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#opengob : En Maldonado: Municipio inaugura #Abredatos | #opendata

#opengob : En Maldonado: Municipio inaugura #Abredatos | #opendata | Public Datasets - Open Data - | Scoop.it
luiy's insight:

Abredatos es un Software creado en el Municipio de Maldonado, cuyo cometido es extraer, anonimizar y publicar Datos Abiertos de Gobierno, para lograr una gestión cada vez más transparente.

 

Este sistema, que permite liberar datos abiertos conforme a la Ley 18.381, se liberará como Software Público para que pueda servir también en otras entidades públicas y privadas. Puede ser usado, estudiado y modificado libremente sin necesidad de pagar licencias.

 

En la instancia de lanzamiento se presentarán dos nuevos datasets de Datos Abiertos de Gobierno, creados con la herramienta Abredatos (Archivo de Noticias de Prensa, y Archivo de Reclamos Municipales con georreferenciación).

 

El sistema podrá ser compartido por todos los Municipios, Intendencias, Juntas Departamentales, Entes Públicos, Escuelas Públicas del país, o de cualquier otro sitio del mundo.

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Data are or data is? | #bigdata #opendata #dataviz

Data are or data is? | #bigdata #opendata #dataviz | Public Datasets - Open Data - | Scoop.it
Is it singular or plural? It's a word we use every day here on the Datablog - but are we getting it completely wrong?

Via OpenDataSoft
luiy's insight:

How do you say "data"?

 

I only ask because it's a contentious issue. Along with split infinitives, getting this one wrong offends and delights in equal measure. And, as we write about data every day, we're either getting it very wrong or very right.

The Wall Street Journal has just published this blog post, in which it finally decides to move away from data "are", saying:

Most style guides and dictionaries have come to accept the use of the noun data with either singular or plural verbs, and we hereby join the majority.

As usage has evolved from the word's origin as the Latin plural of datum, singular verbs now are often used to refer to collections of information: Little data is available to support the conclusions.

Otherwise, generally continue to use the plural: Data are still being collected.

When we asked the question a couple of years ago, loads of you debated it in a much-polarised manner on Twitter.

@jhugman Data is plural. Unsure the correct "datum point" will catch on though. Having referenda about latin declentions belong in musea.

@mkdDCC No to datum. We need to relax about the data is/are thing. It may not be good Latin, but we're not speaking Latin.

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Anna Sakoyan's curator insight, June 18, 2013 5:00 PM

Yes, that's exactly the question I've been struggling with.

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The rising tide of data journalism

The rising tide of data journalism | Public Datasets - Open Data - | Scoop.it

Data journalism, in a modern format, has a rich history steeped in sociology, the growth of cities and rapidly changing culture.

Journalists have long poured over reams of data, often stacking their work stations with expensive paper copies received from government clerks or hand collected through meticulous research. 

The amount of data they were able to clean and then formulate into empirical evidence sufficient for publication was miniscule, as it depended completely on their ability to process these large amounts of data by hand. 

When computer-assisted reporting rose to prominence in the late 1970s, the game of data analysis changed forever. 

With each new increase in processing capability married to the digitization of documents, especially government documents, journalists gained access to unprecedented and massive data bases from which to extrapolate empirical evidence.


Via Irina Radchenko
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"Qworty" the troll "editor" in wikipedia . tt @bodyspacesoc

"Qworty" the troll "editor" in wikipedia . tt @bodyspacesoc | Public Datasets - Open Data - | Scoop.it
The unmasking of a writer who took extraordinary advantage of online anonymity to pursue old vendettas
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Quandl : #opendata and #dataviz tools | #datasets

About current and historical statistical data
luiy's insight:

Quandl is the easiest way to find and use numerical data on the internet.

Quandl has indexed over 5 million time-series datasets from over 400 sources. All of Quandl's datasets are open and free.

 

You can download any Quandl dataset in any format that you want. You can also visualize, save, share, authenticate, validate, upload, index, merge and transform data .

Our long-term goal is to make all the numerical data on the internet easy to find and easy to use.

Further Reading

Learn more about Quandl:

Quandl's long-term goal is to make all the numerical data on the internet easy to find and easy to use. Read more on our vision page.Quandl currently has over 5 million datasets from over 400 sources. Explore Quandl's data on our sources page.Every dataset on Quandl is available via a simple and consistent API.You can also access Quandl data using our R, Python, Matlab, Excel, Maple, Julia, Clojure, and Stata packages.Check out what's new on Quandl (data, features, topics).Explore Quandl's features and what we've got planned for the future. If you have suggestions, please email us.Learn how developers are building innovative applications that use the Quandl data platform, on our partners page.Questions or comments? See help or email us.Quandl is a collaborative project. Find out how you can get involved on our community page.
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Trend ----> "Code" learning infrastructures in EEUU | #learning #code

Trend ----> "Code" learning infrastructures in EEUU | #learning #code | Public Datasets - Open Data - | Scoop.it
Learn about a new "superpower" that isn't being taught in most schools
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Trend ----> "Code" learning infrastrucutres in EEUU... ----------------------------------------------------------  Snoop DoggRapper, Singer-songwriter, and Actor“support tha american dream n make coding available to EVERYONE!!“
Stephen HawkingTheoretical Physicist, Cosmologist, and Author“Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.“
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Hip-hip-Hadoop: Data mining for science

Hip-hip-Hadoop: Data mining for science | Public Datasets - Open Data - | Scoop.it

The model of distributed calculations, where a problem is broken down into distinct parts that can be solved individually on a computer and then recombined, has been around for decades. But when Google developed the MapReduce algorithm, it added a distinct wrinkle to this method of distributed computing and opened new doors for commercial and scientific endeavors.

Read more at: http://phys.org/news/2013-05-hip-hip-hadoop-science.html#jCphttp://phys.org/news/2013-05-hip-hip-hadoop-science.html


Via Howard Rheingold
luiy's insight:

But when Google developed the MapReduce algorithm, it added a distinct wrinkle to this method of distributed computing and opened new doors for commercial and scientific endeavors.

Apache Hadoop is an open-source software framework that evolved from Google's MapReduce algorithm. Many Internet giants—Facebook, Yahoo, eBay, Twitter—rely on Hadoop to crunch data across thousands of computer servers in order to quickly identify and serve customized data to consumers.


--------------------------------------------------------

 

Training Data Scientists

Deploying a new cluster with important, but largely untested technology for scientists is a great first step. But you also have to identify and build a community to take advantage of these emerging tools. TACC has been a leader in education and outreach to the public, offering training, tutorials and university-level instruction on Hadoop as it relates to high-performance parallel computing.

In Fall 2011 and 2012, Xu introduced Hadoop to students in the Visualization and Data Analysis course he co-teaches in the Division of Statistics and Scientific Computing at the university. In addition, Baldridge and Lease jointly designed a new course, "Data-Intensive Computing for Text Analysis," which was offered in Fall 2011, that involved significant use of TACC's Hadoop resources. Interestingly, the course attracted a multi-disciplinary group with 16 computer science students, four iSchool students, three linguistics students, and two electrical and computer engineering students.

At the end of May 2013, Xu will chair a workshop on Benchmarks, Performance Optimization, and Emerging Hardware of Big Data Systems and Applications in conjunction with 2013 IEEE International Conference on Big Data.

Which of the host of new heterogeneous hardware and software technologies available for high-performance clusters are best suited for data-intensive applications? And how can HPC systems be optimally designed to solve big data problems? These are the questions that TACC's Hadoop R&D seeks to answer.



Read more at: http://phys.org/news/2013-05-hip-hip-hadoop-science.html#jCp

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Howard Rheingold's curator insight, May 28, 2013 10:41 PM

Distributed computation and big data meets collective intelligence. Expect this hybrid to develop.

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Visualizing the TEDx idea network | #SNA

TED Fellows Eric Berlow and Sean Gourley of Quid collaborated with TEDx to visualize the explosion of ideas from the TEDx network through the ideas and theme...
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If My Data Is an Open Book, Why Can’t I Read It? New York Times | #bigdata #opendata

If My Data Is an Open Book, Why Can’t I Read It? New York Times | #bigdata #opendata | Public Datasets - Open Data - | Scoop.it

Despite all the hoopla about an “open data” society, many consumers are being kept in the dark ... Our mobile carriers know our locations: where our phones travel during working hours and leisure time, where they reside overnight when we sleep. Verizon Wireless even sells demographic profiles of customer groups — including ZIP codes for where they “live, work, shop and more” — to marketers. But when I called my wireless providers, Verizon and T-Mobile, last week in search of data on my comings and goings, call-center agents told me that their companies didn’t share customers’ own location logs with them without a subpoena. .


Via Rob Kitchin
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“Stock data, bank data, and bond data are all more valuable when they are looked at together,” says Mr. Searls, the author of “The Intention Economy: When Customers Take Charge.” “If I have a choice between apps and one of them shares the data that I can use more easily, I am going to choose that one.”

 

INTEL, for instance, recently introduced a “data economy” project, intended to encourage companies to think of consumers as participants in the information economy, and not just as data-harvesting opportunities. The venture includes a site called WeTheData.com, which looks at current obstacles to information sharing.

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Wikipedia Recent Changes Map. #datavis #opendata

Wikipedia Recent Changes Map. #datavis #opendata | Public Datasets - Open Data - | Scoop.it
A map of recent contributions to Wikipedia from unregistered users.
luiy's insight:

When an unregistered user edits Wikipedia, he or she is identified by his or her IP address. These IP addresses are translated to users' approximate geographic location. Unregistered users only make a fraction of total edits -- only 15% of the contributions to English Wikipedia are from unregistered users. Edits by registered users do not have associated IP information, so the map actually represents only a small portion of the total edit activity on Wikipedia.

 

Built using d3, DataMaps, freegeoip.net, and the Wikimedia RecentChanges IRC feed, broadcast through wikimon. Sourceavailable on github.

 

Built by Stephen LaPorte and Mahmoud Hashemi.

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LinkedUp Catalogue - Linked Education Cloud. #opendata

luiy's insight:

The Linked Education Cloud is a repository/catalogue of Web datasets relevant to educational applications. It is provided according to the standard of the Web of Data, and is constructed based on input from the LinkedUp Community.

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New algorithm maps cancer cells like nodes on a social network #SNA #health

New algorithm maps cancer cells like nodes on a social network #SNA #health | Public Datasets - Open Data - | Scoop.it
A group of researchers from Columbia and Stanford have created a method for turning complex cellular datasets into visualizations that map the similarities between tens of thousands of cells within a tissue sample.
luiy's insight:

The idea of representing large or complex data as a graph is nothing new, but it has taken on more prominence thanks to the rise of social media and those ubiquitous social graphs that map out who’s connected to whom. As we highlighted recently, however, graph analysis is becoming more popular outside the realm of social networks, and is being applied to problems that are more complex than just figuring out simple relationships within a network. In cases such as medical research, especially, graphs can provide a very effective way of seeing how potentially hundreds of thousands of data points spanning perhaps hundreds of variables are similar to each other.

 

That’s exactly what the team at Columbia and Stanford has done with a new algorithm that they’ve demonstrated within the realm of mass cytometry. According to a press release announcing the research (which is available via paid download at Nature Biotechnology):

“The method, called viSNE (visual interactive Stochastic Neighbor Embedding), is based on a sophisticated algorithm that translates high-dimensional data (e.g., a dataset that includes many different simultaneous measurements from single cells) into visual representations similar to two-dimensional ‘scatter plots’ ….

“The viSNE software can analyze measurements of dozens of molecular markers. In the two-dimensional maps that result, the distance between points represents the degree of similarity between single cells. The maps can reveal clearly defined groups of cells with distinct behaviors (e.g., drug resistance) even if they are only a tiny fraction of the total population. This should enable the design of ways to physically isolate and study these cell subpopulations in the laboratory.”

 

I assume they say similar to scatter plots because the algorithm is analyzing data across more than two dimensions, although the resulting chart is essentially the same (i.e., data points with similar characteristics will form clusters).

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Libro en libre descarga: Jóvenes en la era de la #hiperconectividad, tendencias, claves, miradas

Libro en libre descarga: Jóvenes en la era de la #hiperconectividad, tendencias, claves, miradas | Public Datasets - Open Data - | Scoop.it

La mirada de padres y profesores parece mayoritariamente prisionera de una visión reactiva que les dificulta articular un pensamiento estratégico de aprovechamiento de las grandes potencialidades –no exentas de riesgos– que, desde un punto de vista cognitivo, emocional, moral y cívico, este nuevo contexto nos aporta. Sabemos que el cambio es inexorable; podemos aprovecharlo o sufrirlo.


Via A Petapouca, Pierre Levy
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Dr. Doris Molero's curator insight, May 20, 2013 5:50 AM

Jóvenes en la era de la hiperconectividad, tendencias, clave... 

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What is Hadoop? | #bigdata #dataviz

What is Hadoop? | #bigdata #dataviz | Public Datasets - Open Data - | Scoop.it
http://www.intricity.com/ Come discover how Google and Facebook handle their mountains of data. And how you will soon be processing your mounds of data in th...

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Updated Database Landscape map – June 2013 — Too much information

Updated Database Landscape map – June 2013 — Too much information | Public Datasets - Open Data - | Scoop.it

Via Tony Agresta
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Tony Agresta's curator insight, June 10, 2013 12:47 PM


MarkLogic is uniquely positioned on this database landscape map.    Here's what makes the position very different from other vendors:


1.  Search - MarkLogic is directly connected to all the major enterprise search vendors.   Recent recognition of this was confirmed by Gartner in its Enterprise Search Magic Quadrant.   Notice that other NoSQL technologies are nowhere close to this connection point.


2.  General Purpose - MarkLogic provides an enterprise NoSQL database and Application Services and Search.   With support for many development languages, REST and JAVA APIs, MarkLogic has clear links to SAP, Enterprise DB and a host of other database providers.


3.   Graph and Document - MarkLogic has long been recognized as a document store and used widely all over the world for this purpose.  Notice the subtle connection to Graph as well connecting MarkLogic to other vendors in this space like Neo4J.  MarkLogic 7 promises to deliver a world class triple store to index subjects, predicates and objects in XML documents or load other triples through the MarkLogic Content Pump.  For the first time, the only Enterprise NoSQL technology with search will include semantics.  Updated APIs and support for SPARQL are part of this release.


4.  Big Tables - MarkLogic's ability to handle big data has long been known.  The olive green line is designated for vendors like MarkLogic, Cassandra, HBASE, etc.   MarkLogic's partnership with Intel for Distribution of Apache Hadoop and the fact that MarkLogic ships with Hadoop connectors provide additional confirmation for this position.


5.   Key Value Stores - Data can be stored as keys without a database schema required lby relational databases.  In MarkLogic's case, huge quantities of data can be indexed in real time with data stored in memory and disk making search results instant and complete.   After a recent analysis of over 50+ MarkLogic customers, the abilty to quickly get up and running and deliver new information products to market was a business driver they mentioned over and over again.


The fact is, no one else on the list has all of these qualities.   Because of this unique position, visually you see MarkLogic distanced from other clusters or long lists of technology vendors.  


To learn more, you can go to MarkLogic Resources.






Henry Pan's curator insight, June 11, 2013 10:54 AM

Cool

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Drones Kill : The attacks by the drone - clandestine military operation -#dataviz

Drones Kill : The attacks by the drone - clandestine military operation -#dataviz | Public Datasets - Open Data - | Scoop.it
Great data visualization design from Pitch Interactive . Out of Site, Out of Mind is an anima...
luiy's insight:

Out of Sight, Out of Mind 

 

Description 

 

Since 2004, the US has been practicing in a new kind of clandestine military operation. The justification for using drones to take out enemy targets is appealing because it removes the risk of losing American military, it's much cheaper than deploying soldiers, it's politically much easier to maneuver (i.e. flying a drone within Pakistan vs. sending troops) and it keeps the world in the dark about what is actually happening. It takes the conflict out of sight, out of mind. The success rate is extremely low and the cost on civilian lives and the general well-being of the population is very high. This project helps to bring light on the topic of drones. Not to speak for or against, but to inform and to allow you to see for yourself whether you can support drone usage or not.  

 

Attacks and Victims View The first view you see illustrates the attacks by the drones. We also wanted to give an emphasis on the victims. By clicking on the Victims link you can see an expanded view of the actual victims with the total number of attacks and fatalities for each month.

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Partons extraire les données du web lors du premier scrapathon ! | #crawling #scraping

Partons extraire les données du web lors du premier scrapathon ! | #crawling #scraping | Public Datasets - Open Data - | Scoop.it
Le groupe français de l'Open Knowledge Foundation : promouvoir le savoir libre
luiy's insight:

Nous nous associons à Data Publica pour organiser le premier Scrapathonqui se tiendra le 12 juin 2013 de 16 heures à minuit à Paris. Le « scrapathon » (marathon de scraping)  est un événement ouvert à tous et consacré au recueil de données sur le Web. Il réunira une communauté de scrapeurs dont l’objectif est de collecter un maximum de données nouvelles en un minimum de temps.

 

Ce scrapathon sera hébergé par DojoBoost 41 boulevard Saint Martin à Paris. DojoBoost est un accélérateur qui accueille jusqu’à 50 startups par an.

 

L’événement débutera par une séance ouverte de formation au scraping de 16 heures à 18 heures le mercredi 12 juin, il sera suivi de 6 heures de pratique pendant lesquelles les participants scraperont des sites d’intérêt.

Les données recueillies seront libres et mises à la disposition de tous par l’Open Knowledge Foundation France sur nosdonnées.fr, le portail open data citoyen que nous gérons avec Regards Citoyens.

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It's a beautiful thing when free data meets free analytics | #dataviz #predictions

It's a beautiful thing when free data meets free analytics | #dataviz #predictions | Public Datasets - Open Data - | Scoop.it
Machine learning service BigML and open data service Quandl are collaborating to make it easy to build predictive models around economic data. More importantly, though, is how easy Quandl makes it to find and use data.
luiy's insight:

All the free data-analysis tools in the world aren’t too useful if there aren’t also some free datasets available to analyze. That’s why it’s cool to see BigML, the machine learning service I’ve been writing about for the past year, decide to collaborate with open-data provider Quandl. Even if neither service reaches mass market popularity, I like seeing stakeholders from different camps work together to lay the groundwork for a data democracy.

I won’t waste your time recapping BigML — I’ve done it in detail before — but will note that the service does have some new features since the last time I played around with it. Among them is a new sunburst visualization to complement the classic tree one.

 

 

However, if you’re new to Quandl (like I am), it’s pretty cool. It’s a free service offering up more than 6 million financial, economic and social datasets that are neatly formatted and ready for consumption. Even better is that most (maybe all) of the datasets are organized by time, and Quandl automatically brings up an embeddable and interactive line chart when you click on the link to open the dataset.

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Improving options for unlocking your #graph data - Strata | #opendata #dataviz

Improving options for unlocking your #graph data - Strata | #opendata #dataviz | Public Datasets - Open Data - | Scoop.it
The popular open source project GraphLab received a major boost early this week when a new company comprised of its founding developers, raised funding to develop analytic tools for...
luiy's insight:

Data wrangling: creating graphs
Before you can take advantage of the other tools mentioned in this post, you’ll need to turn your data (e.g., web pages) into graphs. GraphBuilder is an open source project from Intel, that uses Hadoop MapReduce1 to build graphs out of large data sets. Another option is the combination of GraphX/Spark described below. (A startup called Trifacta is building a general-purpose, data wrangling tool, that could help as well. )

 

Data management and search
Once you have a graph, there are many options for how to store it. The choice of database largely depends on amount of data (# of nodes, edges, along with the size of data associated with them), the types of tasks (pattern-matching and search, analytics), and workload. In the course of evaluating alternatives to MySQL (for storing social graph data), Facebook’s engineering team developed and released Linkbench – a data set that can be used to study how graph databases handle production workloads.

 

Most graph databases (such as Neo4j2, AllegroGraph, Yarcdata, and InfiniteGraph) come with tools for facilitating and speeding up search – Neo4j comes with a simple query language (Cipher) for search, other graph databases support SPARQL. The Titan distributed graph database supports different storage engines (including HBase and Cassandra) and comes with tools for search and traversal (based on Lucene andGremlin). Used by Twitter to store graph data, FlockDB targets operations involving adjacency lists.

Among Hadoop users HBase is a popular option for storing graph data. Hadapt’s analytic platform3 integrates Apache Hadoop and SQL, and now also supports graph analysis.

 

Graph-parallel frameworks: Pregel, PowerGraph, and GraphX
BSP is a parallel computing model that has inspired many graph analytics tools. Just like Hadoop’s map andreduce, Pregel4, Giraph and Pregelix, come with primitives that let neighboring nodes send/receive messages to one another, or change the state of a node (based on the state of its neighboring nodes). Efficient graph algorithms are a sequence of iterations built from such primitives. GraphLab uses similar primitives (calledPowerGraph) but allows for asynchronous iterative computations, leading to an expanded set of (potentially) faster algorithms.

GraphX is a new, fault-tolerant, framework that runs within Spark. Its core data structure is an immutablegraph5 (Resilient Distributed Graph – or RDG), and GraphX programs are a sequence of transformations on RDG’s (with each transformation yielding a new RDG). Transformations on RDG’s can affect nodes, edges, or both (depending on the state of neighboring edges and nodes). GraphX greatly enhances productivity by simplifying a range of tasks (graph loading, construction, transformation, and computations). But it does so at the expense of performance: early prototype algorithms written in GraphX were slower6 than those written in GraphLab/PowerGraph.

 

Machine-learning and analytics
Machine-learning tools that target graph data lead to familiar applications such as detecting influential users (PageRank) and communities, fraud detection, and recommendations (collaborative filtering is popular among GraphLab users). Moreover techniques developed in one domain are often reused in other settings. Besides GraphLab, distributed analytics have been implemented in Giraph, GraphX, Faunus, and Grappa. In addition, graph databases like Neo4j and Yarcdata come with some analytic capabilities. As I noted in a recent post, open source, single-node systems like Twitter’s Cassovary7 are being used for computations involving massive graphs.

 

Visualization
When you’re dealing with large graphs, being able to zoom in/out helps with clutter, but so do clever layout algorithms. Popular tools for visualizing nodes and edges include Gephi and GraphViz. Users who want to customize their graphs turn to packages like d3.

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Data Intelligence and Analytics Resources

This page contains links to various resources available throughout our network, for analytics practitioners. It is an attempt to add structure to our content.…

Via Pierre Levy
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1. General Resources

Data Science ApprenticeshipData Science eBookData Science Links (this page) | Share this page on TwitterRSS feeds | Big Data on Google+ | @analyticbridge | AnalyticTalentMost popular blog posts on AnalyticBridgeMost popular blog posts on DataScienceCentralJobs | Books | Training | Competitions | Code Snippets | Conferences
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Pierre Levy's curator insight, May 28, 2013 10:27 PM

Rich collection of resources

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Science as an open enterprise Final report | #openscience #opendata

luiy's insight:

The Science as an open enterprise report highlights the need to grapple with the huge deluge of data created by modern technologies in order to preserve the principle of openness and to exploit data in ways that have the potential to create a second open science revolution.

Exploring massive amounts of data using modern digital technologies has enormous potential for science and its application in public policy and business. The report maps out the changes that are required by scientists, their institutions and those that fund and support science if this potential is to be realised.

Areas for action

Six key areas for action are highlighted in the report:

Scientists need to be more open among themselves and with the public and mediaGreater recognition needs to be given to the value of data gathering, analysis and communicationCommon standards for sharing information are required to make it widely usablePublishing data in a reusable form to support findings must be mandatoryMore experts in managing and supporting the use of digital data are requiredNew software tools need to be developed to analyse the growing amount of data being gathered

 

http://royalsociety.org/policy/projects/science-public-enterprise/report/

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Socrata Releases “Open Source Data Server, Community Edition” | #opendata

Socrata Releases “Open Source Data Server, Community Edition” | #opendata | Public Datasets - Open Data - | Scoop.it
Open data platform provider Socrata releases an open source option: “Socrata Open Data Server, Community Edition.” (Socrata Releases “Open Source Data Server, Community Edition”: http://t.co/G5jTBqLPg4...
luiy's insight:

Our Goals for Community Edition

We’re offering an open source product for a number of reasons, all related to accelerating and broadening the growth of open data. We want to:

- Promote data portability throughout the open data ecosystems.

- Support open source software policies in public organizations around the globe.

- Encourage the development of software on top of open data.

In the words of Kevin Merritt, our CEO, “Socrata is investing in an open source product because it will help us accelerate mainstream adoption of the open data cloud model as the de facto enterprise data architecture. We envision a future where the 99 percent of data still locked up in legacy proprietary systems will be open and accessible to the masses.”

 

What “Community Edition” Will Offer

The “Socrata Open Data Server, Community Edition” supports the ongoing development of open data standards in three key areas, all required for a thriving ecosystem:

 

Data Catalog Interoperability – Enables universal federation of different open data catalogs using a standard catalog schema, based on the W3C Data Catalog Vocabulary (DCAT).

 

Data Portability Based on Standard Data Formats – Standardizes outputs including JSON, XML, and CSV, as well as RDF and other Linked Data standards. The goal is to evolve towards standard schemas that developers can use for popular data sets, based on real-world examples and collaboration between data publishers.

 

Application Portability Based on Open Data API Standards – Standardizes the Application Programming Interfaces (APIs) used to programmatically access open data, using established paradigms and protocols such as REST, HTTP, and Structured Query Language (SQL).

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LinkedUp: Linking Web Data for Education

LinkedUp: Linking Web Data for Education | Public Datasets - Open Data - | Scoop.it
An EU project about the potential of open data in education
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About LinkedUp

LinkedUp aims to push forward the exploitation of the vast amounts of public, open data available on the Web, in particular by educational institutions and organizations.

 
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Veni Competition is inviting submissions | LinkedUp: Linking Web Data for Education

Veni Competition is inviting submissions | LinkedUp: Linking Web Data for Education | Public Datasets - Open Data - | Scoop.it

The LinkedUp Veni Competition has now opened, and is inviting submissions.

 

The first of three consecutive competitions, the Veni Competition is calling for you to submit an innovative and robust prototype or demo that uses linked and/or open data for educational purposes.


Via Irina Radchenko
luiy's insight:
Veni Competition is inviting submissionsMay 22, 2013 in Calls, Challenge, Dissemination

The LinkedUp Veni Competition has now opened, and is inviting submissions.

The first of three consecutive competitions, the Veni Competition is calling for you to submit an innovative and robust prototype or demo that uses linked and/or open data for educational purposes.

The idea is to mash up the vast amount of material and data that can be used in education. You might design a tool for combining datasets and visualising them in interesting new ways, or perhaps develop a search engine that combines search results from different libraries.

If you are looking for further inspiration, please see our use cases, curated from high profile organisations such as the Commonwealth of Learning.

The total prize fund for ‘Veni’ is 5.000 EUR, however these prizes are only one reason to participate. It is also a fantastic opportunity to work with the large, documented repositoryof linked datasets that the LinkedUp team has catalogued and structured, including data about open educational resources, course content and university facilities.

The LinkedUp team will also be providing dedicated support, including technical support with code. A designated developer blog contains information such as ‘cooking recipes’ and ‘how-to-guides’.

Participants will also be able to showcase their ideas and solutions to a wide community of researchers and practitioner, and will provide the opportunity to meet interesting people from across the public and private sector. After a review process invitations will be made to the teams behind the best applications, offering the opportunity to present their submissions at OKCon in Geneva, on 17 September 2013.

More information can be found on the Challenge website.

What are you waiting for? Join the Challenge today!

Deadline for submissions: 27 June 2013

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Intel’s Data Economy Initiative Aims to Help People Capture the Value of Personal Data | #dataawareness #databrokers

Intel’s Data Economy Initiative Aims to Help People Capture the Value of Personal Data | #dataawareness #databrokers | Public Datasets - Open Data - | Scoop.it
The world’s largest chip maker wants to see a new kind of economy bloom around personal data.

Via Pierre Levy
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Intel Labs, the company’s R&D arm, is launching an initiative around what it calls the “data economy”—how consumers might capture more of the value of their personal information, like digital records of their their location or work history. To make this possible, Intel is funding hackathons to urge developers to explore novel uses of personal data. It has also paid for a rebellious-sounding website called We the Data, featuring raised fists and stories comparing Facebook to Exxon Mobil.

 

Intel’s effort to stir a debate around “your data” is just one example of how some companies—and society more broadly—are grappling with a basic economic asymmetry of the big data age: they’ve got the data, and we don’t.

 

Internet firms like Google and Amazon are concentrating valuable data about consumers at an unprecedented scale as people click around the Web. But regulations and social standards haven’t kept up with the technical and economic shift, creating a widening gap between data haves and have-nots.

 

“As consumers, we have no right to know what companies know about us. As companies, we have few restrictions on what we can do with this data,” says Hilary Mason, chief data scientist at Bit.ly, a social-media company in New York. “Even though people derive value, and companies derive value, it’s totally chaotic who has rights to what, and it’s making people uncomfortable.”

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Renato P. dos Santos's curator insight, May 21, 2013 12:32 PM

Intel lança iniciativa p/ q as pessoas se beneficiem financeiramente de seus dados. A maior parte dos app não funciona sem acesso aos dados pessoais de localização, etc. As empresas possuem nossos dados e aceitamos isso em troca do material grátis, personalizações e outras conveniências que obtemos em troca. Mas não há realmente (ainda?) uma "economia de dados", apenas lucro das empresas. Será que o Big Data vai acabar em nossas mãos individuais?