The goal of this resource is to support scholarly communication librarians wanting to implement accessibility measures in their open access, open education, and open data initiatives.

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).

This text introduces copyright, publishing formats, note-taking formats, citation styles, source evaluation, library organization, library resources and services, and effective search practices using online databases and Internet search engines.

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.

For those learning about fair use, this is a specific example of how fair use may be used in research for text data mining. The book also explores basic copyright and fair use more generally, as well as the specifics of text data mining.
From the "about" section of the book:

"This book explores the legal literacies covered during the virtual Building Legal Literacies for Text Data Mining Institute, including copyright (both U.S. and international law), technological protection measures, privacy, and ethical considerations. It describes in detail how we developed and delivered the 4-day institute, and also provides ideas for hosting shorter literacy teaching sessions. Finally, we offer reflections and take-aways on the Institute."

Diffusion of Innovations theory (Rogers, 2003) states that potential adopters moving through the innovation-decision process consider the innovation’s relative advantage, compatibility, complexity, trialability and observability. Rogers (2003) claimed that an individual's perception of these characteristics, or attributes, had a direct impact on whether and to what degree an innovation is adopted and diffused throughout an organization or system. The purpose of this presentation is to share, through the lens of Diffusion of Innovations theory, how members of the Online Consortium of Oklahoma supported the adoption and diffusion of the use of an online publishing platform intended to support consortial-wide adoption, modification, and creation of OER. The 25 member institutions of Online Consortium of Oklahoma (OCO) include two-year institutions, four-year institutions, technical institutes, and doctoral degree granting institutions. As a result, OCO’s strategic vision takes into consideration a broad range of needs, interests, and goals. As members of the OER subcommittee envisioned how to promote use of the publishing platform to each of their institutions, it became apparent that one simple on-boarding process for use across all institutions was ineffective. Faculty introduced to the platform would express interest but those who took additional steps to learn more about its use seemed to be those who had the opportunity to observe use of the platform by a respected peer or colleague with whom they were personally acquainted. Additionally, the group noticed faculty initially resistant to using the platform became enthusiastic about its use once given access to its full capacity. In this lightning talk, representatives from OCO member institutions will describe how they intentionally addressed observability and trialability to support the adoption and diffusion of the publishing platform and statewide use of OER.

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
Additional Resources
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.

The book offers a blend of theory and practice in guiding readers to apply design thinking principles to solving some of our world’s biggest problems. At the same time, readers are encouraged to become aware of new and emerging technologies that make prototyping and applying solutions a reality.

Le but de ce manuel est de vous donner les connaissances de base nécessaires pour commencer à rechercher des informations en utilisant le catalogue et les bases de données de la bibliothèque de l’Université d’Alberta. Le manuel contient des instructions étape par étape, des vidéos et des exemples en sciences humaines et sociales. Pour atteindre les objectifs prévus, il vous faudra passer au moins une heure et demie à lire les instructions du didacticiel, répondre aux questions des exercices et revoir les exemples chaque fois que cela sera nécessaire.

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.

An instructional text that seeks to untangle the social complexities and ethical dilemmas of online data and information. DIGITAL CITIZENSHIP will educate readers on the economics of the Internet and the means by which political bad actors exploit its platforms to pervert the public discourse.

The information in this short digest offers ready-to-go, step-by-step activities to complete relevant assessments that build students’ awareness of these key concepts and challenges them to build on their developing skills in the areas of research, writing, and digital citizenship. Faculty are encouraged to adopt the activities as is; adapt them to their own learning environments; and | or extend them to capture more skill development.

Digitizing rare and unique historical documents so they can be shared online is mission-critical work for most cultural heritage institutions, but it can be difficult to complete this work, especially intellectual property rights management, at a scale that matches user demand. The authors of this open educational resource offer guidance for creating scalable, cross-functional workflows using a risk-management approach that increases efficiency and distributes responsibility for rights assessment work more equitably across stakeholders. It includes advice for navigating knowledge gaps, building an engaged team with the right skillsets, reimagining workflows, and rethinking traditional archival processing workflows to build capacity for rights analysis during arrangement and description. Each chapter includes a helpful exercise for implementing this guidance in your own institution.