This record includes training materials associated with 'Network Know-how and Data Handling' workshops offered by Australia's Academic Research Network (AARNet). The workshops are delivered to library and eResearch staff at universities, as well as researcher communities, as train-the-trainer events, part of a broader infrastructure literacy strategy.

This workshop is a ‘train-the-trainer’ session that covers topics such as jargon busting, network literacy and data movement solutions. The workshop will also provide a peek at some collaborative research tools such as Jupyter Notebooks and CloudStor. You will learn about networks, integrated tools, data and storage and where all these things fit in the researcher’s toolkit.

This workshop is targeted at staff who would like to be more confident in giving advice to researchers about the options available to them. It is especially tailored for those with little to no technical knowledge and includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming.

Derek Mueller advocates for a methodology to visualize and understand disciplinarity through what he calls network sense. Mueller’s methodology combines distant reading with thin description in a way that allows academics to avoid the obsessive depth of thick description. Distant reading and thin description complement networks of association in a way that affords inquiry and discovery for newcomers and seasoned scholars alike. Using word clouds, citation frequency graphs, and maps of scholarly activity as visual models, he presents ways we can visualize the field of rhetoric and composition/writing studies and its so-called turns, or widespread attention events, such as the global turn, visual turn, multimodal turn, and so on. This book is published by the WAC Clearinghouse/Colorado State University Open Press #writing book series and co-presented by the Digital Publishing Institute at WVU Libraries.

This unit covers the history and evolution of computer networks, including the various types of network communications. Various forms of networking addressing are also covered, including network topologies, standards and protocols, logical model concepts, network hardware, and wireless communication.

This course will highlight common principles that permeate the functioning of networks and how the same issues related to robustness, fragility and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical systems, random graph models, optimization and game theory, and cover a wide variety of applications.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course in organizational economics prepares doctoral students for further study in the field. The course introduces the classic papers and some recent research. The material is organized into the following modules: boundaries of the firm, employment in organizations, decision-making in organizations, and structures and processes in organizations. Each class session covers a few leading papers.
This course was joint-taught between faculty of the Massachusetts Institute of Technology and Harvard University. The Harvard course is Economics 2670 Organizational Economics.

Organizational Processes enhances students’ ability to take effective action in complex organizational settings by providing the analytic tools needed to analyze, manage, and lead the organizations of the future. Emphasis is placed on the importance of the organizational context in influencing which individual styles and skills are effective. The subject centers on three complementary perspectives, or “lenses”, on an organization: political, cultural, and strategic design. Students enrolled in this class are also jointly enrolled in 15.328, Team Project, in order to complete a field study of an organizational change initiative. Organizational Processes also operates in conjunction with 15.280, Communication for Managers, by sharing certain assignments and holding some joint classes.

The course purpose is to provide the substance and skill necessary to make sound business decisions relating to information systems and to work with senior line managers in the resolution of issues and problems in this area. Categories of issues which will be addressed in the course include:

How do IT and its various manifestations in business, such as the Internet, affect current and future COMPETITIVENESS? How do we align business strategy and plans with IT strategy and IT plans?
How can we ENGAGE executives in learning and leading IT-related change?
How do we IMPLEMENT new systems, CHANGE work behavior, MANAGE projects?
How should we ORGANIZE and GOVERN IT in an organization?

Building on their understanding of graphs, students are introduced to random processes on networks. They walk through an illustrative example to see how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students. This demonstrates how scientists and engineers use mathematics to model and simulate random processes on complex networks. Topics covered include random processes and modeling disease spread, specifically the SIR (susceptible, infectious, resistant) model.

This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation.

This class deals with the modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. The course will cover state of the art results which lead to research opportunities.

This Module is designed for school nurses. It provides an overview of school nurses' roles in serving all students in the school environment, in addition to addressing specific roles for working with students with disabilities. More specifically, it discusses participating in 504 plan and IEP meetings, advocating for students with healthcare needs, promoting their services and their roles as school nurses, collaborating with others, and establishing networks (est. completion time: 1.5 hours).

To get a better understanding of complex networks, students create their own, real social network example by interacting with their peers in the classroom and documenting the interactions. They represent the interaction data as a graph, calculate two mathematical quantities associated with the graph—the degree of each node and the degree distribution of the graph—and analyze how these quantities can be used to infer properties of the social network at hand.

This is an advanced course in game theory. We begin with a rigorous overview of the main equilibrium concepts for non-­cooperative games in both static and dynamic settings with either complete or incomplete information. We define and explore properties of iterated strict dominance, rationalizability, Nash equilibrium, subgame perfection, sequential, perfect and proper equilibria, the intuitive criterion, and iterated weak dominance. We discuss applications to auctions, bargaining, and repeated games. Then we introduce solution concepts for cooperative games and study non-­cooperative implementations. Other topics include matching theory and networks.

Design, operation, and management of traffic flows over complex transportation networks are the foci of this course. It covers two major topics: traffic flow modeling and traffic flow operations. Sub-topics include deterministic and probabilistic models, elements of queuing theory, and traffic assignment. Concepts are illustrated through various applications and case studies. This is a half-term subject offered during the second half of the semester.

This collection uses primary sources to explore the Underground Railroad and the Fugitive Slave Act of 1850. Digital Public Library of America Primary Source Sets are designed to help students develop their critical thinking skills and draw diverse material from libraries, archives, and museums across the United States. Each set includes an overview, ten to fifteen primary sources, links to related resources, and a teaching guide. These sets were created and reviewed by the teachers on the DPLA's Education Advisory Committee.

Students analyze dramatic works using graph theory. They gather data, record it in Microsoft Excel and use Cytoscape (a free, downloadable application) to generate graphs that visually illustrate the key characters (nodes) and connections between them (edges). The nodes in the Cytoscape graphs are color-coded and sized according to the importance of the node (in this activity nodes represent characters in the work and their relative importance to the story). After the analysis, the graphs are further examined to see what the visual depiction of the story in the form of a graph tells readers about the inner workings of the dramatic work. Students gain practice with graph theory vocabulary, including node, edge, betweeness centrality and degree on interaction, and learn about a range of engineering applications of graph theory.

This case study is retrieved from the open book Open Data as Open Educational Resources. Case studies of emerging practice.

It explores why and how open data can be used as a material with which to produce engaging challenges for students as they are introduced to programming. Through describing the process of producing the assignments, and learner responses to them, we suggest that open data is a powerful material for designing learning activities because of its qualities of ease of access and authenticity.

In two successive years, forms of open data were used to construct coursework assignments for postgraduate students at the University of Nottingham, UK. The rationale for using open data was to shift the focus towards an outward-looking approach to coding with networks, files and data structures, and to engage students in constructing applications that had real-world relevance.

Python was chosen as the programming language.

The assignment in the first year utilised e-book text files from Project Gutenberg1, and required students to build an e-reader application. In the next year, car park status data, which was made available in a regularly updated form by the city council through their open data initiative2 was used as the basis for an assignment in which students developed a city-wide car park monitoring application.

What is the internet? Short answer: a distributed packet-switched network. This is the introduction video to the series, "How the Internet Works". Vint Cerf, one of the "fathers of the internet" explains the history of the net and how no one person or organization is really in charge of it.

If companies always try to maximize profits, why are so many social media sites free to use? Learn how these platforms operate, as well as what positive and negative “network effects” are at play, in this May 2023 issue of Page One Economics.