VISUALIZING MATHS & PHYSICS D3 Tips and Tricks by Malcolm Maclean D3.js can help you make data beautiful. D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization. Data is the new medium of choice for telling a story or presenting compelling information on the Internet and d3.js is an extraordinary framework for presentation of data on a web page. Is this book for you? It's not written for experts. It's put together as a guide to get you started if you're unsure what d3.js can do. Why was D3 Tips and Tricks written? Because in the process of learning things, it's a great way to remember them if you write them down :-). As a result, learning how to do cool stuff with D3 meant that I accumulated a sizeable number ways to help me out when the going got tricky. So here we are! What's in the book? But wait! There are over 50 code examples that are used in the book (with their data files) available to download (still free!) The awesome that is Open Source.
Tutorials · mbostock/d3 Wiki Wiki ▸ Tutorials Please feel free to add links to your work!! Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Introductions & Core Concepts Specific Techniques D3 v4 Blogs Books Courses D3.js in Motion (Video Course)Curran Kelleher, Manning Publications, September 2017D3 4.x: Mastering Data Visualization Nick Zhu & Matt Dionis, Packt. Talks and Videos Meetups Research Papers D3: Data-Driven DocumentsMichael Bostock, Vadim Ogievetsky, Jeffrey HeerIEEE Trans.
Max Roser – Economist Text Mining Tool | Theory and Applications Edited by Shigeaki Sakurai, ISBN 978-953-51-0852-8, 226 pages, Publisher: InTech, Chapters published November 21, 2012 under CC BY 3.0 licenseDOI: 10.5772/3115 Edited Volume Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Gallery · mbostock/d3 Wiki Wiki ▸ Gallery Welcome to the D3 gallery! More examples are available for forking on Observable; see D3’s profile and the visualization collection. Visual Index Basic Charts Techniques, Interaction & Animation Maps Statistics Examples Collections The New York Times visualizations Jerome Cukier Jason Davies Jim Vallandingham Institute for Health Metrics and Evaluation Peter Cook Charts and Chart Components Bar Chart Histogram Pareto Chart Line and Area Chart Pie Chart Scatterplot and Bubble chart Parallel Coordinates, Parallel sets and Sankey Sunburst and Partition layout Force Layout Tree Misc Trees and Graphs Chord Layout (Circular Network) Maps Misc Charts Miscellaneous visualizations Charts using the reusable API Useful snippets Tools Interoperability Online Editors Products Store Apps Libraries Games Wish List
Our World in Data — Visualising the Empirical Evidence on how the World is Changing UNSW Learning Analytics & Data Science in Education Research Group December 8, 2015 - 'Research Forward': Exploring practical uses of analytics @ UNSW - L Vigentini (UNSW Australia, Learning & Teaching Unit) November 24, 2015 - Evaluating the student experience in Massive Open Online Courses (MOOCs): methods, problems and insights - C. Zhao, L Vigentini (UNSW Australia, Learning & Teaching Unit) November 10, 2015 - Show me my data! October 27, 2015 - MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroom - Dr Roberto Martinez Maldonado (UTS) & Andrew Clayphan (UNSW) October 13, 2015 - Two short talks: 1) Discrimination-Aware Classifiers for Student Performance Prediction - Ling Luo (University of Sydney) 2) Detecting Students at Risk of Failing - A/Prof Irena Koprinska (University of Sydney) September 29, 2015 - Business Intelligence and Analytics v Learning Analytics – Opportunities for cross-pollination of ideas and practices - A/Prof. August 4, 2015- Learning in the MOOCs and learning from the MOOCs - Dr.
Time Formatting · mbostock/d3 Wiki Wiki ▸ API Reference ▸ Time ▸ Time Formatting D3 includes a helper module for parsing and formatting dates modeled after the venerable strptime and strftime C-library standards. These functions are also notably available in Python's time module. # d3.time.format(specifier) Constructs a new local time formatter using the given specifier. %a - abbreviated weekday name. For %U, all days in a new year preceding the first Sunday are considered to be in week 0. For locale-specific date and time formatters, see locale.timeFormat. The % sign indicating a directive may be immediately followed by a padding modifier: 0 - zero-padding_ - space-padding- - disable padding If no padding modifier is specified, the default is 0 for all directives, except for %e which defaults to _). The returned format is both an object and a function. var format = d3.time.format("%Y-%m-%d"); format.parse("2011-01-01"); // returns a Date format(new Date(2011, 0, 1)); // returns a string # format(date) # format.parse(string)
Northwestern University Center for Interdisciplinary Exploration and Research in Astrophysics - Stellar Evolution The Formation of Nuclear Star Clusters by Fabio Antonini The three simulations correspond to different initial distributions for the cluster orbits. Most galaxies, including the Milky Way, contain massive (10^7 Solar masses) star clusters at their center. Understanding the formation of such nuclear star clusters is important as it could shed light on the processes that have shaped the central regions of galaxies and led to the formation of their central black holes. This visualization shows the (simulated) formation of a compact nuclear star cluster at the center of the dwarf starburst galaxy Henize 2-10. These clusters, the galaxy (Henize 2-10), and the central BH were realized adopting a particle by particle representation and then evolved forward in time with a GPU-based N-body code. Credit: simulations by Arca-Sedda, M., Capuzzo-Dolcetta, Antonini, F. and Seth., A. Download movies: S1, S2, S3 The Late Evolution of Our Solar System by Aaron Geller Download movie Download movie
DATORN i UTBILDNINGEN Text:Jan Hylén E-Post: jan@janhylen.se Ny trend: Skolutveckling med egna frågor och dataanalys Hur kan skolans personal själv använda data på ett strukturerat sätt för att åstadkomma skolutveckling? Det är en trend som är i stark tillväxt. Men kan denna trend inrymmas inom modetermen Learning Analytics, eller är den lärardrivna analysen något annat? Nacka och Stockholm har utforskat en metod för datastödd skolutveckling. Modellen har två grundpelare. Exemplet från Stockholm och Nacka kan ses som en jordnära och konkret tillämpning av begreppet Learning Analytics, vilket förekommer allt oftare. Fenomen på frammarsch Oavsett benämning så räknar många experter med att fenomenet snart kommer att prägla utbildningsväsendet. Engelsmännen är inte lika övertygade om att det kommer att få en så stor påverkan på utbildningssektorn som den amerikanska Horizon-rapporten antar. Individnivån Många kommuner och regioner har system för central antagning till gymnasiet. Datorn i Utbildningen nr 3-2014.
VisIt About VisIt VisIt is an Open Source, interactive, scalable, visualization, animation and analysis tool. From Unix, Windows or Mac workstations, users can interactively visualize and analyze data ranging in scale from small (<101 core) desktop-sized projects to large (>105 core) leadership-class computing facility simulation campaigns. Users can quickly generate visualizations, animate them through time, manipulate them with a variety of operators and mathematical expressions, and save the resulting images and animations for presentations. What's New VisIt is a distributed, parallel visualization and graphical analysis tool for data defined on two- and three-dimensional (2D and 3D) meshes. History VisIt was originally developed by the Department of Energy (DOE) Advanced Simulation and Computing Initiative (ASCI) to visualize and analyze the results of terascale simulations. For any additional questions, send e-mail to VisIt Users.
Signals: Applying Academic Analytics Key Takeaways Applying the principles of business intelligence analytics to academia promises to improve student success, retention, and graduation rates and demonstrate institutional accountability. The Signals project at Purdue University has delivered early successes in academic analytics, prompting additional projects and new strategies. Significant challenges remain before the predictive nature of academic analytics meets its full potential. Academic analytics helps address the public’s desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Student success algorithms customized by course Intervention messages sent to students New strategies for identifying students at risk Today, more than 11,000 students have been impacted by the Signals project, and more than 50 instructors have used Signals in at least one of their courses.