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antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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Social learning dilemma

Social learning dilemma | e-Xploration | Scoop.it
Last week, my father sent me a link to the 100 top-ranked specialties in the sciences and social sciences. The Web of Knowledge report considered 10 broad areas[1] of natural and social science, an...
luiy's insight:

Although we can trace the study of evolution of cooperation to Peter Kropotkin, the modern treatment — especially via agent-based modeling — was driven by the innovative thoughts of Robert Axelrod. Axelrod & Hamilton (1981) ran a computer tournament where other researchers submitted strategies for playing the iterated prisoners’ dilemma. The clarity of their presentation, and the surprising effectiveness of an extremely simple tit-for-tat strategy motivated much of the current work on cooperation. True to their subject matter, Rendell et al. (2010) imitated Axelrod and ran their own computer tournament of social learning strategies, offering 10,000 euros for the best submission. By cosmic coincidence, the prize went to students of cooperation: Daniel Cownden and Tim Lillicrap, two graduate students at Queen’s University, the former a student of mathematician and notable inclusive-fitness theorist Peter Taylor.

 

A restless multi-armed bandit served as the learning environment. The agent could select which of 100 arms to pull in order to receive a payoff drawn independently (for each arm) from an exponential distribution. It was made “restless” by changing the payoff after each pull with probability . A dynamic environment was chosen because copying outdated information is believed to be a central weakness of social learning, and because Papadimitriou & Tsitsiklis (1999) showed that solving this bandit (finding an optimal policy) is PSPACE-complete[3], or in laymen terms: very intractable.

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Stephen Wolfram Adds Analytics to the Quantified-Self Movement | MIT Technology Review

Stephen Wolfram Adds Analytics to the Quantified-Self Movement | MIT Technology Review | e-Xploration | Scoop.it
The creator of the Wolfram Alpha search engine explains why he thinks your life should be measured, analyzed, and improved.
luiy's insight:

What do you see as the big applications in personal analytics?

Augmented memory is going to be very important. I’ve been spoiled because for years I’ve had the ability to search my e-mail and all my other records. I’ve been the CEO of the same company for 25 years, and so I never changed jobs and lost my data. That’s something that I think people will just come to expect. Pure memory augmentation is probably the first step.

 

The next is preëmptive information delivery. That means knowing enough about people’s history to know what they’re going to care about. Imagine someone is reading a newspaper article, and we know there is a person mentioned in it that they went to high school with, and so we can flag it. I think that’s the sort of thing it’s possible to dramatically automate and make more efficient.

 

Then there will be a certain segment of the population that will be into the self-improvement side of things, using analytics to learn about ourselves. Because we may have a vague sense about something, but when the pattern is explicit, we can decide, “Do we like that behavior, do we not?” Very early on, back in the 1990s, when I first analyzed my e-mail archive, I learned that a lot of e-mail threads at my company would, by a certain time of day, just resolve themselves. That was a useful thing to know, because if I jumped in too early I was just wasting my time.

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2012 Social Network Analysis Report – Demographic – Geographic and Search Data Revealed | Ignite Social Media

2012 Social Network Analysis Report – Demographic – Geographic and Search Data Revealed | Ignite Social Media | e-Xploration | Scoop.it
luiy's insight:

Reporting is the same as last year - most sites' search stats were pulled back by querying just their name. For example: "Twitter", instead of "Twitter.com" However, with that said, certain networks such as Tribe.net still needed to utilize the name.com variation, since people looking for tribe could be looking for a myriad of things, thus corrupting the data set.

All data continues to come from Google because they have one of the largest data sets on the web. We continued to use their Google Ad Planner and Google Insight for Search products to pull demographic and geographic data.

 

The Top Cities and Top Region reports show proportionate interest levels to the area based on the given search query.

 

The Demographic and Geographic reports have Y axis numbers that are percentages out of 100, therefore if the score is .52 then it is 52% of the population.

 

The Search Traffic reports are based on proportionate search traffic for the given query. It is on a scale of 100. Therefore if a given month shows the chart near 100, then that is the busiest month for query searches ever reported in Google during that given time frame.

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The Overview Project. Visualization to connect The dots.

The Overview Project. Visualization to connect The dots. | e-Xploration | Scoop.it
Integrated search. This is high on the list of user requests. Overview will shortly have a full-text search field: type in your search word or phrase, and the system will highlight all the documents in the tree that contain that term, and load those documents into the document list. If you find something interesting, you can turn your search results into a tag with one click. New document list. It hasn’t always been obvious that the lower left panel of the main screen is a list of documents, showing all the documents in the currently selected folder or tag. Nor has it been clear how to go through the documents in this list quickly (At the moment, the fastest way is to use the j and k keys.) We’re going to have a much more descriptive list of documents, including title, text snippet, and suggested tags (see below) for document in the list. Suggested tags. Currently, each folder and document is described by a list of characteristic words, the words that make that folder or document different from all the others. We’re expanding this concept into a list of suggested tags, with a super fast way to apply them to a document, or all documents in the list.
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We need to learn how to connect

We need to learn how to connect | e-Xploration | Scoop.it

But if you want to prepare people not just for the next job, but for the one after that, you need to help them think through the relationships they have and what they learn from the people around them. Understanding people isn’t just an HR skill for managers.


Via Kenneth Mikkelsen
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David Hain's curator insight, March 3, 2013 4:38 AM

The usual challenging but profound perspective from Harlod Jarche.  Recommended!

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Visualization of #GlobalBrain community | Bluenod

Bluenod is a simple way to search and explore communities.

Via Spaceweaver
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Spaceweaver's curator insight, May 8, 2013 12:16 AM

Visualization of the #GlobalBrain community on Twitter by using @Bluenod

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Commonwealth of Learning - Perspectives on Open and Distance Learning: Open Educational Resources: Innovation, Research and Practice

Commonwealth of Learning - Perspectives on Open and Distance Learning: Open Educational Resources: Innovation, Research and Practice | e-Xploration | Scoop.it
This book, initiated by the UNESCO/COL Chair in OER, is one in a series of publications by the Commonwealth of Learning (COL) examining OER. It describes the movement in detail, providing readers with insight into OER’s significant benefits, its theory and practice, and its achievements and challenges. The 16 chapters, written by some of the leading international experts on the subject, are organised into four parts by theme: OER in Academia – describes how OER are widening the international community of scholars, following MIT’s lead in sharing its resources and looking to the model set by the OpenCourseWare Consortium OER in Practice – presents case studies and descriptions of OER initiatives underway on three continents Diffusion of OER – discusses various approaches to releasing and “opening” content, from building communities of users that support lifelong learning to harnessing new mobile technologies that enhance OER access on the Internet Producing, Sharing and Using OER – examines the pedagogical, organisational, personal and technical issues that producing organisations and institutions need to address in designing, sharing and using OER Instructional designers, curriculum developers, educational technologists, teachers, researchers, students, others involved in creating, studying or using OER: all will find this timely resource informative and inspiring.
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Global consumer data broker plans to reveal your data | #personaldata #databrokers #privacy

Global consumer data broker plans to reveal your data | #personaldata #databrokers #privacy | e-Xploration | Scoop.it
Acxiom, the company that tracks everything from your income to your politics, plans to introduce a service that will reveal what it knows about you, according to a new report.
luiy's insight:

Consumer data broker Acxiom plans to introduce a service that will reveal to people what it knows about them, according to a Financial Times report.

The company, which is based in Little Rock, Arkansas in the U.S., bills itself as an enterprise data, analytics and software-as-a-service company. It serves 47 of the Fortune 100 companies, more than 7,000 in all, and counts more than a trillion data transactions each week from 700 million consumers worldwide.

 

Even though the company probably has a file on you, that data has never before been available to you. The FT's Emily Steel reports that, in the interest of transparency -- and quite possibly spurred by a U.S. Federal Trade Commission investigation launched in December -- the company will open its database.

 

It's a big deal. This kind of data impacts financial instruments (credit card offers, for example), marketing materials (just got married? Acxiom knows about it, and they've told the appropriate vendors), and even political votes (informing politicians where they may have the most sway).

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Who decides what's cool?.. the coolhunt | gladwell.com -

luiy's insight:

March 17, 1997
ANNALS OF STYLE

Who decides what's cool? 
Certain kids in certain places--
and only the coolhunters know who they are.




The key to coolhunting, then, is to look for cool people first and cool things later, and not the other way around. Since cool things are always changing, you can't look for them, because the very fact they are cool means you have no idea what to look for. What you would be doing is thinking back on what was cool before and extrapolating, which is about as useful as presuming that because the Dow rose ten points yesterday it will rise another ten points today. Cool people, on the other hand, are a constant.

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Four ways data scientists are using digital art to humanize data

Four ways data scientists are using digital art to humanize data | e-Xploration | Scoop.it
The growing pains of big data were apparent at the Data 2.0 Summit on Tuesday in San Francisco. Here is a selection of visualization tools that came up at the meeting.
luiy's insight:

The growing pains of big data were apparent at the Data 2.0 Summit on Tuesday in San Francisco.

 

During one panel, the assertion that data science is dead was indeed debated. Along with the habitual tension between end user requirements for businesses and consumers and the “elitist” ideas of data scientists and engineers, other themes explored included increasing accessibility to data, as well as changing behaviors and encouraging better decision-making with data. Everyone from sales and marketing people to fitness enthusiasts, it turns out, can be motivated by pretty pictures.

As IBM’s Alah Keahey put it during a panel, “there is a hunger for friendly data,” and visualization can help to humanize those threatening terabytes. Here are a selection of new, and new-to-us, visualization tools that came up at the meeting.

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ScienceScape

ScienceScape | e-Xploration | Scoop.it
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SLAVES TO THE ALGORITHM

SLAVES TO THE ALGORITHM | e-Xploration | Scoop.it

Via Dominique Cardon
luiy's insight:

Worse though, far worse, would be if someone in Hollywood filched his computer. It is here that the iconoclasm happens. When Meaney is given a job by a studio, the first thing he does is quantify thousands of factors, drawn from the script. Are there clear bad guys? How much empathy is there with the protagonist? Is there a sidekick? The complex interplay of these factors is then compared by the computer to their interplay in previous films, with known box-office takings. The last calculation is what it expects the film to make. In 83% of cases, this guess turns out to be within $10m of the total. Meaney, to all intents and purposes, has an algorithm that judges the value—or at least the earning power—of art.

To explain how, he shows me a two-dimensional representation: a grid in which each column is an input, each row a film. "Curiously," Meaney says, "if we block this column…" With one hand, he obliterates the input labelled "star", casually rendering everyone from Clooney to Cruise, Damon to De Niro, an irrelevancy. "In almost every case, it makes no difference to the money column."

 

"For me that’s interesting. The first time I saw that I said to the mathematician, ‘You’ve got to change your program—this is wrong.’ He said, ‘I couldn’t care less—it’s the numbers.’" There are four exceptions to his rules. If you hire Will Smith, Brad Pitt or Johnny Depp, you seem to make a return. The fourth? As far as Epagogix can tell, there is an actress, one of the biggest names in the business, who is actually a negative influence on a film. "It’s very sad for her," he says. But hers is a name he cannot reveal. 

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La télémédecine : de la théorie à la pratique | URPS Médecins ...

La TELEMEDECINE : un gros mot ? Un concept fumeux ? Ou bien une réalité pratique et concrète ? En 2013, l'URPS va porter 3 projets de télémédecine d'initiative libérale : ➥ Expertise en dermatologie : demande d'avis en différé ou en ...

Via Emmanuel Capitaine
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‘Going Dark’ Versus a ‘Golden Age for Surveillance’ | Center for Democracy & Technology

‘Going Dark’ Versus a ‘Golden Age for Surveillance’ | Center for Democracy & Technology | e-Xploration | Scoop.it
luiy's insight:

Choosing between “going dark” and “a golden age for surveillance”

This post argues that the big picture for agency access to data is mostly “golden.” The loss of agency access to information, due to encryption, is more than offset by surveillance gains from computing and communications technology. In addition, government encryption regulation harms cybersecurity. These conclusions will not be easily accepted by investigatory agencies, however, so it is important to work through the analysis in more detail.

 

Communications that were previously subject to wiretap may now be shrouded in encryption. In place of the old monopoly telephone network, agencies have to contend with a confusing variety of communications providers, some of which have little experience in complying with legal process. It is no wonder agency officials strenuously object to the use of new technology that hinders their ability to employ traditional surveillance methods.

 

Implementing wiretaps and reading the plaintext of communications are not the only goal, however. The computing and communications infrastructure are vital to economic growth, individual creativity, government operations, and numerous other goals. If there is a modest harm to investigatory agencies and an enormous gain, societies should choose the enormous gain. In 1999 the U.S. government concluded that strong encryption was precisely that sort of valuable technology – it was worth going at least slightly “dark” in order reap the many benefits of effective encryption. Not even the attacks of September 11, 2001 changed that judgment.

The evidence suggests, furthermore, that the degradation of wiretap capability has been modest at most, and—at least statistically—wiretaps have become more useful over time. The number of wiretap orders implemented in the U.S. has grown steadily the last two decades. According to publcally available statistics, court approved wiretaps are now at a record high: 3,194 wiretap court orders were issued for the interception of electronic, wire, or oral communications in 2010, a 34% increase from the 2,376 issued in 2009. In the six instances where encryption was encountered, it did not prevent law enforcement from retrieving the plaintext forms of communication.

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Interview With Barry Wellman

Interview With Barry Wellman | e-Xploration | Scoop.it
© Barry Wellman and Figure/Ground Communication Dr. Wellman was interviewed by Andrew Iliadis on October 30th, 2012 Professor Barry Wellman is based at the University of Toronto, he directs NetLab,...
luiy's insight:

How do you see the individual actor in contemporary society? Your theory of “networked individualism” implies something different from actors working within groups. Is it the information in the network that comprises the individual? Can you say something briefly about this concept?

Well we’re playing off against, mostly against groups. Groups are really tightly bounded and densely knit networks where everybody knows each other; the village or a work team would be the best examples of it. A lot of things that a lot of evidence points out are that people live in multiple communities. I remember when I did my first study in East York, which is in Toronto by the way, in 1968, we were surprised to find out how few people lived in the same neighborhood.  We always thought of communities as neighborhoods. Now we’re studying work groups and we’re finding the same thing. Scholars especially move around from team to team. So, yes, that’s an issue, and networked individualism says, look, there are individuals, they are centers of their own personal networks, and then they move around between team and team. And we can’t analyze them as motley type super-individuals because people are constrained, they’re connected. We couldn’t solve the New York City Hurricane Sandy flood situation by giving everybody a little shovel. We have to have something that is comprised of small little building block groups.

 

Another concept – “Glocalization” – is becoming widely used and this is in part due to your own research on the subject. What is glocalization, and how is it different from the older model of globalization?

Glocalization is an multiple invented term. Keith Hampton, who was once my student and is now a faculty member at Rutgers, and I jointly invented the term. But we found that four or five other people did too. It’s a neologism certainly in which we put together global and local, and what we kept finding is people use social media such as the internet to be wildly connected, but at the same time the local situations turn out to be very important, both online and of course in real life because as computer scientists keep forgetting, they have bodies, so glocalization in our sense means interaction that is both global and local and of course everything in-between happening more or less simultaneously. But for many people the local is more important because almost always the people they speak to on the internet, or whatever form of it, are the same people they see in their physical interaction as well. There is no separation between the two.

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Data visualization meets cyber crime - Risk Management

Data visualization meets cyber crime - Risk Management | e-Xploration | Scoop.it
Big data can be a bad thing - if you can't see the knowledge locked inside. That is particularly important when you are trying to see possible relationships and linkages.
luiy's insight:

According to the Herald, Army General Keith Alexander, head of the US military’s Cyber Command, predicts “the intensity and number of attacks will grow significantly throughout the year.” He said that cyber-attacks – particularly on the US banking sector – are getting worse. In fact, several major banks were recently targeted with coordinated Denial of Service (DoS) attacks.

Because of the increase in the number and severity of these attacks, many organizations have begun using advanced analytics and data visualization technologies to find cyber-crime activity and predict future attacks. These technologies help employees who aren’t data scientists or analysts to ask questions of the data – based on their own business expertise – to quickly and easily find patterns, spot inconsistencies, even get answers to questions they haven’t yet thought to ask.

The Navy Cyber Defense Operations Command is using data visualization to help defend the safety and security of the US Navy’s computer networks. These same capabilities are available for banks. Enormous amounts of network traffic data can be aggregated, manipulated, fused, visualized, processed and analyzed in a drag-and-drop interface. Sophisti­cated analyses can be performed quickly – even immediately – by people across all levels of your organization.

Using multiple types of analysis, alerts and other valuable intelligence can be created for anomaly detection and predictive analytics, and to investigate slow and low network intrusion. And the analytic models get smarter over time with learning and improvement cycles.

Analytics and data visualization gives a more complete picture of a bank’s systems and networks so that you can take a strategic approach to prioritizing resources and efforts instead of just plugging holes and fighting fires. These technological capabilities go well beyond business intelligence to provide data-driven information and analysis that is future directed, so decision makers can be proactive.  To make the data even more accessible, results can be delivered through multiple channels, including smart phones and iPads.

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Data Visualization: 25 free Tools

Data Visualization: 25 free Tools | e-Xploration | Scoop.it
Samples of data visualization tools include maps, graphs, pictures, and many more. The 25 free visualization tools featured in this list.
luiy's insight:

Date visualization can be a pain if one is not aware of the many tools one can use in its endeavor. Data visualization is a process wherein data is shown in a simpler way in order for people to understand easily. Samples of data visualization tools include maps, graphs, pictures, and many more. There are many tools like this in the Internet, but none can be as effective as the 25 free visualization tools featured in this list. Some of these free visualization tools are being used for years now, while others are still new in the business. Some of these apps also try to broaden our approach to this quite tiring yet rewarding part of information technology. A few are orthodox and mathematical in nature, while some apps will try to visualize important human elements such as love, joy, hope, and faith. The 25 best free visualization tools are:

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malek's curator insight, May 9, 2013 5:36 AM

A few are orthodox and mathematical in nature, while some apps will try to visualize important human elements such as love, joy, hope, and faith.

Lindsay Wilson's curator insight, May 9, 2013 7:48 AM

Great to learn about new technology for charts and visualizations 

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The Connected Workplace

The Connected Workplace | e-Xploration | Scoop.it

Today’s digitally connected workplace demands a completely new set of skills. Our increasing interconnectedness is illuminating the complexity of our work environments. More connections create more possibilities, as well as more potential problems.


Via Kenneth Mikkelsen
luiy's insight:

On the negative side, we are seeing that simple work keeps getting automated, like automatic bank machines. Complicated work, for which standardized processes can be developed, usually gets outsourced to the lowest cost of labor.

 

On the positive side, complex work can provide unique business advantages and creative work can help to identify new business opportunities. However, complex work is difficult to copy and creative work constantly changes.

 

But both complex and creative work require greater implicit knowledge. Implicit knowledge, unlike explicit knowledge, is difficult to codify and standardize. It is also difficult to transfer.

 

Implicit knowledge is best developed through conversations and social relationships. It requires trust before people willingly share their know-how. Social networks can enable better and faster knowledge feedback for people who trust each and share their knowledge. But hierarchies and work control structures constrain conversations. Few people want to share their ignorance with the boss who controls their paycheck.But if we agree that complex and creative work are where long-term business value lies, then learning amongst ourselves is the real work in organizations today. In this emerging network era, social learning is how work gets done.

Becoming a successful social organization will require more than just the implementation of enterprise social technologies. Developing, supporting, and encouraging people to use a range of new social workplace skills will be just as important. Individual skills, in addition to new organizational support structures, are both required.

 

Personal knowledge management (PKM) skills can help to make sense of, and learn from, the constant stream of information that workers encounter from social channels both inside and outside the organization. Keeping track of digital information flows and separating the signal from the noise is difficult. There is little time to make sense of it all. We may feel like we are just not able to stay current and make informed decisions. PKM gives a framework to develop a network of people and sources of information that one can draw from on a daily basis. PKM is a process of filtering, creating, and discerning, and it also helps manage individual professional development through continuous learning.

 

Collaboration skills can help workers to share knowledge so that people work and learn cooperatively in teams, communities of practice, and social networks. In order to support collaborative working and learning in the organization, it is important to experience what it means to work and learn collaboratively, and understand the new community and collaboration skills that are involved. “You can’t train someone to be social, only show them how to be social.” Practice is necessary.

 

The power of social networks, like electricity, will inevitably change almost every existing business model. Leaders need to understand the importance of organizational architecture. Working smarter in the future workplace starts by organizing to embrace networks, manage complexity, and build trust. The 21st century connected enterprise is a new world of work and learning.

 

For example, traditional training structures, based on institutions, programs, courses and classes, are changing. Probably the biggest change we are seeing is that the content delivery model is being replaced by more social and collaborative frameworks. This is due to almost universal Internet connectivity, especially with mobile devices, as well as a growing familiarity with online social networks.

 

Work is changing and so organizational learning must change. There is an urgent need for organizational support functions (HR, OD, KM, Training) to move beyond offering training services and toward supporting learning as it is happening in the digitally connected workplace. The connected workplace will not wait for the training department to catch up.

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Ivon Prefontaine's curator insight, April 20, 2013 2:34 PM

"Working smarter in the future workplace starts by organizing to embrace networks, manage complexity, and build trust. The 21st century connected enterprise is a new world of work and learning." We are trying to build community and not just teams. Trust is a community quality. What happens when we disagree? Trust answers that question.

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RetweetLab : Analyser votre compte Twitter rapidement

RetweetLab : Analyser votre compte Twitter rapidement | e-Xploration | Scoop.it

Via Veille digitale, association concert urbain, Rui Guimarães Lima
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Veille digitale's curator insight, May 8, 2013 1:31 AM

Un outil simple et complet à conserver dans les favoris. 

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A Realistic View of What Data Brokers Actually Know About You

A Realistic View of What Data Brokers Actually Know About You | e-Xploration | Scoop.it
There are the firms and sites that compile public data about you and offer it up to anyone, and then there are the data brokers quietly sketching a picture of you from your purchases and behaviors. You should know what they know about you.
luiy's insight:

Non-profit journalism center ProPublica details the business and craft of data brokering, in which layer upon layer of information is compiled to sell to marketing firms. Often times the profiles are unnamed, but if you're wondering why your junk mail and back-of-receipt coupons seem so specific lately, it's because you're not that hard to figure out. Register for a Disney vacation, for example, and a whole bunch of subsidiaries get an idea of what you might like:

(Disney) described sharing even more information: not just a person's name and address and what they purchased, but their age, occupation, and the number, age and gender of their children. It listed companies that received data, among them companies owned by Disney, like ABC and ESPN, as well as others, including Honda, HarperCollins Publishing, Almay cosmetics, and yogurt company Dannon.

You can do something to cut back on your own data broker profile, even if you can't exactly clean the slate:

Some companies do offer more access. A spokeswoman for Epsilon said it allows consumers to review "high level information" about their data - like whether or not you're listed as making a purchase in the "home furnishings" category. (Requests to review this information cost $5 and can only be made by postal mail.)

RapLeaf, a company that advertises that it has "real-time data" on 80 percent of U.S. email addresses, says that it gives customers "total control over the data we have on you," and allows them to review and edit the categories (like "estimated household income" and "Likely Political Contributor to Republicans") that RapLeaf has connected with their email addresses.

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What is Personal Data and How Much Personal Data Exists? | MIT Technology Review #privacy #databrokers

What is Personal Data and How Much Personal Data Exists? | MIT Technology Review #privacy #databrokers | e-Xploration | Scoop.it
As digital data expands, anonymity may become a mathematical impossibility.
luiy's insight:

In 1995, the European Union introduced privacy legislation that defined “personal data” as any information that could identify a person, directly or indirectly. The legislators were apparently thinking of things like documents with an identification number, and they wanted them protected just as if they carried your name.

Today, that definition encompasses far more information than those European legislators could ever have imagined—easily more than all the bits and bytes in the entire world when they wrote their law 18 years ago.

Here’s what happened. First, the amount of data created each year has grown exponentially: it reached 2.8 zettabytes in 2012, a number that’s as gigantic as it sounds, and will double again by 2015, according to the consultancy IDC. Of that, about three-quarters is generated by individuals as they create and move digital files. A typical American office worker produces 1.8 million megabytes of data each year. That is about 5,000 megabytes a day, including downloaded movies, Word files, e-mail, and the bits generated by computers as that information is moved along mobile networks or across the Internet....

 

 

The greater the amount of personal data that becomes available, the more informative the data gets. In fact, with enough data, it’s even possible to discover information about a person’s future. Last year Adam Sadilek, a University of Rochester researcher, and John Krumm, an engineer at Microsoft’s research lab, showed they could predicta person’s approximate location up to 80 weeks into the future, at an accuracy of above 80 percent. To get there, the pair mined what they described as a “massive data set” collecting 32,000 days of GPS readings taken from 307 people and 396 vehicles.

They then imagined the commercial applications, like ads that say “Need a haircut? In four days, you will be within 100 meters of a salon that will have a $5 special at that time.”

Sadilek and Krumm called their system “Far Out.” That’s a pretty good description of where personal data is taking us.

 

 

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WORDij Semantic Network Tools

WORDij Semantic Network Tools | e-Xploration | Scoop.it
Overview of WORDij WORDij is a family of various programs designed to automate content analysis a substantial amount. The software runs on Windows 32-bit and 64-bit, Mac 32-bit and 64-bit, and Linux 74-bit OS. WORDij runs very efficiently and therfore is fast.This is because the basic tools are written in C++ and optimized considerably. Java is used only for the Graphical User Interface. The suite can run data files as large as 550 megabytes with 8 gigabytes of RAM on a 64-bit machine. Small files with only 10s or 100s of documents can also be effectively analyzed. Files analyzed are in UTF-8 format, so the programs can handle languages with graphic characters such as Chinese or Russian. WordLink, a program that moves a window through text to count word pairs, is proximity based, and therefore is more precise than "bag of words" programs. By using an "include list," the opposite of "drop list or stop list," networks among the included words only can be analyzed.. For example, a list of persons' names can be entered and news documents or meeting notes can be run to find the network of persons based on their co-occurrence closely in texts such as news stories
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Careful: Your big data analytics may be polluted by data scientist bias

Careful: Your big data analytics may be polluted by data scientist bias | e-Xploration | Scoop.it
True believers may be guilty of hype, but there’s no denying that big data presents opportunities for businesses of every stripe. That potential is vulnerable to pollution from data bias, and so calls for preventative processes.
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Data bias syndrome

Data bias comes in many forms. It can come from poorly defined business domain objectives. Or, it can come from opting to gather data that are easy to collect rather than data that are most informative. Data scientists can also receive data that have been biased by incorrect assumptions by the domain experts. (And as a footnote, the recent example of the austerity economics Excel scandal shows how a minute data error can have cascading and devastating effects.)

 

Likewise, data scientists themselves are not immune to bias. Some can run afoul of their own preconceived notions about business domain – too much knowledge can cause one to filter out data that may actually be helpful.  Scientists with deep experience in a particular data set may develop too much reliance on pre-existing algorithms without re-examining validity for a particular use case.

 

Finally, data quantity is a common problem. Intelligent learning requires abundant data, and often the data available are not sufficient to draw accurate conclusions – a problem known as data sparsity. This may sound unbelievable considering that data volume is doubling every two years according to an EMC study,  but there’s a difference between a dense data set populated by similar data points, and the far more diverse sets of user data points we find in the real world. In these cases, the gaps in the data are filled by machine learning algorithms that may inherently be biased, based on assumptions made by the data scientist when designing the algorithm. The trick is to find the right balance between unbiased data exploration and data exploitation.

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CNS Newsletter. Cyberinfrastructure for Network Science Center

CNS Newsletter. Cyberinfrastructure for Network Science Center | e-Xploration | Scoop.it
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Over the last several months, CNS was lucky enough to be part of an extremely productive collaboration with the National Academy of Sciences in Washington, DC, that resulted in a state-of-the-art, interactive touch-screen visualization called AcademyScope. Using a 55-inch, multi-touch screen, viewers can explore all reports published by the National Academy of Sciences, National Academy of  Engineering, Institute of Medicine, and National Research Council-from twenty years ago to today. The Automatic Modeof the visualization shows the 100 most frequently downloaded reports of the previous seven days as well as newly released reports. The Interactive Mode supports browsing and exploration by topic, subtopic, and individual report. There's also a QR code that allows users to download a PDF of any report to their smart device. AcademyScope was officially unveiled in the Great Hall at the National Academy in advance of the April 25th D.C. Art Science Evening Rendezvous (DASER) and is available to the public for exploration.
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Maps & Macroscopes -- Gaining Insights from BIG Data: Katy Borner at TEDxBloomington

Katy Börner is the Founding Director of the Cyberinfrastructure for Network Science Center (CNS) (http://cns.iu.edu) at Indiana University. The CNS Center de...
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Katy Börner is the Founding Director of the Cyberinfrastructure for Network Science Center (CNS) (http://cns.iu.edu) at Indiana University. The CNS Center develops plug-and-play macroscope tools and services that convert data into insight and are used by more than 100,000 students, researchers, and practitioners around the globe to answer when (temporal), where (geospatial), what (topical), and with whom (network analysis) questions. She is a curator of the "Places & Spaces: Mapping Science" exhibit (scimaps.org) that demonstrates the power of maps for tracking and communicating human activity and scientific progress on a global scale. Her books "Atlas of Science: Visualizing What We Know" (2010), "Modeling Science Dynamics" (2012), "VIVO: A Semantic Approach to Scholarly Networking and Discovery" (2012), and the new Information Visualization MOOC aim to empower anyone to make sense of small and big data. Börner is the Victor H. Yngve Professor of Information Science at the IU School of Library and Information Science, Adjunct Professor at the School of Informatics and Computing, Adjunct Professor at the Department of Statistics in the College of Arts and Sciences, and Core Faculty of Cognitive Science. @katycns

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