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Open science data A Complete Guide.
Modern information and communication technologies, together with a cultural upheaval within the research community, have profoundly changed research in nearly every aspect. Ranging from sharing and discussing ideas in social networks for scientists to new collaborative environments and novel publication formats, knowledge creation and dissemination as we know it is experiencing a vigorous shift towards increased transparency, collaboration and accessibility. Many assume that research workflows will change more in the next 20 years than they have in the last 200. This book provides researchers, decision makers, and other scientific stakeholders with a snapshot of the basics, the tools, and the underlying visions that drive the current scientific (r)evolution, often called ‘Open Science.’
How will we insure seamless interoperability of Open science data moving forward? What are specific Open science data Rules to follow? What potential environmental factors impact the Open science data effort? What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Open science data? What prevents me from making the changes I know will make me a more effective Open science data leader? This valuable Open science data self-assessment will make you the trusted Open science data domain specialist by revealing just what you need to know to be fluent and ready for any Open science data challenge. How do I reduce the effort in the Open science data work to be done to get problems solved? How can I ensure that plans of action include every Open science data task and that every Open science data outcome is in place? How will I save time investigating strategic and tactical options and ensuring Open science data opportunity costs are low? How can I deliver tailored Open science data advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Open science data essentials are covered, from every angle: the Open science data self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Open science data outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Open science data practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Open science data are maximized with professional results. Your purchase includes access details to the Open science data self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Open Access Data is emerging as a source for cutting edge scholarship. This concise book provides guidance from generating a research idea to publishing results. Both young researchers and well-established scholars can use this book to upgrade their skills with respect to emerging data sources, analysis, and even post-publishing promotion. At the end of each chapter, a tutorial simulates a real example, allowing readers to apply what they learned about accessing open data, and analyzing this data to reach the results. This book can be of use by established researchers analyzing data, publishing, and actively promoting ongoing and research. Key selling features: Describes the steps, from A-Z, for doing open data research Includes interactive tutorials following each chapter Provides guidelines for readers so that they can use their own accessed open data Reviews recent software and websites promoting and enabling open data research Supplements websites which update recent open data sources
Choice Outstanding Academic Title 2023 Offers librarians an authoritative overview of the full spectrum of significant issues and controversies related to open scholarship in a candid and fair-minded manner. There has long been a debate about openness in scholarship, and even the term itself continues to be debated. Openness is a complex and multidimensional concept, and its nature in scholarship continually evolves. One of the hindrances to the transition to greater openness in academia is this lack of clear understanding about how it fits into the practice of scholarly communication. To ensure that librarians as knowledge managers can better educate scholars about the benefits and challenges of open scholarship, Victoria Martin's The Complete Guide to Open Scholarship brings clarity to the concept of openness, tests assumptions concerning it, and strikes the right balance between breaking down complex ideas into simpler ones and honoring the reader's intelligence and previous knowledge of the subject. Readers will learn the history of openness in scholarship as well as several ways in which openness can be perceived. Drawing on specific examples, Martin discusses the most prominent scholarly models based on openness, barriers to openness, concerns about openness in scholarship, and the future of open scholarship.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Open Access Data is emerging as a source for cutting edge scholarship. This concise book provides guidance from generating a research idea to publishing results. Both young researchers and well-established scholars can use this book to upgrade their skills with respect to emerging data sources, analysis, and even post-publishing promotion. At the end of each chapter, a tutorial simulates a real example, allowing readers to apply what they learned about accessing open data, and analyzing this data to reach the results. This book can be of use by established researchers analyzing data, publishing, and actively promoting ongoing and research. Key selling features: Describes the steps, from A-Z, for doing open data research Includes interactive tutorials following each chapter Provides guidelines for readers so that they can use their own accessed open data Reviews recent software and websites promoting and enabling open data research Supplements websites which update recent open data sources cent software and websites promoting and enabling open data research Supplements websites which update recent open data sources
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective. - Offers a range of data science models, engineering tools, and federal use-cases - Provides foundational observations into government data resources and requirements - Introduces experiences and examples of data openness from the US and other countries - A step-by-step guide for the conversion of government towards data-driven policy making - Focuses on presenting data models that work within the constraints of the US government - Presents the why, the what, and the how of injecting AI into federal culture and software systems
We’re living in a digital world. Most of our global economy is digital and the sheer volume of data is stupendous. It’s 2020 and we’re living in the future. Data Scientist is one of the hottest job on the market right now. Demand for data science is huge and will only grow, and it seems like it will grow much faster than the actual number of data scientists. So if you want to make a career change and become a data scientist, now is the time. This book will guide you through the process. From my experience of working with multiple companies as a project manager, a data science consultant or a CTO, I was able to see the process of hiring data scientists and building data science teams. I know what’s important to land your first job as a data scientist, what skills you should acquire, what you should show during a job interview.