The Advanced Certificate and the Advanced Diploma in Applications of ICT in Libraries permit library staff to obtain accreditation for their skills in the use of ICT. Anyone can make use of the materials and assessment is available in variety of modes, including distance learning.
This resource is a modification of the Washington Models for the Evaluation of Bias Content in Instructional Materials (2009) that is made available through OER Commons under a public domain license. This resource attempts to both update the content with more contemporary vocabulary and also to narrow the scope to evaluating still images as they are found online. It was developed as a secondary project while working on a BranchED OER grant during summer 2020. It includes an attached rubric adapted from the Washington Model (2009).
The book presents a coherent theory of building information, focusing on its representation and management in the digital era. It addresses issues such as the information explosion and the structure of analogue building representations to propose a parsimonious approach to the deployment and utilization of symbolic digital technologies like BIM.
Identification and discovery of OER is a significant barrier for faculty adoption. Mapping OER to courses is a strategy library professionals can adopt to ease the burden on faculty. The Course Mapping Companion Kit provides curation workflow steps that can be adapted to institutional needs. The companion kit also provides instructions on preparing curated OER with course alignment tags for inclusion on VIVA Open, an OER Commons hosted website. By tagging curated, course-aligned OER to VIVA Open, faculty may trace and locate OER suitable for their courses, as well as courses at other Virginia institutions of higher education.
Creative Commons for Educators, Academic Librarians, and GLAM by Creative Commons is organized into the following 5 units:
Unit 1: What Is Creative Commons
Unit 2: Copyright Law
Unit 3: Anatomy of a CC License
Unit 3: Anatomy of a CC license
Unit 4: Using CC Licenses and CC-Licensed Works
Unit 5: CC for Educators
5. CC for Educators
Unit 5: CC for Academic Librarians
Unit 5: CC for GLAM
Additional Certificate Resources (Template syllabus, Word documents, Epub files) are available here: https://certificates.creativecommons.org/about/certificate-resources-cc-by/.
This is Volume Two of the Crystal Ball series: Foundations for Data Science. The author titled the first volume “Introduction to Data Science” because it led readers through a dip-your-toes-in-the-water
experience. Readers took a brief tour through the various elements in this diverse field and got a feel for what it was all about. In Foundations, which is the next level, the reader's growing knowledge is further developed to a firm base on which to build everything else.
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.
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.
Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”
Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
How can we effectively and efficiently teach data science to students with little to no background in computing and statistical thinking? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more? This introductory data science course is our (working) answer to this question.
The source code for everything you see here can be found on GitHub.
The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results. Time permitting, the course also introduces additional concepts and tools like interactive visualization and reporting, text analysis, and Bayesian inference. A heavy emphasis is placed on a consistent syntax (with tools from the tidyverse), reproducibility (with R Markdown), and version control and collaboration (with Git and GitHub). In addition, out-of-class learning is supplemented with interactive tutorials. The goal of the course is to bring students from zero to being able to work in a team on a fully reproducible data science project analysing a dataset of their choice and answering questions they care about.
Data Science in a Box contains the materials required to teach (or learn from) the course described above, all of which are freely-available and open-source. They include course materials such as slide decks, lecture and live coding videos, homework assignments, guided labs, sample exams, a final project assignment, as well as materials for instructors such as pedagogical tips, information on computing infrastructure, technology stack, and course logistics.
Majority of the materials linked live in the GitHub repo serving this website.
Please note that Data Science in a Box uses a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
""Life is made up of a never-ending sequence of decisions. Many decisions – such as what to
watch on television or what to eat for breakfast – are rather unimportant. Other decisions –
such as what career to pursue, whether or not to invest all of one’s savings in the purchase
of a house – can have a major impact on one’s life. This book is concerned with Decision
Making, which the Oxford Dictionary defines as “the process of deciding about something
important”. We will not attempt to address the issue of what decisions are to be considered
“important”. After all, what one person might consider an unimportant decision may be
viewed by another individual as very important. What we are interested in is the process of
making decisions and what it means to be a “rational” decision maker""--Introductory paragraph.
Find 32 Youtube videos of lectures based on the Decision Making book through this link: http://faculty.econ.ucdavis.edu/faculty/bonanno/DM_Book.html.
Copyright information: You are free to redistribute this book in pdf format. If you make use of any part of this book you must give appropriate credit to the author. You may not remix, transform, or build upon the material without permission from the author. You may not use the material for commercial purposes.
Differentiating open access and open educational resource can be a challenge in some contexts. Excellent resources such as "How Open Is It?: A Guide for Evaluating the Openness of Journals" (CC BY) https://sparcopen.org/our-work/howopenisit created by SPARC, PLOS, and OASPA greatly aid us in understanding the relative openness of journals. However, visual resources to conceptually differentiate open educational resources (OER) from resources disseminated using an open access approach do not currently exist. Until now.
This one page introductory guide differentiates OER and OA materials on the basis of purpose (teaching vs. research), method of access (analog and digital), and in terms of the relative freedoms offered by different levels of Creative Commons licenses, the most common open license. Many other open licenses, including open software licenses also exist.
This is a collection of all materials used in Health Information Technology by Dr. Chi Zhang at Kennesaw State University, including lecture slides, assignments, and assessments, including a question bank.
Topics covered include:
Clinical Financial Records
Patient Bedside Systems
Health Information Networks
HIPAA Privacy and Security
This collection brings together scholarship and pedagogy from multiple perspectives and disciplines, offering nuanced and complex perspectives on Information Literacy in the second decade of the 21st century. Taking as a starting point the concerns that prompted the Association of Research Libraries (ACRL) to review the Information Literacy Standards for Higher Education and develop the Framework for Information Literacy for Higher Education (2015), the chapters in this collection consider six frameworks that place students in the role of both consumer and producer of information within today's collaborative information environments. Contributors respond directly or indirectly to the work of the ACRL, providing a bridge between past/current knowledge and the future and advancing the notion that faculty, librarians, administrators, and external stakeholders share responsibility and accountability for the teaching, learning, and research of Information Literacy.
Good researchers have a host of tools at their disposal that make navigating today’s complex information ecosystem much more manageable. Gaining the knowledge, abilities, and self-reflection necessary to be a good researcher helps not only in academic settings, but is invaluable in any career, and throughout one’s life. The Information Literacy User’s Guide will start you on this route to success.The Information Literacy User’s Guide is based on two current models in information literacy: The 2011 version of The Seven Pillars Model, developed by the Society of College, National and University Libraries in the United Kingdom and the conception of information literacy as a metaliteracy, a model developed by one of this book’s authors in conjunction with Thomas Mackey, Dean of the Center for Distance Learning at SUNY Empire State College. These core foundations ensure that the material will be relevant to today’s students.The Information Literacy User’s Guide introduces students to critical concepts of information literacy as defined for the information-infused and technology-rich environment in which they find themselves. This book helps students examine their roles as information creators and sharers and enables them to more effectively deploy related skills. This textbook includes relatable case studies and scenarios, many hands-on exercises, and interactive quizzes.
This book is written as an introductory text, meant for those with little or no experience with computers or information systems. While sometimes the descriptions can get a little bit technical, every effort has been made to convey the information essential to understanding a topic while not getting bogged down in detailed terminology or esoteric discussions.
Library and Information Science (LIS) is the academic and professional study of how information and information carriers are produced, disseminated, discovered, evaluated, selected, acquired, used, organized, maintained, and managed. This book intends to introduce the reader to fundamental concerns and emerging conversations in the field of library and information science.
A secondary goal of this book is to introduce readers to prominent writers, articles, and books within the field of library science. The book originated as a collection of annotations of important LIS articles. Though these citations are being developed into a fuller text, we hope that this book remains firmly rooted in the literature of LIS and related fields, and helps direct readers toward important resources when a particular topic strikes their fancy.
Part 1: Introduction to data. Data structures, variables, summaries, graphics, and basic data collection and study design techniques.
Part 2: Exploratory data analysis. Data visualization and summarization, with particular emphasis on multivariable relationships.
Part 3: Regression modeling. Modeling numerical and categorical outcomes with linear and logistic regression and using model results to describe relationships and make predictions.
Part 4: Foundations for inference. Case studies are used to introduce the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
Part 5: Statistical inference. Further details of statistical inference using randomization tests, bootstrap intervals, and mathematical models for numerical and categorical data.
Part 6: Inferential modeling. Extending inference techniques presented thus-far to linear and logistic regression settings and evaluating model performance.
Each part contains multiple chapters and ends with a case study. Building on the content covered in the part, the case study uses the tools and techniques to present a high-level overview.
You will learn how scholarly information is produced, organized, and accessed; how to construct and use effective search strategies in a variety of web tools and scholarly databases; how to choose finding tools appropriate to the type of information you need; critical thinking skills in the evaluation of resources; and best practices in the ethical use of information.
The Little Book of Semaphores is a free (in both senses of the word) textbook that introduces the principles of synchronization for concurrent programming.In most computer science curricula, synchronization is a module in an Operating Systems class. OS textbooks present a standard set of problems with a standard set of solutions, but most students don't get a good understanding of the material or the ability to solve similar problems.The approach of this book is to identify patterns that are useful for a variety of synchronization problems and then show how they can be assembled into solutions. After each problem, the book offers a hint before showing a solution, giving students a better chance of discovering solutions on their own.The book covers the classical problems, including "Readers-writers," "Producer-consumer", and "Dining Philosophers." In addition, it collects a number of not-so-classical problems, some written by the author and some by other teachers and textbook writers. Readers are invited to create and submit new problems.
Most books that use MATLAB are aimed at readers who know how to program. This book is for people who have never programmed before. As a result, the order of presentation is unusual. The book starts with scalar values and works up to vectors and matrices very gradually. This approach is good for beginning programmers, because it is hard to understand composite objects until you understand basic programming semantics.