Carrying capacity dashboard analyses : Australian case studies of populations scaled to place

In a globalised world, it makes sense to examine our demands on the landscape through the wide-angle lens of ecological footprint analysis. However, the important impetus towards a more localised societal system suggests a review of this approach and a return to its origins in carrying capacity assessment. The determination of whether we live within or beyond our carrying capacity is entirely scalar, with national, regional and local assessments dependent not only on the choices of the population but the capability of a landscape - at scale. The Carrying Capacity Dashboard, an openly accessible online modelling interface, has been developed for Australian conditions, facilitating analysis at various scales. Like ecological footprint analysis it allows users to test a variety of societal behaviours such as diet, consumption patterns, farming systems and ecological protection practices; but unlike the footprint approach, the results are uniquely tailored to place. This paper examines population estimates generated by the Carrying Capacity Dashboard. It compares results in various scales of analysis, from national to local. It examines the key behavioural choices influencing Australian carrying capacity estimates. For instance, the assumption that the consumption of red meat automatically lowers carrying capacity is examined and in some cases, debunked. Lastly, it examines the implications of implementing carrying capacity assessment globally, but not through a wide angle lens; rather, by examining the landscape one locality at a time.

A customisable dashboard display for environmental performance visualisations

We conducted an exploratory study of a mobile energy monitoring tool: The Dashboard. Our point of departure from prior work was the emphasis of end-user customisation and social sharing. Applying extensive feedback, we deployed the Dashboard in real-world conditions to socially linked research participants for a period of five weeks. Participants were encouraged to devise, construct, place, and view various data feeds. The aim of our study was to test the assumption that participants, having control over their Dashboard configuration, would engage, and remain engaged, with their energy feedback throughout the trial. Our research points to a set of design issues surrounding the adoption and continued use of such tools. A novel finding of our study is the impact of social links between participants and their continued engagement with the Dashboard. Our results also illustrate the emergence of energy-voyeurism, a form of social energy monitoring by peers.

An intuitive dashboard for Bayesian network inference

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

Aid for Trade indicators Dashboard: user manual

This Manual is intended to guide individuals and institutions seeking to use the Aid for Trade Dashboard developed by the Economic Commission for Latin America and the Caribbean as part of the Development Project “Facilitating the Effective Integration of Developing Countries into the Global Economy through Aid for Trade Schemes”. This Dashboard presents a set of online indicators for all the countries comprising the five United Nations’ Regional Commissions which are currently participating in this Project.

Ayuda para el comercio: dashboard de indicadores clave (ApCDIC). Manual de usuario

Incluye bibliografía.

Design and implementation of a clinical dashboard

In a large health care system, the importance of accurate information as feedback mechanisms about its performance is necessary on many levels from the senior level management to service level managers for valid decision-making purposes. The implementation of dashboards is one way to remedy the problem of data overload by providing up-to-date, accurate, and concise information. As this health care system seeks to have an organized, systematic review mechanism in place, dashboards are being created in a variety of the hospital service departments to monitor performance indicators. The Infection Control Administration of this health care system is one that does not currently utilize a dashboard but seeks to implement one. ^ The purpose of this project is to research and design a clinical dashboard for the Infection Control Administration. The intent is that the implementation and usefulness of the clinical dashboard translates into improvement in the measurement of health care quality.^

Dashboard configurable para la gestión y administración de una instancia de OpenStack

En este documento está descrito detalladamente el trabajo realizado para completar todos objetivos marcados para este Trabajo de Fin de Grado, que tiene como meta final el desarrollo de un dashboard configurable de gestión y administración para instancias de OpenStack. OpenStack es una plataforma libre y de código abierto utilizada como solución de Infraestructura como Servicio (Infrastructure as a Service, IaaS) en clouds tanto públicos, que ofrecen sus servicios cobrando el tiempo de uso o los recursos utilizados, como privados para su utilización exclusiva en el entorno de una empresa. El proyecto OpenStack se inició como una colaboración entre la NASA y RackSpace, y a día de hoy es mantenido por las empresas más potentes del sector tecnológico a través de la Fundación OpenStack. La plataforma OpenStack permite el acceso a sus servicios a través de una Interfaz de Linea de Comandos (Command Line Interface, CLI), una API RESTful y una interfaz web en forma de dashboard. Esta última es ofrecida a través del servicio Horizon. Este servicio provee de una interfaz gráfica para acceder, gestionar y automatizar servicios basados en cloud. El dashboard de Horizon presente algunos problemas como que: solo admite opciones de configuración mediante código Python, lo que hace que el usuario no tenga ninguna capacidad de configuración y que el administrador esté obligado a interactuar directamente con el código. no tiene soporte para múltiples regiones que permitan que un usuario pueda distribuir sus recursos por distintos centros de datos en diversas localizaciones como más le convenga. El presente Trabajo de Fin de Grado, que es la fase inicial del proyecto FI-Dash, pretende solucionar estos problemas mediante el desarrollo de un catálogo de widget de la plataformaWireCloud que permitirán al usuario tener todas las funcionalidades ofrecidas por Horizon a la vez que le ofrecen capacidades de configuración y añaden funcionalidades no presentes en Horizon como el soporte de múltiples regiones. Como paso previo al desarrollo del catálogo de widgets se ha llevado a cabo un estudio de las tecnologías y servicios ofrecidos por OpenStack, así como de las herramientas que pudieran ser necesarias para la realización del trabajo. El proceso de desarrollo ha sido dividido en distintas fases de acuerdo con los distintos componentes que forman parte del dashboard cada uno con una funcion de gestion sobre un tipo de recurso distinto. Las otras fases del desarrollo han sido la integración completa del dashboard en la plataforma WireCloud y el diseño de una interfaz gráfica usable y atractiva.---ABSTRACT---Throughout this document it is described the work performed in order to achieve all of the objectives set for this Final Project, which has as its main goal the development of a configurable dashboard for managing and administrating OpenStack instances. OpenStack is a free and open source platform used as Infrastructure as a Service (IaaS) for both public clouds, which offer their services through payments on time or resources used, and private clouds for use only in the company’s environment. The OpenStack project started as a collaboration between NASA and Rackspace, and nowadays is maintained by the most powerful companies in the technology sector through the OpenStack Foundation. The OpenStack project provides access to its services through a Command Line Interface (CLI), a RESTful API and a web interface as dashboard. The latter is offered through a service called Horizon. This service provides a graphical interface to access, manage and automate cloud-based services. Horizon’s dashboard presents some problems such as: Only supports configuration options using Python code, which grants the user no configuration capabilities and forces the administrator to interact directly. No support for multiple regions that allow a user to allocate his resources by different data centers in different locations at his convenience. This Final Project, which is the initial stage of the FI-Dash project, aims to solve these problems by developing a catalog of widgets for the WireCloud platform that will allow the user to have all the features offered by Horizon while offering configuration capabilities and additional features not present in Horizon such as support for multiple regions. As a prelude to the development of the widget catalog, a study of technologies and services offered by OpenStack as well as tools that may be necessary to carry out the work has been conducted. The development process has been split in phases matching the different components that are part of the dashboard, having each one of them a function of management of one kind of resource. The other development phases have been the achieving of full integration with WireCloud and the design of a graphical interface that is both usable and atractive.

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

Development of a Dashboard for Sentiment Analysis of Football in Twitter based on Web Components and D3.js

This thesis is the result of a project whose objective has been to develop and deploy a dashboard for sentiment analysis of football in Twitter based on web components and D3.js. To do so, a visualisation server has been developed in order to present the data obtained from Twitter and analysed with Senpy. This visualisation server has been developed with Polymer web components and D3.js. Data mining has been done with a pipeline between Twitter, Senpy and ElasticSearch. Luigi have been used in this process because helps building complex pipelines of batch jobs, so it has analysed all tweets and stored them in ElasticSearch. To continue, D3.js has been used to create interactive widgets that make data easily accessible, this widgets will allow the user to interact with them and �filter the most interesting data for him. Polymer web components have been used to make this dashboard according to Google's material design and be able to show dynamic data in widgets. As a result, this project will allow an extensive analysis of the social network, pointing out the influence of players and teams and the emotions and sentiments that emerge in a lapse of time.

Beginning to Explore Dashboard Dining

It can be nutritious and healthy if done right. Fruits and vegetables, a granola bar, smoothie, or some fresh squeezed Florida orange juice would be good choices. On the other hand, it can poison you. Perishable protein and dairy products must be packed in a well- insulated cooler with plenty of ice and a refrigerator thermometer kept inside to en-sure the food stays below 40 degrees Fahrenheit. If you are not completely safe, it can kill you. According to Hagerty Insurance of Traverse City, Michigan, the top ten worst foods to consume are coffee, hot soups, tacos, chili, juicy hamburgers, fried chicken, any barbecued food, filled doughnuts, soft drinks, and chocolate. (see Lisa Chin, 2003) It simply takes a sudden scalding spill, an unexpected splash, or dripping condiments, any of which demand your immediate attention, to become an instant fatality.

D2.5 - First bundle of dashboard components

This document describes the core components to create customizable game analytics and dashboards: their present status; links to their full designs and downloadable versions; and how to configure them, and take advantage of the analytics visualizations and the underlying architecture of the platform. All the dashboard components are working with data collected using the xAPI data format that the RAGE project has developed in collaboration with ADL Co-Lab.

The MOOC Dashboard: Visualising MOOC data for everyone

Abstract Massive Open Online Courses (MOOCs) generate enormous amounts of data. The University of Southampton has run and is running dozens of MOOC instances. The vast amount of data resulting from our MOOCs can provide highly valuable information to all parties involved in the creation and delivery of these courses. However, analysing and visualising such data is a task that not all educators have the time or skills to undertake. The recently developed MOOC Dashboard is a tool aimed at bridging such a gap: it provides reports and visualisations based on the data generated by learners in MOOCs. Speakers Manuel Leon is currently a Lecturer in Online Teaching and Learning in the Institute for Learning Innovation and Development (ILIaD). Adriana Wilde is a Teaching Fellow in Electronics and Computer Science, with research interests in MOOCs and Learning Analytics. Darron Tang (4th Year BEng Computer Science) and Jasmine Cheng (BSc Mathematics & Actuarial Science and starting MSc Data Science shortly) have been working as interns over this Summer (2016) as have been developing the MOOC Dashboard.