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| data science and ux: Designing with Data Rochelle King, Elizabeth F Churchill, Caitlin Tan, 2017-03-29 On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move |
| data science and ux: Practical Web Analytics for User Experience Michael Beasley, 2013-06-21 Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively. - Discover concrete information on how web analytics data support user research and user-centered design - Learn how to frame questions in a way that lets you navigate through massive amounts of data to get the answer you need - Learn how to gather information for personas, verify behavior found in usability testing, support heuristic evaluation with data, analyze keyword data, and understand how to communicate these findings with business stakeholders |
| data science and ux: The User Experience Team of One Leah Buley, 2013-07-09 The User Experience Team of One prescribes a range of approaches that have big impact and take less time and fewer resources than the standard lineup of UX deliverables. Whether you want to cross over into user experience or you're a seasoned practitioner trying to drag your organization forward, this book gives you tools and insight for doing more with less. |
| data science and ux: Researching UX: Analytics Luke Hay, 2017-01-10 Good UX is based on evidence. Qualitative evidence, such as user testing and field research, can only get you so far. To get the full picture of how users are engaging with your website or app, you'll need to use quantitative evidence in the form of analytics. This book will show you, step by step, how you can use website and app analytics data to inform design choices and definitively improve user experience. Offering practical guidelines, with plenty of detailed examples, this book covers: why you need to gather analytics data for your UX projects getting set up with analytics tools analyzing data how to find problems in your analytics using analytics to aid user research, measure and report on outcomes By the end of this book, you'll have a strong understanding of the important role analytics plays in the UX process. It will inspire you to take an analytics first approach to your UX projects. |
| data science and ux: Quantifying the User Experience Jeff Sauro, James R Lewis, 2016-07-12 Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website? This book provides a foundation for statistical theories and the best practices needed to apply them. The authors draw on decades of statistical literature from human factors, industrial engineering, and psychology, as well as their own published research, providing both concrete solutions (Excel formulas and links to their own web-calculators), along with an engaging discussion on the statistical reasons why tests work and how to effectively communicate results. Throughout this new edition, users will find updates on standardized usability questionnaires, a new chapter on general linear modeling (correlation, regression, and analysis of variance), with updated examples and case studies throughout. - Completely updated to provide practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices - Includes new and revised information on standardized usability questionnaires - Includes a completely new chapter introducing correlation, regression, and analysis of variance - Shows practitioners which test to use, why they work, and best practices for application, along with easy-to-use Excel formulas and web-calculators for analyzing data - Recommends ways for researchers and practitioners to communicate results to stakeholders in plain English |
| data science and ux: Unleashing the Power of UX Analytics Jeff Hendrickson, 2023-08-18 Optimize UX analytics for your design workflow and discover effective techniques and strategies to craft unforgettable, impactful user experiences Purchase of the print or Kindle book includes a free PDF eBook Key Features Enhance your understanding of qualitative and quantitative analysis for successful UX projects Apply design thinking and use surveys, interviews, and UX analytics tools for better product design Overcome bottlenecks and challenges at each stage of the user experience Book DescriptionUX analytics is a field that recognizes the significance of understanding human behavior and emotions in designing user experiences. It goes beyond mere metrics and embraces a people-centric approach. With the help of this comprehensive guide, you’ll acquire essential skills, knowledge, and techniques to establish a top-notch UX analytics practice. Unleashing the Power of UX Analytics will equip you with the strategies and tactics necessary to effectively collect, analyze, and interpret data, empowering you to make informed decisions that enhance the overall user experience. It emphasizes the importance of empathy in comprehending user needs and desires, enabling you to create meaningful and impactful design solutions. As you advance, this book walks you through the entire UX analytics process, from setting goals and defining key performance indicators (KPIs) to implementing various research methods and tools. You'll gain insights into user interview best practices, usability testing, and techniques for gathering qualitative and quantitative data. Armed with the knowledge of data analysis and interpretation, you'll be able to uncover patterns, trends, and user preferences to make data-driven decisions.What you will learn Understand the significance of analytics in successful UX projects Apply design thinking as a problem-solving tool in a UX practice Explore taxonomies, dashboards, KPIs, and data visualizations to understand data enterprise in depth Discover key considerations to determine which UX analytics tools are best for your projects Craft a North Star statement and understand how it guides your work Design and deliver the best research findings collateral Get to grips with heuristics and performing the effective evaluations Who this book is forThis book is for product managers, UX researchers, designers, and anyone involved in UX and business development, both in management roles and as individual contributors. If you are looking to master the methodologies, principles, and best practices for driving product design decisions through UX analytics, this book is absolutely the right pick for you. While a basic understanding of user experience principles is beneficial, it is not a prerequisite, as everything you need to know will be explained. |
| data science and ux: Game Usability Katherine Isbister, Celia Hodent, 2022-03-13 This book introduces the basics in game usability and overall game UX mindset and techniques, as well as looking at current industry best practices and trends. Fully updated for its second edition, it includes practical advice on how to include usability in already tight development timelines, and how to advocate for UX and communicate results to higher-ups effectively. The book begins with an introduction to UX strategy considerations for games, and to UX design, before moving on to cover core user research and usability techniques as well as how to fit UX practices into the business process. It provides considerations of player differences and offers strategies for inclusion as well as chapters that give platform and context specific advice. With a wealth of new interviews with industry leaders and contributions from the very best in game UX, the book also includes brand new chapters on: Accessibility Mobile Game Usability Data Science Virtual and Augmented Reality Esports This book will be vital reading for all professional game developers and game UX advocates, as well as those students aspiring to work in game development and game UX. |
| data science and ux: The UX Book Rex Hartson, Pardha S. Pyla, 2018-11-02 The discipline of user experience (UX) design has matured into a confident practice and this edition reflects, and in some areas accelerates, that evolution. Technically this is the second edition of The UX Book, but so much of it is new, it is more like a sequel. One of the major positive trends in UX is the continued emphasis on design—a kind of design that highlights the designer's creative skills and insights and embodies a synthesis of technology with usability, usefulness, aesthetics, and meaningfulness to the user. In this edition a new conceptual top-down design framework is introduced to help readers with this evolution. This entire edition is oriented toward an agile UX lifecycle process, explained in the funnel model of agile UX, as a better match to the now de facto standard agile approach to software engineering. To reflect these trends, even the subtitle of the book is changed to Agile UX design for a quality user experience. Designed as a how-to-do-it handbook and field guide for UX professionals and a textbook for aspiring students, the book is accompanied by in-class exercises and team projects. The approach is practical rather than formal or theoretical. The primary goal is still to imbue an understanding of what a good user experience is and how to achieve it. To better serve this, processes, methods, and techniques are introduced early to establish process-related concepts as context for discussion in later chapters. - Winner of a 2020 Textbook Excellence Award (College) (Texty) from the Textbook and Academic Authors Association - A comprehensive textbook for UX/HCI/Interaction Design students readymade for the classroom, complete with instructors' manual, dedicated web site, sample syllabus, examples, exercises, and lecture slides - Features HCI theory, process, practice, and a host of real world stories and contributions from industry luminaries to prepare students for working in the field - The only HCI textbook to cover agile methodology, design approaches, and a full, modern suite of classroom material (stemming from tried and tested classroom use by the authors) |
| data science and ux: Search Analytics for Your Site Louis Rosenfeld, 2011-07-06 Any organization that has a searchable web site or intranet is sitting on top of hugely valuable and usually under-exploited data: logs that capture what users are searching for, how often each query was searched, and how many results each query retrieved. Search queries are gold: they are real data that show us exactly what users are searching for in their own words. This book shows you how to use search analytics to carry on a conversation with your customers: listen to and understand their needs, and improve your content, navigation and search performance to meet those needs. |
| data science and ux: The Intelligent Company Bernard Marr, 2010-03-10 Today's most successful companies are Intelligent Companies that use the best available data to inform their decision making. This is called Evidence-Based Management and is one of the fastest growing business trends of our times. Intelligent Companies bring together tools such as Business Intelligence, Analytics, Key Performance Indicators, Balanced Scorecards, Management Reporting and Strategic Decision Making to generate real competitive advantages. As information and data volumes grow at explosive rates, the challenges of managing this information is turning into a losing battle for most companies and they end up drowning in data while thirsting for insights. This is made worse by the severe skills shortage in analytics, data presentation and communication. This latest book by best-selling management expert Bernard Marr will equip you with a set of powerful skills that are vital for successful managers now and in the future. Increase your market value by gaining essential skills that are in high demand but in short supply. Loaded with practical step-by-step guidance, simple tools and real life examples of how leading organizations such as Google, CocaCola, Capital One, Saatchi & Saatchi, Tesco, Yahoo, as well as Government Departments and Agencies have put the principles into practice. The five steps to more intelligent decision making are: Step 1: More intelligent strategies by identifying strategic priorities and agreeing your real information needs Step 2: More intelligent data by creating relevant and meaningful performance indicators and qualitative management information linked back to your strategic information needs Step 3: More intelligent insights by using good evidence to test and prove ideas and by analysing the data to gain robust and reliable insights Step 4: More intelligent communication by creating informative and engaging management information packs and dashboards that provide the essential information, packaged in an easy-to-read way Step 5: More intelligent decision making by fostering an evidence-based culture of turning information into actionable knowledge and real decisions Bernard Marr did it again! This outstanding and practical book will help your company become more intelligent and more successful. Marr takes the fields of business-intelligence, analytics and scorecarding to bring them together into a powerful and easy-to-follow 5-step framework. The Intelligent Company is THE must-read book of our times. Bruno Aziza, Co-author of best-selling book Drive Business Performance and Worldwide Strategy Lead, Microsoft Business Intelligence Book after book Bernard Marr is redefining the fundamentals of good business management. The Intelligent Company is a must read in these changing times and a reference you will want on your desk every day! Gabriel Bellenger, Accenture Strategy |
| data science and ux: Improving the User Experience through Practical Data Analytics Mike Fritz, Paul D. Berger, 2015-03-03 Improving the User Experience through Practical Data Analytics shows you how to make UX design decisions based on data—not hunches. Authors Fritz and Berger help the UX professional recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you'll delight your users, increase your bottom line and gain a powerful competitive advantage for your company—and yourself. Key features include: - Practical advise on choosing the right data analysis technique for each project. - A step-by-step methodology for applying each technique, including examples and scenarios drawn from the UX field. - Detailed screen shots and instructions for performing the techniques using Excel (both for PC and Mac) and SPSS. - Clear and concise guidance on interpreting the data output. - Exercises to practice the techniques - Practical guidance on choosing the right data analysis technique for each project. - Real-world examples to build a theoretical and practical understanding of key concepts from consumer and financial verticals. - A step-by-step methodology for applying each predictive technique, including detailed examples. - A detailed guide to interpreting the data output and examples of how to effectively present the findings in a report. - Exercises to learn the techniques |
| data science and ux: The UX Book Rex Hartson, Pardha S. Pyla, 2012-01-25 The UX Book: Process and Guidelines for Ensuring a Quality User Experience aims to help readers learn how to create and refine interaction designs that ensure a quality user experience (UX). The book seeks to expand the concept of traditional usability to a broader notion of user experience; to provide a hands-on, practical guide to best practices and established principles in a UX lifecycle; and to describe a pragmatic process for managing the overall development effort. The book provides an iterative and evaluation-centered UX lifecycle template, called the Wheel, for interaction design. Key concepts discussed include contextual inquiry and analysis; extracting interaction design requirements; constructing design-informing models; design production; UX goals, metrics, and targets; prototyping; UX evaluation; the interaction cycle and the user action framework; and UX design guidelines. This book will be useful to anyone interested in learning more about creating interaction designs to ensure a quality user experience. These include interaction designers, graphic designers, usability analysts, software engineers, programmers, systems analysts, software quality-assurance specialists, human factors engineers, cognitive psychologists, cosmic psychics, trainers, technical writers, documentation specialists, marketing personnel, and project managers. - A very broad approach to user experience through its components—usability, usefulness, and emotional impact with special attention to lightweight methods such as rapid UX evaluation techniques and an agile UX development process - Universal applicability of processes, principles, and guidelines—not just for GUIs and the Web, but for all kinds of interaction and devices: embodied interaction, mobile devices, ATMs, refrigerators, and elevator controls, and even highway signage - Extensive design guidelines applied in the context of the various kinds of affordances necessary to support all aspects of interaction - Real-world stories and contributions from accomplished UX practitioners - A practical guide to best practices and established principles in UX - A lifecycle template that can be instantiated and tailored to a given project, for a given type of system development, on a given budget |
| data science and ux: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| data science and ux: UX Optimization W. Craig Tomlin, 2018-09-26 Combine two typically separate sources of data—behavioral quantitative data and usability testing qualitative data—into a powerful single tool that helps improve your organization’s website by increasing conversion and ROI. The combination of the what is happening data of website activity, coupled with the why it's happening data of usability testing, provides a complete 360-degree view into what is causing poor performance, where your website can be optimized, and how it can be improved. There are plenty of books focusing on big data and using data analytics to improve websites, or on utilizing usability testing and UX research methods for improvement. This is the first book that combines both subjects into a methodology you can use over and over again to improve any website. UX Optimization is ideal for anyone who wants to combine the power of quantitative data with the insights provided by qualitative data to improve website results. The book uses step-by-step instructions with photos, drawings, and supporting screenshots to show you how to: define personas, conduct behavioral UX data analysis, perform UX and usability testing evaluations, and combine behavioral UX and usability data to create a powerful set of optimization recommendations that can dramatically improve any website. What You’ll Learn Understand personas: what they are and how to use them to analyze data Use quantitative research tools and techniques for analysis Know where to find UX behavioral data and when to use it Use qualitative research tools, techniques, and procedures Analyze qualitative data to find patterns of consistent task flow errors Combine qualitative and quantitative data for a 360-degree view Make recommendations for optimizations based on your findings Test optimization recommendations to ensure improvements are achieved Who This Book Is For Big data analytics (quantitative) professionals who want to learn more about the qualitative side of analysis; UX researchers, usability testers, and UX designers (qualitative professionals) who want to know more about big data and behavioral UX analysis; and students of UX, UX designers, product managers, developers, and those at startups who want to understand how to use behavioral UX and usability testing data to optimize their websites and apps. |
| data science and ux: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science and ux: Search Patterns Peter Morville, Jeffery Callender, 2010-01-14 What people are saying about Search Patterns Search Patterns is a delight to read -- very thoughtful and thought provoking. It's the most comprehensive survey of designing effective search experiences I've seen. --Irene Au, Director of User Experience, Google I love this book! Thanks to Peter and Jeffery, I now know that search (yes, boring old yucky who cares search) is one of the coolest ways around of looking at the world. --Dan Roam, author, The Back of the Napkin (Portfolio Hardcover) Search Patterns is a playful guide to the practical concerns of search interface design. It contains a bonanza of screenshots and illustrations that capture the best of today's design practices and presents a fresh perspective on the broader role of search and discovery. --Marti Hearst, Professor, UC Berkeley and author, Search User Interfaces (Cambridge University Press) It's not often I come across a book that asks profound questions about a fundamental human activity, and then proceeds to answer those questions with practical observations and suggestions. Search Patterns is an expedition into the heart of the web and human cognition, and for me it was a delightful journey that delivered scores of insights. --Dave Gray, Founder and Chairman, XPLANE Search is swiftly transforming everything we know, yet people don't understand how mavens design search: by stacking breadcrumbs, scenting widgets, and keeping eyeballs on the engine. I urge you to put your eyeballs on this unique and important book. --Bruce Sterling, Writer, Futurist, and Co-Founder, The Electronic Frontier Foundation As one who searches a lot (and often ends up frustrated), Search Patterns is a revelation. --Nigel Holmes, Designer, Theorist, and Principal, Explanation Graphics Search Patterns is a fabulous must-have book! Inside, you'll learn the whys and wheres of practically every modern search design trick and technique. --Jared Spool, CEO and Founder, User Interface Engineering Search is among the most disruptive innovations of our time. It influences what we buy and where we go. It shapes how we learn and what we believe. In this provocative and inspiring book, you'll explore design patterns that apply across the categories of web, ecommerce, enterprise, desktop, mobile, social, and real-time search and discovery. Filled with colorful illustrations and examples, Search Patterns brings modern information retrieval to life, covering such diverse topics as relevance, faceted navigation, multi-touch, personalization, visualization, multi-sensory search, and augmented reality. By drawing on their own experience-as well as best practices and evidence-based research-the authors not only offer a practical guide to help you build effective search applications, they also challenge you to imagine the future of discovery. You'll find Search Patterns intriguing and invaluable, whether you're a web practitioner, mobile designer, search entrepreneur, or just interested in the topic. Discover a pattern language for search that embraces user psychology and behavior, information architecture, interaction design, and emerging technology Boost enterprise efficiency and e-commerce sales Enable mobile users to achieve goals, complete tasks, and find what they need Drive design innovation for search interfaces and applications |
| data science and ux: Global UX Whitney Quesenbery, Daniel Szuc, 2011-11-09 Chapter 1: The Start of the Journey Chapter 2: It's a New World Chapter 3: Culture and UX Chapter 4: Building Cultural Awareness Chapter 5: Global Companies and Global Strategies Chapter 6: Effective Global Teams Chapter 7 -- Research in the Field Chapter 8 -- Bringing it Home Chapter 9 -- Design for a Global Audience Chapter 10 -- Delivering ValueThe start of the journey -- It's a new world -- Culture and UX -- Building your cultural awareness -- Global companies and global strategy -- Effective global teams -- Research in the field -- Bringing it home -- Design for a global audience -- Delivering value. |
| data science and ux: Games User Research Anders Drachen, Pejman Mirza-Babaei, Lennart E. Nacke, 2018 Games live and die commercially on the player experience. Games User Research is collectively the way we optimise the quality of the user experience (UX) in games, working with all aspects of a game from the mechanics and interface, visuals and art, interaction and progression, making sure every element works in concert and supports the game UX. This means that Games User Research is essential and integral to the production of games and to shape the experience of players. Today, Games User Research stands as the primary pathway to understanding players and how to design, build, and launch games that provide the right game UX. Until now, the knowledge in Games User Research and Game UX has been fragmented and there were no comprehensive, authoritative resources available. This book bridges the current gap of knowledge in Games User Research, building the go-to resource for everyone working with players and games or other interactive entertainment products. It is accessible to those new to Games User Research, while being deeply comprehensive and insightful for even hardened veterans of the game industry. In this book, dozens of veterans share their wisdom and best practices on how to plan user research, obtain the actionable insights from users, conduct user-centred testing, which methods to use when, how platforms influence user research practices, and much, much more. |
| data science and ux: Practical Empathy Indi Young, 2015-01-15 Conventional product development focuses on the solution. Empathy is a mindset that focuses on people, helping you to understand their thinking patterns and perspectives. Practical Empathy will show you how to gather and compare these patterns to make better decisions, improve your strategy, and collaborate successfully. |
| data science and ux: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science and ux: Designing User Interfaces With a Data Science Approach Banubakode, Abhijit Narayanrao, Bhutkar, Ganesh Dattatray, Kurniawan, Yohannes, Gosavi, Chhaya Santosh, 2022-03-18 Data science has been playing a vital role in almost all major fields. Many researchers are interested in the development of IT applications, which are user-driven with a focus on issues. This can be addressed using data science. User-driven research and data science have gained much attention from many private, public, and government organizations and research institutions. Designing User Interfaces With a Data Science Approach promotes the inclusion of more diversified users for user-centered designs of applications across domains and analyzes user data with a data science approach for effective and user-friendly user interface designs. It introduces the foundations of advanced topics of human-computer interaction, particularly with user-centered designs and techniques. Covering topics such as artificial neural networks, natural dialog systems, and machine learning, this book is an essential resource for faculty, research scholars, industry professionals, students of higher education, mathematicians, data scientists, interaction designers, visual designers, software engineers, user experience researchers, accessibility engineers, cognitive system engineers, academicians, and libraries. |
| data science and ux: Data Science Ivo D. Dinov, Milen Velchev Velev, 2021-12-06 The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time. The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public. |
| data science and ux: Think Like a UX Researcher David Travis, Philip Hodgson, 2019-01-10 Think Like a UX Researcher will challenge your preconceptions about user experience (UX) research and encourage you to think beyond the obvious. You’ll discover how to plan and conduct UX research, analyze data, persuade teams to take action on the results and build a career in UX. The book will help you take a more strategic view of product design so you can focus on optimizing the user’s experience. UX Researchers, Designers, Project Managers, Scrum Masters, Business Analysts and Marketing Managers will find tools, inspiration and ideas to rejuvenate their thinking, inspire their team and improve their craft. Key Features A dive-in-anywhere book that offers practical advice and topical examples. Thought triggers, exercises and scenarios to test your knowledge of UX research. Workshop ideas to build a development team’s UX maturity. War stories from seasoned researchers to show you how UX research methods can be tailored to your own organization. |
| data science and ux: Emerging Innovations and Applications in Computer Science, Statistics and Data Science V. Prakash, Manimannan G, P. Arumugam, 2024-10-14 The rapid advancement of technology and the rise of data-driven innovations have profoundly shaped research across a variety of fields. This edited book consolidates pioneering studies and analyses that utilize cutting-edge approaches such as machine learning, statistical techniques, and data-centric methodologies. From predictive analytics in healthcare to breakthroughs in cyber security and Internet of Things (IoT) applications, the content presents a wealth of insights aimed at tackling challenges in today’s fast-paced, digitally transformed world. It underscores the transformative role of artificial intelligence, big data analytics, and block chain technologies in revolutionizing sectors like healthcare, finance, e-commerce, and climate research. This collection of chapters spans a diverse range of interdisciplinary subjects. It features healthcare studies that explore predictive models for conditions such as cervical and lung cancers, as well as thyroid disorders, showcasing the revolutionary impact of artificial intelligence in improving diagnostic precision and treatment strategies. Concurrently, research on IoT, cloud computing, and block chain highlights the growing necessity of secure and interconnected infrastructures in paving the way for smart living and decentralized systems. Statistical methodologies, including time series analysis, Bayesian models, and survival analysis, are explored in real-world contexts, offering valuable insights into climatic trends, consumer behavior, and industrial advancements. This book is the result of a collaborative effort by esteemed researchers and practitioners, whose expertise provides innovative solutions to real-world challenges. By bridging theoretical advancements with practical implementations, the volume serves as a comprehensive resource for scholars, industry experts, and students. We trust that this work will inspire further research and catalyze meaningful progress in the domains of technology, healthcare, and beyond. |
| data science and ux: Design, User Experience, and Usability: UX Research and Design Marcelo M. Soares, Elizabeth Rosenzweig, Aaron Marcus, 2021-07-03 This three volume set LNCS 12779, 12780, and 12781 constitutes the refereed proceedings of the 10th International Conference on Design, User Experience, and Usability, DUXU 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of DUXU 2021, Part I, are organized in topical sections named: UX Design Methods and Techniques; Methods and Techniques for UX Research; Visual Languages and Information Visualization; Design Education and Practice. |
| data science and ux: Bottlenecks David C. Evans, 2017-02-11 Learn the psychological constrictions of attention, perception, memory, disposition, motivation, and social influence that determine whether customers will be receptive to your digital innovations. Bottlenecks: Aligning UX Design with User Psychology fills a need for entrepreneurs, designers, and marketing professionals in the application of foundational psychology to user-experience design. The first generation of books on the topic focused on web pages and cognitive psychology. This book covers apps, social media, in-car infotainment, and multiplayer video games, and it explores the crucial roles played by behaviorism, development, personality, and social psychology. Author David Evans is an experimental psychology Ph.D. and senior manager of consumer research at Microsoft who recounts high-stakes case studies in which behavioral theory aligned digital designs with the bottlenecks in human nature to the benefit of users and businesses alike. Innova tors in design and students of psychology will learn: The psychological processes determining users’ perception of, engagement with, and recommendation of digital innovations Examples of interfaces before and after simple psychological alignments that vastly enhanced their effectiveness Strategies for marketing and product development in an age of social media and behavioral targeting Hypotheses for research that both academics and enterprises can perform to better meet users’ needs Who This Book Is For Designers and entrepreneurs will use this book to give their innovations an edge on what are increasingly competitive platforms such as apps, bots, in-car apps, augmented reality content. Usability researchers and market researchers will leverage it to enhance their consulting and reporting. Students and lecturers in psychology departments will want it to help land employment in the private sector. Praise “Bottlenecks’ is a tight and eminently actionable read for business leaders in startups and enterprises alike. Evans gives us a rich sense of key psychological processes and even richer examples of them in action.” - Nir Eyal, Author of Hooked: How to Build Habit-Forming Products “Clients frequently ask our UX researchers and designers for deeper truths about why certain designs work and others fail. Bottlenecks offers practical explanations and evidence based on the idea that human cognition did not begin with the digital age.” - John Dirks, UX Director and Partner, Blink UX “Bottlenecks brings together two very important aspects of user experience design: understanding users and translating this into business impact. A must-read for anyone who wants to learn both.” - Josh Lamar, Sr. UX Lead, Microsoft Outlook |
| data science and ux: Eye Tracking in User Experience Design Jennifer Romano Bergstrom, Andrew Schall, 2014-03-12 Eye Tracking for User Experience Design explores the many applications of eye tracking to better understand how users view and interact with technology. Ten leading experts in eye tracking discuss how they have taken advantage of this new technology to understand, design, and evaluate user experience. Real-world stories are included from these experts who have used eye tracking during the design and development of products ranging from information websites to immersive games. They also explore recent advances in the technology which tracks how users interact with mobile devices, large-screen displays and video game consoles. Methods for combining eye tracking with other research techniques for a more holistic understanding of the user experience are discussed. This is an invaluable resource to those who want to learn how eye tracking can be used to better understand and design for their users. - Includes highly relevant examples and information for those who perform user research and design interactive experiences - Written by numerous experts in user experience and eye tracking - Highly relevant to anyone interested in eye tracking & UX design - Features contemporary eye tracking research emphasizing the latest uses of eye tracking technology in the user experience industry |
| data science and ux: UX Research Brad Nunnally, David Farkas, 2016-11-15 One key responsibility of product designers and UX practitioners is to conduct formal and informal research to clarify design decisions and business needs. But there’s often mystery around product research, with the feeling that you need to be a research Zen master to gather anything useful. Fact is, anyone can conduct product research. With this quick reference guide, you’ll learn a common language and set of tools to help you carry out research in an informed and productive manner. This book contains four sections, including a brief introduction to UX research, planning and preparation, facilitating research, and analysis and reporting. Each chapter includes a short exercise so you can quickly apply what you’ve learned. Learn what it takes to ask good research questions Know when to use quantitative and qualitative research methods Explore the logistics and details of coordinating a research session Use softer skills to make research seem natural to participants Learn tools and approaches to uncover meaning in your raw data Communicate your findings with a framework and structure |
| data science and ux: UX Strategy Jaime Levy, 2015-05-20 User experience (UX) strategy requires a careful blend of business strategy and UX design, but until now, there hasn’t been an easy-to-apply framework for executing it. This hands-on guide introduces lightweight strategy tools and techniques to help you and your team craft innovative multi-device products that people want to use. Whether you’re an entrepreneur, UX/UI designer, product manager, or part of an intrapreneurial team, this book teaches simple-to-advanced strategies that you can use in your work right away. Along with business cases, historical context, and real-world examples throughout, you’ll also gain different perspectives on the subject through interviews with top strategists. Define and validate your target users through provisional personas and customer discovery techniques Conduct competitive research and analysis to explore a crowded marketplace or an opportunity to create unique value Focus your team on the primary utility and business model of your product by running structured experiments using prototypes Devise UX funnels that increase customer engagement by mapping desired user actions to meaningful metrics |
| data science and ux: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Sun-Yuan Hsieh, Suseendran Gopalakrishnan, Balaganesh Duraisamy, 2021-09-27 This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man. |
| data science and ux: The Decision Maker's Handbook to Data Science Stylianos Kampakis, 2019-11-26 Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science. |
| data science and ux: Graph Databases in Action Dave Bechberger, Josh Perryman, 2020-11-24 Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization. Summary Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Isolated data is a thing of the past! Now, data is connected, and graph databases—like Amazon Neptune, Microsoft Cosmos DB, and Neo4j—are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value. About the book Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization. What's inside Graph databases vs. relational databases Systematic graph data modeling Querying and navigating a graph Graph patterns Pitfalls and antipatterns About the reader For software developers. No experience with graph databases required. About the author Dave Bechberger and Josh Perryman have decades of experience building complex data-driven systems and have worked with graph databases since 2014. Table of Contents PART 1 - GETTING STARTED WITH GRAPH DATABASES 1 Introduction to graphs 2 Graph data modeling 3 Running basic and recursive traversals 4 Pathfinding traversals and mutating graphs 5 Formatting results 6 Developing an application PART 2 - BUILDING ON GRAPH DATABASES 7 Advanced data modeling techniques 8 Building traversals using known walks 9 Working with subgraphs PART 3 - MOVING BEYOND THE BASICS 10 Performance, pitfalls, and anti-patterns 11 What's next: Graph analytics, machine learning, and resources |
| data science and ux: What UX is Really About Celia Hodent, 2021-12-14 In this not-too-long and easy-to-read book, author Celia Hodent presents a clear overview of the challenges, demands, and rewards of becoming a user experience professional. If this field interests you, there’s no better place to start than with the volume you now hold in your hand. Alan Cooper, Ancestry Thinker, Software Alchemist, Regenerative Rancher, Author of The Inmates Are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity The main objective of What UX is Really About: Introducing a Mindset for Great Experiences is to provide a quick introduction to user experience (UX 101) for students, professionals, or simply curious readers who want to understand this trendy yet commonly misunderstood practice better. Readers will learn that UX is much more than a set of techniques, guidelines, and tools. It is a mindset; a philosophy that takes the perspective of the humans that will use a product. It is about solving their problems, offering them a pleasurable experience, and building a win-win, long-lasting relationship between them and the company developing the product. Above all, it is about improving people’s lives with technology. What UX is Really About is informative, concise, and provides readers with a high-level overview of the science, design, and methodologies of UX. KEY FEATURES: • The most approachable and concise introduction book about UX. • Easy to read and aims to popularize the UX mindset while debunking its main misconceptions. • Small format size makes it easy to carry around. • Includes content relatable and meaningful to the readers by taking many examples from everyday life with a conversational and light writing style. • Tackles the psychology, design, research, process, strategy, and ethics behind offering the best experience with products, systems, or services. • Includes a glossary. Celia Hodent holds a PhD in psychology, and is a leading expert in the application of cognitive science and psychology to product development, with over 13 years of experience in the development of UX strategy in video game studios, such as Ubisoft, LucasArts, and Epic Games (Fortnite). She currently leads an independent UX consultancy, working with a wide range of international media and enterprise companies to help ensure their products are engaging, successful, and respectful of users. Celia conducts workshops and provides guidance on the topics of game-based UX, playful learning (gamification), ethics, implicit biases, and inclusion in tech. Celia is the author of The Gamer’s Brain: How Neuroscience and UX Can Impact Video Game Design and The Psychology of Video Games. |
| data science and ux: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| data science and ux: Design, User Experience, and Usability: Theory and Practice Aaron Marcus, Wentao Wang, 2018-07-10 The three-volume set LNCS 10918, 10919, and 10290 constitutes the proceedings of the 7th International Conference on Design, User Experience, and Usability, DUXU 2018, held as part of the 20th International Conference on Human-Computer Interaction, HCII 2018, in Las Vegas, NV, USA in July 2018. The total of 1171 papers presented at the HCII 2018 conferences were carefully reviewed and selected from 4346 submissions. The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of applications areas. The total of 165 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in this three-volume set. The 55 papers included in this volume are organized in topical sections on design thinking, methods and practice, usability and user experience evaluation methods and tools, and DUXU in software development. |
| data science and ux: Successful User Experience: Strategies and Roadmaps Elizabeth Rosenzweig, 2015-08-03 Successful User Experience: Strategy and Roadmaps provides you with a hands-on guide for pulling all of the User Experience (UX) pieces together to create a strategy that includes tactics, tools, and methodologies. Leveraging material honed in user experience courses and over 25 years in the field, the author explains the value of strategic models to refine goals against available data and resources. You will learn how to think about UX from a high level, design the UX while setting goals for a product or project, and how to turn that into concrete actionable steps. After reading this book, you'll understand: - How to bring high-level planning into concrete actionable steps - How Design Thinking relates to creating a good UX - How to set UX Goals for a product or project - How to decide which tool or methodology to use at what point in product lifecycle This book takes UX acceptance as a point of departure, and builds on it with actionable steps and case studies to develop a complete strategy, from the big picture of product design, development and commercialization, to how UX can help create stronger products. This is a must-have book for your complete UX library. - Uses strategic models that focus product design and development - Teaches how to decipher what tool or methodology is right for a given moment, project, or a specific team - Presents tactics on how to understand how to connect the dots between tools, data, and design - Provides actionable steps and case studies that help users develop a complete strategy, from the big picture of product design, development, and commercialization, to how UX can help create stronger products - Case studies in each chapter to aid learning |
| data science and ux: Observing the User Experience Elizabeth Goodman, Mike Kuniavsky, 2012-09-01 Observing the User Experience: A Practitioner's Guide to User Research aims to bridge the gap between what digital companies think they know about their users and the actual user experience. Individuals engaged in digital product and service development often fail to conduct user research. The book presents concepts and techniques to provide an understanding of how people experience products and services. The techniques are drawn from the worlds of human-computer interaction, marketing, and social sciences. The book is organized into three parts. Part I discusses the benefits of end-user research and the ways it fits into the development of useful, desirable, and successful products. Part II presents techniques for understanding people's needs, desires, and abilities. Part III explains the communication and application of research results. It suggests ways to sell companies and explains how user-centered design can make companies more efficient and profitable. This book is meant for people involved with their products' user experience, including program managers, designers, marketing managers, information architects, programmers, consultants, and investors. - Explains how to create usable products that are still original, creative, and unique - A valuable resource for designers, developers, project managers - anyone in a position where their work comes in direct contact with the end user - Provides a real-world perspective on research and provides advice about how user research can be done cheaply, quickly and how results can be presented persuasively - Gives readers the tools and confidence to perform user research on their own designs and tune their software user experience to the unique needs of their product and its users |
| data science and ux: Ethics and Data Science Mike Loukides, Hilary Mason, DJ Patil, 2018-07-25 As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today. |
| data science and ux: Contemporary Research Methods and Data Analytics in the News Industry Gibbs, William J., 2015-07-01 The advent of digital technologies has changed the news and publishing industries drastically. While shrinking newsrooms may be a concern for many, journalists and publishing professionals are working to reorient their skills and capabilities to employ technology for the purpose of better understanding and engaging with their audiences. Contemporary Research Methods and Data Analytics in the News Industry highlights the research behind the innovations and emerging practices being implemented within the journalism industry. This crucial, industry-shattering publication focuses on key topics in social media and video streaming as a new form of media communication as well the application of big data and data analytics for collecting information and drawing conclusions about the current and future state of print and digital news. Due to significant insight surrounding the latest applications and technologies affecting the news industry, this publication is a must-have resource for journalists, analysts, news media professionals, social media strategists, researchers, television news producers, and upper-level students in journalism and media studies. This timely industry resource includes key topics on the changing scope of the news and publishing industries including, but not limited to, big data, broadcast journalism, computational journalism, computer-mediated communication, data scraping, digital media, news media, social media, text mining, and user experience. |
| data science and ux: Human Interface and the Management of Information. Information and Knowledge in Applications and Services Sakae Yamamoto, 2014-08-01 The two-volume set LNCS 8521 and 8522 constitutes the refereed proceedings of the Human Interface and the Management of Information thematic track, held as part of the 16th International Conference on Human-Computer Interaction, HCII 2014, held in Heraklion, Greece, in June 2014, jointly with 13 other thematically similar conferences. The total of 1476 papers and 220 posters presented at the HCII 2014 conferences were carefully reviewed and selected from 4766 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. This volume contains papers addressing the following major topics: e-learning and e-education; decision support; information and interaction in aviation and transport; safety, security and reliability; communication, expression and emotions; art, culture and creativity; information and knowledge in business and society. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
