Tools - Cool Infographics Adioma creates information graphics out of your textual data, using timelines, grids and icons. Create impressive charts from spreadsheets. Assemble into dashboards, embed in websites, or simply share a link. What is Pattern Analysis? PATN is a software package that performs 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™. There are usually around 7 components to a 'realistic' (read as adequate, comprehensive, fair, reasonable or intelligent) pattern analysis in PATN- Import the data Check the data using PATN's Visible Statistics functions. PATN is setup to make it easy for you to follow this process.
Visual Literacy: An E-Learning Tutorial on Visualization for Communication, Engineering and Business Visual Cards for Collaboration and Team Creativity Making the Complex Clear Visual Literacy for Managers - How Sketching enables Visual Problem Solving and Communication (get the hardcopy edition at sketchingatwork.com) By clicking on a map or diagram thumbnail below, you can access an interactive graphic overview on tools, books, researchers in different visualization fields, as well as on key success factors of visualization. There is also an interactive organizing table that shows (incl. examples) one hundred visualization-based methods. Stairs to visual excellence "Towards A Periodic Table of Visualization Methods for Management"Lengler R., Eppler M. (2007). Version 1.5 of the periodic table as PDF Chris Wallace has implemented an XML page on which you can see and print the mouseover pictures individually. August 21st 2008 marks the 61st birthday of the great Ben Shneiderman, for many the father of information visualization. Imperfect Storm (Click on image to enlarge)
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. 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. The graph is directed. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm. Top Word Pairs in Tweet in Entire Graph:[355] agblog,via[199] agchat,conference[151] rt,agchat[121] vancecrowe,agchat[102] kansas,city[99] rt,agchatfound[86] rt,farmfutures[71] agchat,agchat[67] farm,futures[67] via,beef
Wizualizacja sztuki 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. 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. Using network analysis helped Google revolutionize search technology and was used by journalists who investigated the connections between people and banks during the Panama Papers Leak. Learn more about Connect the Dots and all the DataBasic tools here. Have you used DataBasic tools in your classroom, organization, or personal projects?
Big Data: why is metadata more personal than our fingerprints A l’occasion du colloque « la politique des données personnelles : Big Data ou contrôle individuel « organisé par l’Institut des systèmes complexes et l’Ecole normale supérieure de Lyon qui se tenait le 21 novembre dernier, Yves-Alexandre de Montjoye (@yvesalexandre) était venu présenter ses travaux, et à travers lui, ceux du MediaLab sur ce sujet (Cf. « D’autres outils et règles pour mieux contrôler les données » ). Yves-Alexandre de Montjoye est doctorant au MIT. Il travaille au laboratoire de dynamique humaine du Media Lab, aux côtés de Sandy Pentland, dont nous avons plusieurs fois fait part des travaux. Nos données de déplacements sont encore plus personnelles que nos empreintes digitales Faire correspondre des empreintes digitales n’est pas si simple, rappelle Yves-Alexandre de Montjoye. Image : illustration de l’unicité de nos parcours repérés via des antennes mobiles. Et Yves-Alexandre de nous inviter à retrouver un de ses collègues du Media Lab. Hubert Guillaud
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. 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. People often ask me, ‘How is it possible that you research spreadsheets? Did you actually write a dissertation on spreadsheets?’ The answer is, Yes, I did. Ninety-five percent of all U.S. companies still use spreadsheets for financial reporting, so spreadsheets run the financial domain. Analysts decide the strategy of their company based on spreadsheets. Either way, analysts make decisions that steer the company based on the data in their spreadsheets. Spreadsheets often exist under the radar. Spreadsheets Are Code
Tutorial 1: Introducing Graph Data Next: Introducing RDF The semantic web can seem unfamiliar and daunting territory at first. If you're eager to understand what the semantic web is and how it works, you must first understand how it stores data. After this tutorial, you should be able to: Describe in basic terms what the semantic web is.Experience the paradigm-shift of storing information as a graph database, rather than a hierarchical or relational database.Understand that the semantic web of data is defined using Resource Description Framework (RDF).Understand the basic principles of RDF statements and how they can define data graphs. Estimated time: 5 minutes If you come from a traditional IT background and are used to the idea of storing data either in a hierarchy (for example XML) or in a relational database (for example MySQL, MS SQL), you may not yet have come across Resource Description Framework, or RDF. Although it might not be familiar to you, it is the type of database that builds the semantic web, globally. 03.
SKOS Simple Knowledge Organization System - home page SKOS is an area of work developing specifications and standards to support the use of knowledge organization systems (KOS) such as thesauri, classification schemes, subject heading lists and taxonomies within the framework of the Semantic Web ... [read more] Alignment between SKOS and new ISO 25964 thesaurus standard (2012-12-13) ISO 25964-1, published in 2011, replaced the previous thesaurus standards ISO 2788 and ISO 5964 (both now withdrawn). From Chaos, Order: SKOS Recommendation Helps Organize Knowledge (2009-08-18) Today W3C announces a new standard that builds a bridge between the world of knowledge organization systems - including thesauri, classifications, subject headings, taxonomies, and folksonomies - and the linked data community, bringing benefits to both. Call for Review: SKOS Reference Proposed Recommendation (2009-06-15) The Semantic Web Deployment Working Group has published the Proposed Recommendation of SKOS Simple Knowledge Organization System Reference.
What is an ontology and why we need it Figure 8. 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. We cannot say that the class Alsace is a subclass of the class France: Alsace is not a kind of France. However, Alsace region is a kind of a French region. Only classes can be arranged in a hierarchy—knowledge-representation systems do not have a notion of sub-instance. As a final note on defining a class hierarchy, the following set of rules is always helpful in deciding when an ontology definition is complete: The ontology should not contain all the possible information about the domain: you do not need to specialize (or generalize) more than you need for your application (at most one extra level each way). For our wine and food example, we do not need to know what paper is used for the labels or how to cook shrimp dishes. Figure 9.