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Data and Models in Action by A. Stein,F.W. Penning de Vries Pdf
Under leadership of CT de Wit a large amount of modeling, building prototypes and also application, was carried out in the 1970s and 1980s. Comprehensive models were built, evaluated and carefully documented in the areas of crop growth production, plant breeding, soil water and nutrients, and in crop protection. Simulation techniques and biophysical theories developed in parallel. Simulation and experimentation always went hand in hand. Much of this work is documented in a long series of PhD theses under supervision of De Wit, in the series of Simulation Monographs (PUDOC), and in numerous other publications. This work has inspired many scientists across the global science community. The CT de Wit Graduate School of Production Ecology (PE) of the Wageningen University builds further on this platform and finds new subjects for research on and with models, and data. The PE platform provides also an excellent opportunity to develop contacts, cooperation and joint software with research groups in related fields and abroad. This book precipitates from such an exploration in new directions. We realize that modem information systems and statistics can offer a substantial contribution to the modelling framework. Good examples can be found here, and these provide a clear direction for the years to come.
R for Data Science by Hadley Wickham,Garrett Grolemund Pdf
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Action Research by Jerry W. Willis,Claudia Edwards Pdf
The first three chapters of Action Research: Models, Methods, and Examples covers the history, foundations, and basics of conducting action research projects. In those chapters you will learn about the origins of action research as well as about the different methods and models of action research - from the original approaches used by Kurt Lewin and his students in the 1940s and 1950s to the diversity of current approaches to AR that are used to develop both solutions to real world problems and to construct a better understanding of important issues and concern in communities, schools, businesses, and organizations. The nine chapters in the second part of the book illustrate the many ways action research is practiced today. Those chapters illustrate the use of action research methods to accomplish everything from individual personal professional development to changing policies and practices in large organizations and systems. The chapters detail many specific methods of doing AR such as participatory action research, emancipatory action research, designbased action research, collaborative inquiry, and many others.
How Teachers Can Turn Data into Action by Daniel R. Venables Pdf
" From state and Common Core tests to formative and summative assessments in the classroom, teachers are awash in data. Reviewing the data can be time-consuming, and the work of translating data into real change can seem overwhelming. Tapping more than 30 years' experience as an award-winning teacher and a trainer of PLC coaches, Daniel R. Venables, author of The Practice of Authentic PLCs: A Guide to Effective Teacher Teams, soothes the trepidation of even the biggest ""dataphobes"" in this essential resource. Field-tested and fine-tuned with professional learning communities around the United States, the Data Action Model is a teacher-friendly, systematic process for reviewing and responding to data in cycles of two to nine weeks. This powerful tool enables you and your teacher team to * Identify critical gaps in learning and corresponding instructional gaps; * Collaborate on solutions and develop a goal-driven action plan; and * Evaluate the plan's effectiveness after implementation and determine the next course of action. With easy-to-use templates and protocols to focus and deepen data conversations, this indispensable guide delineates exactly what should be accomplished in each team meeting to translate data into practice. In the modern sea of data, this book is your life preserver! "
Bayesian Models of Perception and Action by Wei Ji Ma,Konrad Paul Kording,Daniel Goldreich Pdf
An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain
A person's biological, psychological and social systems are the basis of Orem's self-care model of nursing. Self-care means the activities to maintain life, health and well-being which individuals perform. The model emphasises personal responsibility, holistic approaches and state of health and well-being rather than ill-health.
Neural Network Models of Conditioning and Action by Michael L. Commons,Stephen Grossberg,John Staddon Pdf
Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
Encyclopedia of Big Data by Laurie A. Schintler,Connie L. McNeely Pdf
This encyclopedia will be an essential resource for our times, reflecting the fact that we currently are living in an expanding data-driven world. Technological advancements and other related trends are contributing to the production of an astoundingly large and exponentially increasing collection of data and information, referred to in popular vernacular as “Big Data.” Social media and crowdsourcing platforms and various applications ― “apps” ― are producing reams of information from the instantaneous transactions and input of millions and millions of people around the globe. The Internet-of-Things (IoT), which is expected to comprise tens of billions of objects by the end of this decade, is actively sensing real-time intelligence on nearly every aspect of our lives and environment. The Global Positioning System (GPS) and other location-aware technologies are producing data that is specific down to particular latitude and longitude coordinates and seconds of the day. Large-scale instruments, such as the Large Hadron Collider (LHC), are collecting massive amounts of data on our planet and even distant corners of the visible universe. Digitization is being used to convert large collections of documents from print to digital format, giving rise to large archives of unstructured data. Innovations in technology, in the areas of Cloud and molecular computing, Artificial Intelligence/Machine Learning, and Natural Language Processing (NLP), to name only a few, also are greatly expanding our capacity to store, manage, and process Big Data. In this context, the Encyclopedia of Big Data is being offered in recognition of a world that is rapidly moving from gigabytes to terabytes to petabytes and beyond. While indeed large data sets have long been around and in use in a variety of fields, the era of Big Data in which we now live departs from the past in a number of key respects and with this departure comes a fresh set of challenges and opportunities that cut across and affect multiple sectors and disciplines, and the public at large. With expanded analytical capacities at hand, Big Data is now being used for scientific inquiry and experimentation in nearly every (if not all) disciplines, from the social sciences to the humanities to the natural sciences, and more. Moreover, the use of Big Data has been well established beyond the Ivory Tower. In today’s economy, businesses simply cannot be competitive without engaging Big Data in one way or another in support of operations, management, planning, or simply basic hiring decisions. In all levels of government, Big Data is being used to engage citizens and to guide policy making in pursuit of the interests of the public and society in general. Moreover, the changing nature of Big Data also raises new issues and concerns related to, for example, privacy, liability, security, access, and even the veracity of the data itself. Given the complex issues attending Big Data, there is a real need for a reference book that covers the subject from a multi-disciplinary, cross-sectoral, comprehensive, and international perspective. The Encyclopedia of Big Data will address this need and will be the first of such reference books to do so. Featuring some 500 entries, from "Access" to "Zillow," the Encyclopedia will serve as a fundamental resource for researchers and students, for decision makers and leaders, and for business analysts and purveyors. Developed for those in academia, industry, and government, and others with a general interest in Big Data, the encyclopedia will be aimed especially at those involved in its collection, analysis, and use. Ultimately, the Encyclopedia of Big Data will provide a common platform and language covering the breadth and depth of the topic for different segments, sectors, and disciplines.
Data Model Patterns: A Metadata Map by David C. Hay Pdf
Data Model Patterns: A Metadata Map not only presents a conceptual model of a metadata repository but also demonstrates a true enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that different readers can benefit from different parts. It offers a view of the world being addressed by all the techniques, methods, and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) and presents several concepts that need to be addressed by such tools. This book is pertinent, with companies and government agencies realizing that the data they use represent a significant corporate resource recognize the need to integrate data that has traditionally only been available from disparate sources. An important component of this integration is management of the "metadata" that describe, catalogue, and provide access to the various forms of underlying business data. The "metadata repository" is essential to keep track of the various physical components of these systems and their semantics. The book is ideal for data management professionals, data modeling and design professionals, and data warehouse and database repository designers. A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation) Provides a step-by-step description of model and is organized so that different readers can benefit from different parts Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) Presents many concepts that are not currently being addressed by such tools — and should be
Action Patterns in Business Process Models by Sergey Smirnov,Weidlich, Matthias,Mendling, Jan ,Weske, Mathias Pdf
Business process management experiences a large uptake by the industry, and process models play an important role in the analysis and improvement of processes. While an increasing number of staff becomes involved in actual modeling practice, it is crucial to assure model quality and homogeneity along with providing suitable aids for creating models. In this paper we consider the problem of offering recommendations to the user during the act of modeling. Our key contribution is a concept for defining and identifying so-called action patterns - chunks of actions often appearing together in business processes. In particular, we specify action patterns and demonstrate how they can be identified from existing process model repositories using association rule mining techniques. Action patterns can then be used to suggest additional actions for a process model. Our approach is challenged by applying it to the collection of process models from the SAP Reference Model.
Statistics in Action teaches a modern approach to statistics that uses graphing calculators or statistical software and allows readers to place more emphasis on statistical concepts and data analysis than on following recipes for calculations. The text is intended to be used as much as a dialog as a reference; readers will have to be thoughtful as they paruse the text. An activity introduces each major topic, allowing readers to experience the major concepts of statistical thinking before going on to confirm them through further analysis (often involving simulation) and practice with data. Statistics in Action gives the design and analysis of both experiments and surveys an honest treatment, rather than forcing them into overly simplistic models.
