25 Awesome Beta Research Tools from Libraries Around the World
If you're tired of using the same old search box on your local library website for research projects, it might be time to broaden your horizons. Try out one of these in-the-works betas sponsored by world-class libraries around the world. From academic libraries like that at MIT or renowned research centers like the Library of Congress, the following beta research tools feature innovative tricks to connect you with the most relevant, valid results on the Internet and in their card catalogs. Melvil Dewey would be proud. Tools Used at College and University Libraries Check out this list for academically-minded beta search tools sponsored by universities around the world. Vera Multi-Search: MIT: This new tool is still in the works, but once it's officially approved, students and researchers can use Vera Multi-Search as a way to find material in several different databases with one single search.
Computer-assisted qualitative data analysis software
Definition[edit] CAQDAS is used in psychology, marketing research, ethnography, and other social sciences. The CAQDAS Networking project[1] lists the following tools. A CAQDAS program should have: Content searching toolsCoding toolsLinking toolsMapping or networking toolsQuery toolsWriting and annotation tools Free / open source software for CAQDAS[edit] Proprietary software for CAQDAS[edit] ATLAS.ti (Windows; Mac OS and iPad announced)f4analyse (Windows, Mac OS and Linux)HyperRESEARCH (Windows, Mac OS)MAXQDA (Windows; Mac OS)NVivo (Windows; Mac OS announced for 2014)QDA Miner (Windows)Qiqqa (Windows, Android)XSight (Windows)Coding Analysis Toolkit (CAT)Saturate Web-Based CAQDAS software[edit] Pros and cons[edit] See also[edit] References[edit] Jump up ^ "CAQDAS". External links[edit]
How the CIA define problems & plan solutions: The Phoenix Checklist
In a recent BBH Labs post (Wind Tunnel Marketing, The Sequel: On the Need for Divergent Insight) that talked about the need for divergent thinking and stimulus in approaching problem solving (& creative ideation), Chaz Wigley, the Chairman of BBH in Asia Pacific, mentioned how the CIA‘s (I’ve always wanted to link to the CIA) Problem Definition Checklist provoked precisely this kind of approach; rounded, many-faceted, flexible. These questions are known as “context-free questions” and are designed “to encourage agents to look at a challenge from many different angles. Using Phoenix is like holding your challenge in your hand. You can turn it, look at it from underneath, see it from one view, hold it up to another position, imagine solutions, and really be in control of it” (see the excellent, if chewy, paper on Exploring Exploratory Testing, for more here). My personal favourite question in the problem definition list is the somewhat open-ended: ‘what isn’t the problem?’. Enjoy.
[Coding Analysis Toolkit] Home Page
Self-Improving Bayesian Sentiment Analysis for Twitter
That’s quite the mouthful. Let me start with a huge caveat: I’m not an expert on this, and much of it may be incorrect. I studied Bayesian statistics about fifteen years ago in university, but have no recollection of it (that sounds a bit like Bill Clinton: “I experimented with statistics but didn’t inhale the knowledge”). Even so, given the increasing quantity of real-time content on the Internet, I find the automated analysis of it fascinating, and hope that something in this post might pique your interest. Naive Bayes classifier Bayesian probability, and in particular the Naïve Bayes classifier, is successfully used in many parts of the web, from IMDB ratings to spam filters. The classifier examines the independent features of an item, and compares those against the features (and classification) of previous items to deduce the likely classification of the new item. It is ‘naïve’ because the features are assessed independently. 4 legs65kg weight60cm height DogHumanDog Classifying Sentiment
REVISTA PIXEL-BIT.NÚMERO 15. JUNIO 2000
Francisco Rodríguez Mondejar Universidad de Murcia Este artículo es un resumen de algunos de los principales aspectos desarrollados e investigados en la tesis doctoral dedicada al estudio de las actitudes del profesorado hacia la informática en los centros de Primaria con Proyecto Atenea de la Región de Murcia. This paper is an abstract of the main aspects developed and researched in the doctoral thesis about the teachers’ attitudes towards the computer science in Primary schools working with Proyecto Atenea in Murcia. Descriptores: Informática, Nuevas Tecnologías de la Información y Comunicación, Actitudes hacia los Medios, Educación, Recursos y Medios, Ordenadores. 1. El caso de la introducción de la informática en los centros no es una excepción, como tampoco lo es la necesidad de conocer las demandas de los profesores y las direcciones de actuación más adecuadas, así como comprender qué creencias y actitudes poseen frente a este fenómeno innovador. 2. 3. 3.1. 3.2. Figura 3. Tabla 1. 4.
Think like a statistician – without the math
I call myself a statistician, because, well, I'm a statistics graduate student. However, ask me specific questions about hypothesis tests or required sampling size, and my answer probably won't be very good. The other day I was trying to think of the last time I did an actual hypothesis test or formal analysis. I couldn't remember. I actually had to dig up old course listings to figure out when it was. Instead, the most important things I've learned are less formal, but have proven extremely useful when working/playing with data. Attention to Detail Oftentimes it's the little things that end up being the most important. The point is that trends and patterns are important, but so are outliers, missing data points, and inconsistencies. See the Big Picture With that said, it's important not to get too caught up with individual data points or a tiny section in a really big dataset. No Agendas This should go without saying, but approach data as objectively as possible. Look Outside the Data
Social Network Analysis
Social Network Analysis: Introduction and Resources What is Social Network Analysis? Network Data Collection and Representation Network Theories Analysis of Network Data Software Applications Books and Journals Article References Selected Online SNA Portals Ulrike Gretzel November, 2001 What is Social Network Analysis? Social network analysis is based on an assumption of the importance of relationships among interacting units. Actors and their actions are viewed as interdependent rather than independent, autonomous units Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial) Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors Wasserman, S. and K. Scott, J., 1992, Social Network Analysis. Index Network Theories
SITE Monitoring Service
