Lego Serious Play at CERN, Challenge Based innovation CBi is the latest iteration of an evolving experiment at CERN in Geneva. The CBi acronym stands for “Challenge Based innovation”, and the experiment pulls in students from several countries and multiple disciplines. The Scimpulse Foundation collaborates with CERN since 2013 and in this occasion we facilitate a concept design workshop. It’s a sunny September morning in Mayrin, the outskirts of Geneva, right on the side of the ATLAS experiment building there is a new shell enclosure where a bunch of students practice and learn about innovation. Dr. Marco Manca is the coach of the team and he wants to make sure that they come out of the experience with a new mindset. The challenge is to design something that may enable blind people to perceive the surrounding environment; maybe some type of augmented sensory device. They call themselves the “Heisenberg” team. They fly through the training! what is Vision? To know how we did it, keep on reading … Let me see! Let me see!
Social Relations & Culture | Cycorp Thing is the "universal collection": the collection which, by definition, contains everything there is. Every individual object, every other collection. Everything that is represented in the Knowledge Base ("KB") and everything that could be represented in the KB. Cyc is designed to support representing any imaginable concept in a form that is immediately compatible with all other representations in the KB, and directly usable by computer software. Intangible Things are things that are not physical -- are not made of, or encoded in, matter. Individual is the collection of all things that are not sets or collections. Sets in Cyc, also know as Mathematical Sets, define specific groupings of things. Collections in Cyc are natural kinds or classes, as opposed to mathematical sets; their instances have some common attribute(s). Cyc contains assertions about logical truth that provide the core assumptions required for using Cyc to perform logical inferences. Artifacts are inanimate. Sports.
Knowledge to Wisdom OER Commons Data, Information, Knowledge, & Wisdom by Gene Bellinger, Durval Castro, Anthony Mills There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. Yet as I entered this arena of activity I quickly found there didn't seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding I have been able to piece together regarding data, information, knowledge, and wisdom. I figured to understand one of them I had to understand all of them. According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories: Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. A further elaboration of Ackoff's definitions follows: Data... data is raw. Ex: It is raining. What is it?
DIKW Pyramid The DIKW Pyramid, also known variously as the "DIKW Hierarchy", "Wisdom Hierarchy", the "Knowledge Hierarchy", the "Information Hierarchy", and the "Knowledge Pyramid",[1] refers loosely to a class of models[2] for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom. "Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge".[1] History[edit] "The presentation of the relationships among data, information, knowledge, and sometimes wisdom in a hierarchical arrangement has been part of the language of information science for many years. Data, Information, Knowledge, Wisdom[edit] In the same year as Ackoff presented his address, information scientist Anthony Debons and colleagues introduced an extended hierarchy, with "events", "symbols", and "rules and formulations" tiers ahead of data.[7][16] Data, Information, Knowledge[edit] Description[edit] Data[edit] Structural vs.
The Expert Enough Manifesto The Expert Enough Manifesto is licensed under a Creative Commons License. Feel free to share the work anywhere, please just link back here if you don’t mind. New: order the Expert Enough Manifesto as a poster at CafePress! Perfect for your home, office, studio or wherever you want a reminder about the brilliance inside you. This is what we’re all about. Expert Enough is here to inspire you to learn more, do more, be more. Life is richest when we become good at a lot of different things. If you agree, we’d love to consider you a regular reader. We’ll be sharing tips, how-to articles, interviews with experts, case studies and more on all kinds of topics from psychology to technology, from food to fitness, from inspiration to perspiration. Sign up for email or RSS updates and follow us on Twitter or Facebook. Thanks Holstee for the inspiration. Turning your skills and expertise into a way to support yourself is more doable than you might think. But expertise isn’t an absolute.
Manage Your Data: Data Management: Subject Guides The MIT Libraries supports the MIT community in the management and curation of research data by providing the following services: Data Management Guide This Data Management and Publishing Guide is a practical self-help guide to the management and curation of research data throughout its life cycle. It provides guidance on a range of topics, including: planning for data management, documentation/metadata, file formats, data organization, data security and backup, citing data, data integration, funder requirements, ethical and legal issues, and sharing and archiving data. Assistance with Creating Data Management Plans Many funders, such as the National Science Foundation, have requirements for data sharing and data management plans. Workshops Our workshops teach you how to manage data more efficiently for your own use and help you to effectively share your data with others. Individual Consultation and Collaboration with Researchers Referrals to Related Services Contact Us
How To Learn On Your Own: Make A Personal Scholar Resource Plan One of the most challenging and gratifying parts of learning alone is the opportunity to search for and select your own learning material. Students in traditional classrooms usually don’t get to decide how they are going to master course content. Instructors decide for them in the form of textbook selection, quizzes, tests, group projects, etc. As an independent learner, you can make your study time more effective by using only the learning methods that work for you. A resource plan is a document used to brainstorm the learning material you can use when you begin your studies. This article will show you how to create a resource plan to use in your independent studies. Step 1: Set a Goal The first step to creating a resource plan is to decide on a single goal. Ineffective Goal – Learn HTMLEffective Goal – Create several websites using HTML, referring only minimally to a coding book. Step 2: Collect Materials Books – The written word is still one of the best ways to learn a subject.
Data visualization Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It is not owned by any one field, but rather finds interpretation across many (e.g. it is viewed as a modern branch of descriptive statistics by some, but also as a grounded theory development tool by others). It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".[1] A primary goal of data visualization is to communicate information clearly and efficiently to users via the information graphics selected, such as tables and charts. Effective visualization helps users in analyzing and reasoning about data and evidence. Data visualization is both an art and a science. Overview[edit] Data visualization is one of the steps in analyzing data and presenting it to users. Indeed, Fernanda Viegas and Martin M. Graphics reveal data.
The Problem with the Data-Information-Knowledge-Wisdom Hierarchy - David Weinberger by David Weinberger | 9:00 AM February 2, 2010 The data-information-knowledge-wisdom hierarchy seemed like a really great idea when it was first proposed. But its rapid acceptance was in fact a sign of how worried we were about the real value of the information systems we had built at such great expense. What looks like a logical progression is actually a desperate cry for help. The DIKW hierarchy (as it came to be known) was brought to prominence by Russell Ackoff in his address accepting the presidency of the International Society for General Systems Research in 1989. Where is the Life we have lost in living? Those lines come from the poem “The Rock” by T.S. The DIKW sequence made immediate sense because it extends what every Computer Science 101 class learns: information is a refinement of mere data. But, the info-to-knowledge move is far more problematic than the data-to-info one. So, what is “knowledge” in the DIKW pyramid? And humbug.
Data analysis - Wikipedia Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The process of data analysis[edit] Data cleaning[edit] The need for data cleaning will arise from problems in the way that data is entered and stored. Initial data analysis[edit] Quality of data[edit] Test for common-method variance.
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