TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 3: Network Theory and Terminology

In this class, we will discuss network theory fundamentals, including concepts such as diameter, distance, clustering coefficient and others. We will also discuss different types of networks, such as scale-free networks, random networks etc. Readings: Graph structure in the Web, A. Broder and R. Kumar and F. Maghoul and P. Raghavan and S. Rajagopalan and R. Stata and A. Tomkins and J. Wiener Computer Networks 33 309--320 (2000) [Web link, Alternative Link] Optional: The Structure and Function of Complex Networks, M.E.J. Newman, SIAM Review 45 167--256 (2003) [Web link] Original course at: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 13: Web Technologies 2 - The Semantic Web

The semantic web represents a current research effort to increase the capability of machines to make sense of content on the web. In this class, Peter Scheir will give a guest lecture on the basic principles underlying the semantic web vision, including RDF, OWL and other standards.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 4: Social Network Analysis

What are fundamental entities in social networks and what information is contained in social graphs? We will discuss some selected concepts in social network analysis, such as one- and two mode networks, prestige and centrality, and cliques, clans and clubs. Readings: Web tool predicts election results and stock prices, J. Palmer, New Scientist, 07 February (2008) [Protected Access] Optional: Social Network Analysis, Methods and Applications, S. Wasserman and K. Faust (1994)

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 2: Small World Problem

We will discuss several examples and research efforts related to the small world problem and set the ground for our discussion of network theory and social network analysis. Readings: An Experimental Study of the Small World Problem, J. Travers and S. Milgram Sociometry 32 425-443 (1969) [Protected Access] Optional: The Strength of Weak Ties, M.S. Granovetter The American Journal of Sociology 78 1360--1380 (1973) [Protected Access] Optional: Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network, J. Leskovec and E. Horvitz MSR-TR-2006-186. Microsoft Research, June 2007. [Web Link, the most recent and comprehensive study on the subject!] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 8: Web Technologies 1

This class focuses on a selected subset of web technologies that are of interest to the topics of this course. Readings: Chapter 5 "Representational State Transfer (REST)", in "Architectural Styles and the Design of Network-based Software Architecture", Roy Fielding, Dissertation, University of California Irvine, 2000 Optional: Chapter "Representational State Transfer (REST)" in "Pro PHP XML and Web Services", R. Richards 633--672, 2006

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 10: Text Mining

This class introduces basics of web mining and information retrieval including, for example, an introduction to the Vector Space Model and Text Mining. Guest Lecturer: Dr. Michael Granitzer Optional: Modeling the Internet and the Web: Probabilistic Methods and Algorithms, Pierre Baldi, Paolo Frasconi, Padhraic Smyth, Wiley, 2003 (Chapter 4, Text Analysis)

Foundation Year RTS Part 3 Portfolio Template Summary: Science and Engineering 2012-13

Part 3 Routes to Success summary of the portfolio template on one A4 page

Foundation Year RTS3 Portfolio Template: Science and Engineering 2012-13

Part 3 Routes to Success Portfolio template on five A4 page

WAIS Seminar:Modelling the Web: Examples of modelling language, network evolution and physical-social systems

Speaker(s): Prof. Steffen Staab Organiser: Dr Tim Chown Time: 23/05/2014 10:30-11:30 Location: B53/4025 Abstract The Web is constructed based on our experiences in a multitude of modalities: text, networks, images, physical locations are some examples. Understanding the Web requires from us that we can model these modalities as they appear on the Web. In this talk I will show some examples of how we model text, hyperlink networks and physical-social systems in order to improve our understanding and our use of the Web.

Web Science and Data Analytics Course

Resources from the Singapore Summer School 2014 hosted by NUS. ws-summerschool.comp.nus.edu.sg

Corticosterone plasma concentrations in Carioca High- and Low-Conditioned freezing rats after a fear conditioned task

Our group in the Psychology Department at Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) developed a rat genetic model of extreme freezing in response to contextual cues in an experimental chamber previously associated with footshock. One of the lines, Carioca High Freezing (CHF), exhibits an enhanced conditioned freezing response, whereas the other line, Carioca Low Freezing (CLF), shows the opposite response. The present study investigated corticosterone concentration between these two lines of animals and a random (RND) line of rats both under basal conditions and test condition after an emotional challenge using a contextual fear conditioning protocol. Comparisons between basal and test plasma corticosterone concentrations suggested differential basal and fear-induced differences between the two lines. The differences between basal conditions is an important and relevant aspect to be considered in behavioral experiments using or assessing stress and could help to understand variability in naïve populations.

Science and technology.

Es el punto de partida para el aprendizaje de la ciencia con todas las respuestas a las preguntas que un niño puede hacer respecto a él mismo y el mundo que le rodea. Cuenta con actividades que no requieren un equipo especial y recuadros con más información.

Primary science and literacy.

La ciencia y el conocimiento del lenguaje, oral y escrito, es decir, la lectura y la escritura están indisolublemente ligados. La sociedad actual, muy desarrollada, necesita personas científicamente alfabetizadas. Además, la asociación entre ciencia y alfabetización es recíproca: la ciencia ofrece contextos para el uso y desarrollo de las aptitudes de lectura, escritura y comprensión, mientras que la alfabetización contribuye al acceso individual al apasionante y difícil mundo científico.