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The DEI Metadata Handbook: A Guide to Diverse, Equitable, and Inclusive Description
H. E. Wintermute; Heather Campbell; Christopher Dieckman · Iowa State University Digital Press · 2024
Written primarily for professionals in library and information science but with applicability to archives and other information management industries, this handbook provides an overview of metadata work that focuses on diversity, equity, and inclusion (DEI). DEI metadata work has several goals: enhancing diverse representation in descriptive metadata; improving discovery of diverse resources; and mitigating negative effects of inaccurate, outdated, or offensive terminology. Readers will gain a broad awareness of DEI-related issues in metadata creation and management; learn techniques for retroactively reviewing and updating existing metadata to address these issues; and develop strategies to create metadata that better meets DEI needs.
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Social Data Analysis
Mikaila Arthur; Roger Clark · Rhode Island College Digital Publishing · 2021
Social data analysis enables you, as a researcher, to organize the facts you collect during your research. Your data may have come from a questionnaire survey, a set of interviews, or observations. They may be data that have been made available to you from some organization, national or international agency or other researchers. Whatever their source, social data can be daunting to put together in a way that makes sense to you and others. This book is meant to help you in your initial attempts to analyze data. In doing so it will introduce you to ways that others have found useful in their attempts to organize data. You might think of it as like a recipe book, a resource that you can refer to as you prepare data for your own consumption and that of others. And, like a recipe book that teaches you to prepare simple dishes, you may find this one pretty exciting. Analyzing data in a revealing way is at least as rewarding, we’ve found, as it is to cook up a yummy cashew carrot paté or a steaming corn chowder. We’d like to share our pleasure with you.
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Arcade Expression Crash Course with COVID-19 data
Wing Cheung · Wing Cheung · 2022
Written for ArcGIS Pro 2.5
![The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond [Revised Edition]](/api/files/covers/otl-1718.webp)
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The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond [Revised Edition]
J.J. Sylvia IV · ROTEL · 2024
The Data Renaissance delves into the complexities of data's role in various industries and its broader impact on society. It highlights the challenges in investigating data practices, citing examples like TikTok, where algorithms and data handling are closely guarded secrets. The content, contributed by students under the guidance of an expert, covers a wide range of topics, including the ethical aspects of generative AI in education and the workplace, and case studies reflecting real-world experiences. This evolving text, intended to be updated with each class, serves as a dynamic resource for educators and students alike, offering insights and discussion guides for an in-depth understanding of the ever-changing landscape of data in our digital age.
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Discrete Mathematics for Information Technology
Simon Crothers · Kwantlen Polytechnic University · 2026
Discrete Mathematics for Information Technology is an applied, student-focused introduction to the mathematical foundations that underpin modern computing. Designed for learners in information technology and business contexts, this textbook bridges theory and practice by connecting core concepts, such as number systems, data representation, logic, proofs, relations, algorithms, Boolean algebra, and graph theory, to real-world applications in programming, networking, databases, and systems design. Each chapter is structured with clear explanations, worked examples, case studies, and programming-oriented exercises that reinforce both conceptual understanding and practical skills. Emphasis is placed on developing analytical thinking, problem-solving, and the ability to model and reason about digital systems with precision and confidence. As an open educational resource, the text is adaptable and accessible, supporting a wide range of teaching and learning environments. By integrating mathematical rigor with authentic IT scenarios, this book equips students with the essential tools needed to succeed in modern, data-driven and technology-focused careers.
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Scientific Computing for Chemists with Python
Charles Weiss · Charles J. Weiss · 2025
This book serves as an introduction to coding for chemists. The tools employed in this book are the powerful and popular combination of Jupyter notebooks and the Python programming language. No background beyond first-year college chemistry and occasionally some very basic spectroscopy (for advanced chapters) is assumed for most of this book. This book starts with a brief primer on Jupyter notebooks in chapter 0 and computer programming with Python in chapters 1 and 2. If you already have a background in these tools, feel free to skip ahead. The rest of the book dives into applications of Python to solving chemical problems. Python and Jupyter were chosen for a variety of reasons, including that they are: Relatively easy to use and learn Powerful and well-suited for solving chemical problems Free, open-source software Cross-platform (e.g., runs on Windows, macOS, and Linux) Supplemented with numerous, specialized libraries for handling specific types of data or problems (e.g., machine learning) Supported by a helpful and welcoming community Learning to use a number of popular Python scientific libraries to solve chemical problems is one of the themes of this book. A Python library can be thought of as a tool pack with premade functions for performing common tasks in scientific data processing, analysis, and visualization. For example, the matplotlib library provides a variety of functions for creating a wide range of plots, while the scikit-learn library contains functions and resources for machine learning.
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Introduction to Computer Programming with Python
Harris Wang · Athabasca University Press · 2023
This introduction to computer programming with Python begins with some of the basics of computing and programming before diving into the fundamental elements and building blocks of computer programs in Python language. From the installation of Python, Python interactive programming, and integrated development environments to raising and handling exceptions, using compound data types to solve problems, and implement divide-and-conquer processes using functions, classes and modules, this textbook will set students up for success in programming and computing study and practice. The included exercises and projects are designed to hone students’ skills.
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Programming with Java
Ashik Bhuiyan; Md Amiruzzaman · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2025
Programming with Java is a beginner-friendly eTextbook that introduces readers to the fundamentals of Java, one of the most widely used programming languages. It covers key concepts like syntax, data types, control structures, and object-oriented programming, offering clear explanations and practical examples. The chapters build progressively, making the material accessible even to those with little or no programming background. The book also explores important topics such as exception handling, file I/O, and basic data structures, providing a strong foundation for further computer science studies. With interactive exercises and end-of-chapter projects, readers can apply their knowledge through real-world applications. This eTextbook equips students with the skills they need to confidently take on advanced programming challenges and pursue successful careers in software development.
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The Crystal Ball Instruction Manual
Stephen Davies · University of Mary Washington · 2020
Welcome to Volume Two of the Crystal Ball series: Foundations for Data Science! I titled the first volume “Introduction to Data Science” because it led you through a dip-your-toes-in-the-water experience. You took a brief tour through the various elements in this diverse field and got a feel for what it was all about. Since you’re still reading, this means you’re still interested, and ready to explore the next level. In Foundations, we’ll solidify your growing knowledge so that you have a firm base on which to build everything else. Future volumes in this series will cover applications, advanced techniques, and some special data types that require special treatment. But it all ultimately rests on the skill set you’ll have built in Volumes One and Two of this series.
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An Introduction to Python Jupyter Notebooks for College Math Teachers
Paul Isihara; Claire Wagner; Peter Jantsch; Thomas VanDrunen · Paul Isihara, Claire Wagner, Peter Jantsch, and Thomas VanDrunen · 2024
This book is an introduction to the use of Python Jupyter Notebooks (JNBs) for college math teachers and their students. The book is an evolving work in progress, but we hope this 1st Edition may already be quite useful to a college math teacher who is interested to use Jupyter Notebooks in their courses. Each section of the book has been written in the form of a Jupyter Notebook and can be downloaded using the download button at the top menu bar. In some cases, additional data files or image files may need to be downloaded as well. All the files are available in the src folder of the github repository https://github.com/timothyprojectGiG/JB_Math_Textbook Each chapter contains a JNB lab with solutions. Experienced teachers can modify these labs and create new labs tailored to their courses. The chapters were written by different authors/authorship teams, and as such, vary in style. Each chapter essentially can be read independently of the others as long as one has the pre-requisite mathematical knowledge. Some effort has been made to standardize the chapter formats, and the index may help the reader find specific topics of interest. There is virtually no limit to what can be done with JNBs, and we hope this work serves as a useful starting point for teachers and students to enrich and enliven the study of mathematics using this tool. The JNBs used to create this book contain special markdown code which are needed to produce features such as indexing, referencing, and highlight boxes in the Jupyter Book. Some editing of a downloaded JNB may may make it more suitable for classroom use.
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An Open Guide to Data Structures and Algorithms
Paul Bible; Lucas Moser; Mia Scarlato · PALNI · 2023
This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of speed, memory usage, and programming complexity that separate novice programmers from professional practitioners.
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The Shallow and the Deep: A biased introduction to neural networks and old school machine learning
Michael Biehl · University of Groningen Press · 2023
The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.
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Programming Persistent Memory: A Comprehensive Guide for Developers
Stteve Scargall · Apress · 2020
Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK) Design and optimize data structures for persistent memory Study how real-world applications are modified to le
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Computer Systems Security: Planning for Success
Ryan Tolboom · Ryan Tolboom
The text, labs, and review questions in this book are designed as an introduction to the applied topic of computer security. With these resources students will learn ways of preventing, identifying, understanding, and recovering from attacks against computer systems. This text also presents the evolution of computer security, the main threats, attacks and mechanisms, applied computer operation and security protocols, main data transmission and storage protection methods, cryptography, network systems availability, recovery, and business continuation procedures.
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Earth, Space, and Environmental Science Explorations with ArcGIS Pro
Wing Cheung · Wing Cheung
When I first started teaching Geographic Information Systems (GIS) over 15 years ago, there were only a handful of GIS tutorial books on the market. While a few of them focused on a particular theme, many of them were mainly technical publications with no unifying theme. These publications are perfect for teaching new users about basic GIS operations and continue to be invaluable to many users today. However, these publications consisted of tutorials focusing on vastly different real-world scenarios, and felt a little disjointed to integrate into a class that focuses on a specific field (e.g. Environmental Science, Geography, Urban Planning). Since then, I am happy to see that there has been a proliferation of publications that are not only focused on introducing the technology, but also provide an introduction to a specific field as well as GIS. It is with this consideration in mind that I wrote this book to not only introduce students to the tools in ArcGIS Pro, but also to provide a glimpse into some of the topics that may be covered in an introductory course in Geography, Environmental Science, or related disciplines. I aimed to achieve a balance of technical instruction and information about Earth, Space, and Environmental Sciences, to make this publication helpful for new GIS learners, as well as students who may be interested in learning about the applications of GIS in the Earth, Space, and Environmental Sciences. Before you begin with the book, please download and unzip the data for the book at https://tinyurl.com/ESESGIS. Each chapter will begin with a list of the technical objectives for the lab exercise, which is particularly useful to refer to should you need a refresher on how to perform a particular task at a later date. The list of technical objectives is followed by a short introduction that provides the context for the exercise. Subsequently, you will find the step-by-step directions for the exercises with questions embedded throughout (in yellow h
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Evidence-based Software Engineering
Derek Jones · Knowledge Software · 2020
This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available. The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster. The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.
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The Crystal Ball Instruction Manual
Stephen Davies · University of Mary Washington · 2020
A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.
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The Discipline of Organizing: 4th Professional Edition
Robert Glushko · University of California, Berkeley · 2013
We organize things, we organize information, we organize information about things, and we organize information about information. But even though “organizing” is a fundamental and ubiquitous challenge, when we compare these activities their contrasts are more apparent than their commonalities. We propose to unify many perspectives about organizing with the concept of an Organizing System, defined as an intentionally arranged collection of resources and the interactions they support. Every Organizing System involves a collection of resources, a choice of properties or principles used to describe and arrange resources, and ways of supporting interactions with resources. By comparing and contrasting how these activities take place in different contexts and domains, we can identify patterns of organizing. We can create a discipline of organizing in a disciplined way. The 4th edition builds a bridge between organizing and data science. It reframes descriptive statistics as organizing techniques, expands the treatment of classification to include computational methods, and incorporates many new examples of data-driven resource selection, organization, maintenance, and personalization. It introduces a new “data science” category of discipline-specific content, both in the chapter text and in endnotes, marked with [DS] in editions that contain endnotes.
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A Primer for Computational Biology
Shawn O'Neil · Oregon State University · 2017
A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.
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The Unicode cookbook for linguists: Managing writing systems using orthography profiles
Steven Moran; Michael Cysouw · Language Science Press · 2018
This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research.