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Organizing, understanding, analysis, & publishing

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What are some approaches to taking in information, looking at it for understanding, figuring out some bits / nuggets of additional info, and then sharing (publishing) back to an audience? This is a collection to help understand this topic.

The great British Brexit robbery: how our democracy was hijacked. How the Trump-Russia Data Machine Games Google to Fool Americans. A year ago I was part of a digital marketing team at a tech company.

How the Trump-Russia Data Machine Games Google to Fool Americans

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About is easy for writers to get started with, is completely customizable by designers, and can be extended by programmers through easy APIs and full access to the server code. Create a new post in two steps. Enter text into the box, then click the Post button. New DataBasic Tool Lets You “Connect the Dots” in Data. Catherine D'Ignazio and I have launched a new DataBasic tool and activity, Connect the Dots, aimed at helping students and educators see how their data is connected with a visual network diagram.

New DataBasic Tool Lets You “Connect the Dots” in Data

By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach to understanding it. The new tool gives users a network diagram to reveal links as well as a high level report about what the network looks like. NodeXL Graph Gallery: Graph Details. The graph represents a network of 1,613 Twitter users whose recent tweets contained "#agchat", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets.

NodeXL Graph Gallery: Graph Details

The network was obtained from Twitter on Tuesday, 13 December 2016 at 16:56 UTC. The tweets in the network were tweeted over the 9-day, 18-hour, 37-minute period from Saturday, 03 December 2016 at 22:04 UTC to Tuesday, 13 December 2016 at 16:42 UTC. Pattern recognition - Wikipedia.

Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation.

Pattern recognition - Wikipedia

This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms. Overview[edit] What is Pattern Analysis? PATN is a software package that performs Pattern Analysis.

What is Pattern Analysis?

PATN aims to try and display patterns in complex data. Complex in PATN's terms, means that you have at least 6 objects that you want to know something about and a suite of more than 4 variables that describe those objects. Data must be in the form of a spreadsheet of rows (the objects in PATN) and the columns (variables), as in Microsoft Excel™. SAVI. Spreadsheets Are Graphs Too! - Neo4j Graph Database. By Felienne Hermans, Assistant Professor, Delft University of Technology | August 26, 2015 Editor’s Note: Last May at GraphConnect Europe, Felienne Hermans – Assistant Professor at Delft University of Technology – gave this engaging talk on why you shouldn’t overlook the power of the humble spreadsheet.

Spreadsheets Are Graphs Too! - Neo4j Graph Database

Listen to or read her presentation below. Register for GraphConnect San Francisco to hear more speakers like Felienne present on the emerging world of graph database technologies. Tutorial 1: Introducing Graph Data. Next: Introducing RDF The semantic web can seem unfamiliar and daunting territory at first.

Tutorial 1: Introducing Graph Data

If you're eager to understand what the semantic web is and how it works, you must first understand how it stores data. We start from the ground up by outlining the graph database - the data storage model used by the semantic web. SKOS Simple Knowledge Organization System - home page. What is an ontology and why we need it. Figure 8.

What is an ontology and why we need it

Hierarchy of wine regions. The "A" icons next to class names indicate that the classes are abstract and cannot have any direct instances. The same class hierarchy would be incorrect if we omitted the word “region” from the class names. MooWheel: a javascript connections visualization library. View the project on Google Code 06.29.2008 version 0.2 now available!

MooWheel: a javascript connections visualization library

Get it. What's new? Looking for version 0.1 instead? SPARQL in 11 minutes. OntoWiki — Agile Knowledge Engineering and Semantic Web. Swj210 1. 20 Big Data Repositories You Should Check Out. DBpedia. Welcome - the Datahub. Tutorial 4: Introducing RDFS & OWL. Next: Querying Semantic Data Having introduced the advantages of modeling vocabulary and semantics in data models, let's introduce the actual technology used to attribute RDF data models with semantics. RDF data can be encoded with semantic metadata using two syntaxes: RDFS and OWL. After this tutorial, you should be able to: Understand how RDF data models are semantically encoded using RDFS and OWLUnderstand that OWL ontologies are RDF documentsUnderstand OWL classes, subclasses and individualsUnderstand OWL propertiesBuild your own basic ontology, step by stepEstimated time: 5 minutes.

How to organize your EndNote library. Knowledge management. Knowledge management (KM) is the process of capturing, developing, sharing, and effectively using organizational knowledge.[1] It refers to a multi-disciplinary approach to achieving organizational objectives by making the best use of knowledge.[2] An established discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, library, and information sciences.[3][4] Other fields may contribute to KM research, including information and media, computer science, public health, and public policy.[5] Several Universities offer dedicated Master of Science degrees in Knowledge Management.

Many large companies, public institutions, and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments.[6] Several consulting companies provide advice regarding KM to these organisations.[6] History[edit] Research[edit] Knowledge base. A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems which were the first knowledge-based systems.