uses a map interface to gather new spatial data from users, as well as allowing browsing and search. Users can also annotate existing data on the site through the map interface. The final site presents a mix between theory-based design and the inherent limitations of a practical implementation

Experiences with a dialog-driven process model for Web application development

We present a dialog-driven process model for the development of web-based applications that uses a graphical notation to model and iteratively refine the application’s dialog flow, and communicate with non-technical stakeholders in the development process. This way, the user interface can drive the design and implementation of the application logic and data model instead of being dictated by it. After an introduction of the underlying notation and dialog control framework, we present how these tools can support the phases of the development process and discuss experiences gained from the implementation of a web application that was built using this approach

Electrochemical materials discovery and intelligence

Design and implementation of efficient and cost-effective electrochemical devices is a complex challenge. It hinges on big-data driven knowledge at the frontiers of multi-disciplinary efforts in materials discovery and design. These massive data–driven processes, however, require intensive cognitive, yet expensive systems, including human, to determine the best design decisions. A novel approach towards Artificial Intelligence (AI) and Machine Learning (ML) algorithms can overcome the complexity of selecting advanced new materials with the predictable and desired properties. Focusing on advanced electrocatalysts for CO2 conversion as a use case, we demonstrate an AI-driven “Virtual Materials Intelligence” platform (beta) for materials data management and intelligent design equipped with an advanced user interface and predictive capabilities in view of materials properties and function. The platform combines information originating from large data sets of different origins. The data storage, data analysis, and advanced analysis algorithms enable efficient and secure data flow between several different simulation and characterization activities. The cloud-based platform ultimately aims to manage all available materials databases and relevant modeling, simulation, performance, cost, and characterization data and how they can be communicated to materials fabrication and design teams

Leveraging Existing Technology: Developing a Trusted Digital Repository for the U.S. Geological Survey

As Federal Government agencies in the United States pivot to increase access to scientific data (Sheehan, 2016), the U.S. Geological Survey (USGS) has made substantial progress (Kriesberg et al., 2017). USGS authors are required to make federally funded data publicly available in an approved data repository (USGS, 2016b). This type of public data product, known as a USGS data release, serves as a method for publishing reviewed and approved data. In this paper, we present major milestones in the approach the USGS took to transition an existing technology platform to a Trusted Digital Repository. We describe both the technical and the non-technical actions that contributed to a successful outcome.We highlight how initial workflows revealed patterns that were later automated, and the ways in which assessments and user feedback influenced design and implementation. The paper concludes with lessons learned, such as the importance of a community of practice, application programming interface (API)-driven technologies, iterative development, and user-centered design. This paper is intended to offer a potential roadmap for organizations pursuing similar goals. &nbsp

Designing websites with eXtensible web (xWeb) methodology

Today, eXtensible Markup Language (XML) is fast emerging as the dominant standard for storing, describing, representing and interchanging data among various enterprises systems and databases in the context of complex web enterprises information systems (EIS). Conversely, for web EIS (such as e-commerce and portals) to be successful, it is important to apply a high level, model driven solutions and meta-data vocabularies to design and implementation techniques that are capable of handling heterogonous schemas and documents. For this, we need a methodology that provides a higher level of abstraction of the domain in question with rigorously defined standards that are to be more widely understood by all stakeholders of the system. To-date, UML has proven itself as the language of choice for modeling EIS using OO techniques. With the introduction of XML Schema, which provides rich facilities for constraining and defining enterprise XML content, the combination of UML and XML technologies provide a good platform (and the flexibility) for modeling, designing and representing complex enterprise contents for building successful EIS. In this paper, we show how a layered view model coupled with a proven user interface analysis framework (WUiAM) is utilized in providing architectural construct and abstract website model (called eXtensible Web, xWeb), to model, design and implement simple, user-centred, collaborative websites at varying levels of abstraction. The uniqueness xWeb is that the model data (web user interface definitions, website data descriptions and constraints) and the web content are captured and represented at the conceptual level using views (one model) and can be deployed (multiple platform specific models) using one or more implementation models

HiReD: a high-resolution multi-window visualisation environment for cluster-driven displays

High-resolution, wall-size displays often rely on bespoke software for performing interactive data visualisation, leading to interface designs with little or no consistency between displays. This makes adoption for novice users difficult when migrating from desktop environments. However, desktop interface techniques (such as task- and menu- bars) do not scale well and so cannot be relied on to drive the design of large display interfaces. In this paper we present HiReD, a multi-window environment for cluster-driven displays. As well as describing the technical details of the system, we also describe a suite of low-precision interface techniques that aim to provide a familiar desktop environment to the user while overcoming the scalability issues of high-resolution displays. We hope that these techniques, as well as the implementation of HiReD itself, can encourage good practice in the design and development of future interfaces for high-resolution, wall-size displays

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Published: 13 January 2022Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.We would like to thank Rutger van Deelen for providing the initial (PyQt) setup and implementation of the bidseditor application and Yorguin José Mantilla Ramos for the useful architectural feedback and for the initial code of the sova2coin EEG/MEG plugin. We are also grateful for all the feedback, questions, and contributions that users have submitted on GitHub

Concurrency Controls in Event-Driven Programs

Functional reactive programming (FRP) is a programming paradigm that utilizes the concepts of functional programming and time-varying data types to create event-driven applications. In this paradigm, data types in which values can change over time are primitives and can be applied to functions. These values are composable and can be combined with functions to create values that react to changes in values from multiple sources. Events can be modeled as values that change in discrete time steps. Computation can be encoded as values that produce events, with combination operators, it enables us to write concurrent event-driven programs by combining the concurrent computation as events. Combined with the denotational approach of functional programming, we can write programs in a concise manner. The style of event-driven programming has been widely adopted for developing graphical user interface applications, since they need to process events concurrently to stay responsive. This makes FRP a fitting approach for managing complex state and handling of events concurrently. In recent years, real-time systems such as IoT (internet of things) applications have become an important field of computation. Applying FRP to real-time systems is still an active area of research.For IoT applications, they are commonly tasked to perform data capturing in real time and transmit them to other devices. They need to exchange data with other applications over the internet and respond in a timely manner. The data needs to be processed, for simple analysis or more computation intensive work such as machine learning. Designing applications that perform these tasks and remain efficient and responsive can be challenging. In this thesis, we demonstrate that FRP is a suitable approach for real-time applications. These applications require soft real-time requirements, where systems can tolerate tasks that fail to meet the deadline and the results of these tasks might still be useful.First, we design the concurrency abstractions needed for supporting asynchronous computation and use it as the basis for building the FRP abstraction. Our implementation is in Haskell, a functional programming language with a rich type system that allows us to model abstractions with ease. The concurrency abstraction is based on some of the ideas from the Haskell solution for asynchronous computation, which elegantly supports cancelation in a composable way. Based on the Haskell implementation, we extend our design with operators that are more suitable for building web applications. We translate our implementation to JavaScript as it is more commonly used for web application development, and implementing the RxJS interface. RxJS is a popular JavaScript library for reactive programming in web applications. By implementing the RxJS interface, we argue that our programming model implemented in Haskell is also applicable in mainstream languages such as JavaScript

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications