This handbook was written and edited by a group of about 40 collaborators in a series of six book sprints that took place between 1 and 10 June 2021. It aims to support higher education institutions with the practical implementation of content relating to the FAIR principles in their curricula, while also aiding teaching by providing practical material, such as competence profiles, learning outcomes, lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.

Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern science communication, and data stewardship. The FAIR principles are used as a guide throughout the text, and this book should leave experimentalists consciously incompetent about data stewardship and motivated to respect data stewards as representatives of a new profession, while possibly motivating others to consider a career in the field. The ebook, avalable for no additional cost when you buy the paperback, will be updated every 6 months on average (providing that significant updates are needed or avaialble). Readers will have the opportunity to contribute material towards these updates, and to develop their own data management plans, via the free Data Stewardship Wizard.

The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study. Table of Contents: List of Figures / Introduction / Principles of Linked Data / The Web of Data / Linked Data Design Considerations / Recipes for Publishing Linked Data / Consuming Linked Data / Summary and Outlook

Using the factor analysis of information risk (FAIR) methodology developed over ten years and adopted by corporations worldwide, Measuring and Managing Information Risk provides a proven and credible framework for understanding, measuring, and analyzing information risk of any size or complexity. Intended for organizations that need to either build a risk management program from the ground up or strengthen an existing one, this book provides a unique and fresh perspective on how to do a basic quantitative risk analysis. Covering such key areas as risk theory, risk calculation, scenario modeling, and communicating risk within the organization, Measuring and Managing Information Risk helps managers make better business decisions by understanding their organizational risk. - Uses factor analysis of information risk (FAIR) as a methodology for measuring and managing risk in any organization. - Carefully balances theory with practical applicability and relevant stories of successful implementation. - Includes examples from a wide variety of businesses and situations presented in an accessible writing style.

Explains what fairness is and ways to be fair.

'Ben's book is an elegant and essential intervention in an era of enervating culture wars. It asks and answers nothing less than the most important question of our time: how can we recover the ability to talk to one another?' - Sathnam Sanghera, author of Empireland What does it mean to be fair? Why do we feel unfairness so strongly? What has happened to us today that we spend more time condemning each other's views than giving each other a fair hearing? The idea of fairness is one of the most commonly-expressed concepts, yet nobody ever stops to think what it really means. We all simply take the word 'fair' for granted. In this polemical guide to fairness, Ben Fenton explains the meaning of the word, how it fits into our genetic make-up and why we need our innate sense of fair play now more than ever. Fenton explores the idea that the unconscious procedure that humans go through in deciding fairness is the vital balancing act between competition and cooperation, the two driving forces that have made us the super-species of Planet Earth. He describes the neurology, anthropology, psychology, history and future of fairness and looks at how it affects our lives through politics, law, sex, religion, race, sport, business and even war. As a reporter of thirty years' experience, Fenton brings all his skills to bear in a lively and challenging description of the profound inner meaning of a throwaway phrase and why it matters so much to every single person in the world to seek To Be Fair. PART I – WHY SHOULD WE TRY TO BE FAIR? 1. Why You Should Read This Book 2. The Principles of Fairness 3. The Neurology and Psychology of Fairness 4. Fairness in History 5. Is Fairness a Creation of the English speaking World? (Spoiler alert: No) PART II – WHAT DOES FAIRNESS LOOK LIKE IN THE MODERN WORLD? 6. Fairness in Sport (and other forms of War) 7. Fairness in Business and Economics 8. Fairness in Law and Taxation 9. Fairness in Communication and Technology 10. Fairness in Politics and Government 11. Fairness in Human Relationships 12. Is Fair Play the Answer to the Ultimate Question?

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.

Differentiated instruction is a nice idea, but what happens when it comes to assessing and grading students? What's both fair and leads to real student learning? Fair Isn't Always Equal answers that question and much more. Rick Wormeli offers the latest research and common sense thinking that teachers and administrators seek when it comes to assessment and grading in differentiated classes. Filled with real examples and "gray" areas that middle and high school educators will easily recognize, Rick tackles important and sometimes controversial assessment and grading issues constructively. The book covers high-level concepts, ranging from "rationale for differentiating assessment and grading" to "understanding mastery" as well as the nitty-gritty details of grading and assessment, such as: whether to incorporate effort, attendance, and behavior into academic grades;whether to grade homework;setting up grade books and report cards to reflect differentiated practices;principles of successful assessment;how to create useful and fair test questions, including how to grade such prompts efficiently;whether to allow students to re-do assessments for full credit. This thorough and practical guide also includes a special section for teacher leaders that explores ways to support colleagues as they move toward successful assessment and grading practices for differentiated classrooms.

This handbook was written and edited by a group of about 40 collaborators in a series of six book sprints that took place between 1 and 10 June 2021. It aims to support higher education institutions with the practical implementation of content relating to the FAIR principles in their curricula, while also aiding teaching by providing practical material, such as competence profiles, learning outcomes, lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.