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antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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The Modern Data Nerd Isn't as Nerdy as You Think

The Modern Data Nerd Isn't as Nerdy as You Think | e-Xploration | Scoop.it
Data scientists are fast becoming the rock stars of the 21st century. Thanks in part to Nate Silver's eerily accurate election predictions and Paul DePodesta's baseball-revolutionizing Moneyball techniques, math nerds have become celebrities.

Via Pierre Levy
luiy's insight:

Data scientists are fast becoming the rock stars of the 21st century. Thanks in part to Nate Silver’s eerily accurate election predictions and Paul DePodesta’s baseball-revolutionizing Moneyballtechniques, math nerds have become celebrities. It’s debatable how much their work differs from what statisticians have done for years, but it’s a growing field, and many companies are desperate to hire their own data scientists.

The irony is that many of these math nerds aren’t as math nerdy as you might expect.

 

Some of the best minds in the field lack the sort of heavy math or science training you might expect. Silver and Paul DePodesta have bachelor’s degrees in economics, but neither has a PhD. Former Facebook data scientist and Cloudera co-founder Jeff Hammerbacher — who helped define the field as it’s practiced today — only has a bachelor’s in mathematics. The top ranked competitor at Kaggle — which runs regular contest for data scientists — doesn’t have a PhD, and many of the site’s other elite competitors don’t either.

 

“In fact, I argue that often Ph.D.s in computer science in statistics spend too much time thinking about what algorithm to apply and not enough thinking about common sense issues like which set of variables (or features) are most likely to be important,” says Kaggle CEO Anthony Goldbloom.

 

Data scientist John Candido agrees. “An understanding of math is important,” he says, “but equally important is understanding the research. Understanding why you are using a particular type of math is more important than understanding the math itself.”

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Pierre Levy's curator insight, April 15, 2013 8:42 AM

Knowing the field is more important than knowing the math...

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Hive Plots - Visually Interpreting Network Structure and Content Made Possible

Hive Plots - Visually Interpreting Network Structure and Content Made Possible | e-Xploration | Scoop.it
luiy's insight:

To rationally visualize networks, we introduce the hive plot. The hive plot is based on meaningful network properties, which can be selected to address a specific question.

 

Nodes are assigned to one of three (or more) axes, which may be divided into segments. Nodes are ordered on a segment based on properties such as connectivity, density, centrality or quantitative annotation (e.g. gene expression). The user is free to choose whatever rules fit their data and visualization requirements. Edges are drawn as Bezier curves, which can be annotated with color, thickness or label to communicate additional information.

 

Hive plots make it possible to assess network structure because they are founded on network properties, not on aesthetic layout. Visualizations of two networks are directly comparable. Importantly, hive plots are perceptually uniform — differences in hive plots are proportional to differences in underlying networks. This makes it possible to use hive plots to assess network similarity.

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Behavior prediction software company Behavio now part of Google

Behavior prediction software company Behavio now part of Google | e-Xploration | Scoop.it

Behavio, a company that developed software capable of collecting smartphone data in order to certain predict behavior, is now part of Google.


Via LeapMind, Alessio Erioli, Andrea Graziano
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Jayne Fenton Keane's curator insight, April 17, 2013 2:41 PM

This is progressing fast

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Create a PDF e-book of tweets with tweetbook.in

Create a PDF e-book of tweets with tweetbook.in | e-Xploration | Scoop.it
Tweetbook.in lets you create and save a diary like PDF ebook of tweets. It is a simple app that lets you take a backup of your tweets and favourites. It is one of the earliest twitter backup applications.

Via Ana Cristina Pratas
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Cardiff Online Social Media Observatory (COSMOS): Social Media and Data Mining

Cardiff Online Social Media Observatory (COSMOS): Social Media and Data Mining | e-Xploration | Scoop.it
Project Information about the The SOCSI/COMSC Research Network, Cardiff School of Social Sciences at Cardiff University, Wales, UK.

Via Martin Weller
luiy's insight:

Cardiff Online Social Media Observatory (COSMOS) is an Economic and Social Research Council (ESRC) and Joint Information Systems Committee (JISC) half a million pound investment that brings together social, political, health, mathematical and computer scientists to study the methodological, theoretical, empirical and policy dimensions of Big ‘Social’ Data.   Our objective is to establish a coordinated international social science response to this new form of data in order to address next-generation research questions.

Our empirical research programme is contextualized in terms of the ‘coming crisis of empirical sociology’ (Savage and Burrows, 2007), which is located in the increasing asymmetry between traditional social scientific methods and the power of transactional data generated through the internet. This has led some commentators to question the extent to which university-based sociology and social science can compete with the data rich resources built into the marketing and data generation strategies of the large multi-national corporations that hold and marshal much of this transactional data.

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#baidu : 'Chinese Google' Opens research lab dedicated to “deep learning”

#baidu : 'Chinese Google' Opens  research lab dedicated to “deep learning” | e-Xploration | Scoop.it
Wired (blog) 'Chinese Google' Opens Artificial-Intelligence Lab in Silicon Valley Wired (blog) The artificial intelligence community moved toward systems that solved problems by crunching massive amounts of data, rather than trying to build “neural...

Via RomanGodzich
luiy's insight:

It doesn’t look like much. The brick office building sits next to a strip mall in Cupertino, California, about an hour south of San Francisco, and if you walk inside, you’ll find a California state flag and a cardboard cutout of R2-D2 and plenty of Christmas decorations — even though we’re well into April.

 

But there are big plans for this building. It’s where Baidu — “the Google of China” — hopes to create the future.

In late January, word arrived that the Chinese search giant was setting up a research lab dedicated to “deep learning” — an emerging computer science field that seeks to mimic the human brain with hardware and software — and as it turns out, this lab includes an operation here in Silicon Valley, not far from Apple headquarters, in addition to a facility back in China. The company just hired its first researcher in Cupertino, with plans to bring in several more by the end of the year.

Baidu calls its lab The Institute of Deep Learning, or IDL. Much like Google and Apple and others, the company is exploring computer systems that can learn in much the same way people do. “We have a really big dream of using deep learning to simulate the functionality, the power, the intelligence of the human brain,” says Kai Yu, who leads Baidu’s speech- and image-recognition search team and just recently made the trip to Cupertino to hire that first researcher. “We are making progress day by day.”

If you want to compete with Google, it only makes sense to set up shop in Google’s backyard. “In Silicon Valley, you have access to a huge talent pool of really, really top engineers and scientists, and Google is enjoying that kind of advantage,” Yu says. Baidu first opened its Cupertino office about a year ago, bringing in various other employees before its big move into deep learning.

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#DataScience : WEKA, for Waikato Environment for Knowledge - Machine Learning Group

#DataScience : WEKA, for Waikato Environment for Knowledge - Machine Learning Group | e-Xploration | Scoop.it
luiy's insight:

An exciting and potentially far-reaching development in computer science is the invention and application of methods of machine learning (ML). These enable a computer program to automatically analyse a large body of data and decide what information is most relevant. This crystallised information can then be used to automatically make predictions or to help people make decisions faster and more accurately.

Project Objectives

Our objectives are to

make ML techniques generally available;apply them to practical problems that matter to New Zealand industry;develop new machine learning algorithms and give them to the world;contribute to a theoretical framework for the field.

 

Software

Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. WEKA's users are ML researchers and industrial scientists, but it is also widely used for teaching. Recently, our team has also worked on MOA, an environment for mining data streams.

 

http://es.wikipedia.org/wiki/Weka_(aprendizaje_autom%C3%A1tico)

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Has the design professional become a dinosaur?

Has the design professional become a dinosaur? | e-Xploration | Scoop.it

In this day and age, is there a need for design professionals? Is DIY the wave of the future?


Via Luca Baptista
luiy's insight:

The amount of information available through the websites of architects and interior designers, social media sites, online communities, blogs and reality shows gives consumers unprecedented access to the world of design. Add to that lifestyle retailers, and it appears we can assemble everything  we need to outfit a home, remodel it or even build it from the ground up. Even though statistical evidence shows that costs escalate as much as 40% compared to what it would cost if hiring a design professional, most DIYers would do it all over again.

 

DESIGNOSAURUS REX

 

There is also the reality that design professionals charge fees, and paying fees is like paying more taxes, it chips away some more at our hard earned money. An attorney, dentist, plumber or electrician also provide specialized services, which we need from time to time. Do you ever really need a design professional?

 

With tangible evidence to the contrary, why should you hire a design professional?

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Jusqu'où vont vos tweets ? Découvrez-le via une sublime visualisation de données

Jusqu'où vont vos tweets ? Découvrez-le via une sublime visualisation de données | e-Xploration | Scoop.it
Comment se propage un tweet ? Des français lancent Where Does My Tweet Go? pour vous aider à visualiser l'impact d'un message publié sur Twitter.
luiy's insight:

Benoît Vidal, en charge du digital chez MFG Labs, est venu vers moi avec deux références qui ne pouvaient que piquer mon intérêt.

 

En effet, il a d’un côté participé très tôt à l’aventure Cinémur et de l’autre co-fondé Dataveyes (qui est finaliste du concours start-up Presse-Citron ).

 

C’est aujourd’hui avec la casquette d’architecte de l’information pour MFG Labs qu’il m’a proposé de découvrir Where Does My Tweet Go (ou WDMTG pour faire court).

 

Qu’est-ce que « Where Does My Tweet Go » ?

Benoît Vidal : « WDMTG propose une nouvelle expérience digitale pour découvrir les tweets les plus intéressants, via un « algorithme visualisé » appelé SpreadRank.


Le SpreadRank permet de déterminer les tweets qui propagent le plus à travers le réseau. Nous visualisons l’algorithme pour éviter l’effet « boîte noire » des services de personnalisation et recommandation plus classiques (Zite, etc).


Le SpreadRank s’avère très efficace et permet de différencier fortement les tweets beaucoup retweetés mais seulement à travers leur communauté (exemple : un tweet de Justin Bieber) versus les tweets avec une vraie valeur informationnelle qui propagent fortement (exemple : arrestation de DSK à New-York).


En somme, WDMTG permet de comprendre comment un message se propage sur twitter, et intéresse de fait fortement les marques. »

Comment est née l’idée de WDMTG ?

Benoît : L’idée est née de la volonté de montrer et de mesurer la puissance virale d’un message sur un réseau comme twitter, le réseau social où l’information propage comme sur aucun autre réseau. C’est en visualisant cette propagation que le SpreadRank est né. La visualisation nourrit l’algorithme et non le contraire.


À l’inverse de Klout, nous ne calculons pas un score sur les personnes, mais sur les messages. Bien sûr, l’influence d’une personne joue dans la vie d’un tweet, mais observer la pénétration d’un message à travers le réseau est bien plus pertinent ; les messages avec un SpreadRank élevé ont de fait plus d’impact.

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» About Journal of Digital Humanities

» About Journal of Digital Humanities | e-Xploration | Scoop.it
luiy's insight:

The Journal of Digital Humanities (ISSN 2165-6673) is a comprehensive, peer-reviewed, open access journal that features the best scholarship, tools, and conversations produced by the digital humanities community in the previous quarter.

 

The Journal of Digital Humanities offers expanded coverage of the digital humanities in three ways. First, by publishing scholarly work beyond the traditional research article. Second, by selecting content from open and public discussions in the field. Third, by encouraging continued discussion through peer-to-peer review.

 

The Journal of Digital Humanities selects content from the Editors’ Choice pieces from Digital Humanities Now, which highlights the best scholarship — in whatever form — that drives the field of digital humanities field forward. The Journal of Digital Humanities provides three additional layers of evaluation, review, and editing to the pieces initially identified by Digital Humanities Now.

 

The Journal of Digital Humanities and Digital Humanities Now are produced by the Roy Rosenzweig Center for History and New Media.

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The first Bitcoin crash

The first Bitcoin crash | e-Xploration | Scoop.it

Why the fall? Until last week you could exchange Bitcoins for Dollars on Paypal, but that was then blocked by Paypal, so converting Bitcoins to real currency became a lot harder, and the Bitcoin fell sharply.


Via Luca Baptista
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#lak13 : Case Study - Virtual Worlds and Learning Analytics

The presentation delivered during LAK13, Leuven April 2013

Via Ana Cristina Pratas, Rui Guimarães Lima
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Google builds data-after-death tool

Google builds data-after-death tool | e-Xploration | Scoop.it
Google becomes the first major company to allow users to decide what happens to their data after they die or become inactive online.

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Google will allow users to decide what happens to their data after they die or become inactive online, the first major company to deal with the sensitive issue.

The feature applies to email, social network Google Plus and other accounts.

Users can choose to delete data after a set period of time, or pass it on to specific people.

Internet users around the world have expressed concern about what happens to their data after their demise.

"We hope that this new feature will enable you to plan your digital afterlife - in a way that protects your privacy and security - and make life easier for your loved ones after you're gone," Google said in a blogpost.

California-based Google also owns YouTube, photo-sharing service Picasa and Blogger.

Google said users can opt to have their data deleted after three, six, nine or 12 months of inactivity. Alternatively, certain contacts can be sent data from some or all of their services.

However, the company said it would text a provided number or email a secondary email address to warn users before any action is taken.

People are increasingly placing content on social networks and data storage facilities hosted in cyberspace, or the "cloud".

Other companies have also attempted to tackle the questions that raises after a person's death. Facebook, as an example, allows users to "memorialise" an account.


Via Wildcat2030
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Data Visualization, Design and Information Munging // Martin Krzywinski / Genome Sciences Center

Data Visualization, Design and Information Munging // Martin Krzywinski / Genome Sciences Center | e-Xploration | Scoop.it
luiy's insight:
HE ART OF π, φ AND e

Numerology is bogus, but art based on numbers has a beautiful random quality.

 

For other examples of numerical art, see my inessiness project. Nixie clock lovers should investigate theaccidental similarity number.

ART OF π

Cristian Ilies Vasile had the idea of representing the digits of π as links that connected segments associated with successive digits. The image is composed of links (segment:position) 3:0 → 1:1 → 4:2 → 1:3 → 5:4 …

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Oycib. Collaborative infrastructure for e-Research. by @luiy

Oycib. Collaborative infrastructure for e-Research. by @luiy | e-Xploration | Scoop.it
luiy's insight:

The Oycib infrastructure is based in an ethnographic observation model based agents called e-Xploration. The infrastructure allows the analysis, interpretation and visualization of profiles and digital practices.

 

The proposed profiles analysis is based on the metaphor of Maya social organization.

 

Furthermore, we propose work in the context-awareness to enhance the collaboration and cooperation among people and groups.

 

This infrastructure is developed with different systems based on OPEN philosophies, such as: ELGG, SCi2, GEPHI and SIGMA.

 

Finally, the Oycib project is developed in an international context with institutions in Mexico and Spain: Universidad Autónoma del Estado de Hidalgo (UAEH), Barcelona Tech (UPC) and Consejo Nacional de Ciencia y Tecnología (CONACYT).

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Emergency Situation Awareness tool for social media | CSIRO

Emergency Situation Awareness tool for social media | CSIRO | e-Xploration | Scoop.it
Social media channels provide a new, rich source of information from which disaster managers and emergency response agencies can obtain real-time awareness of developing situations.
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Digital Social Research

Digital Social Research | e-Xploration | Scoop.it
Harnessing technology for Social Science Research
luiy's insight:
What is Digital Social Research

New Data. ‘Born-digital’ data sources including social transactional data. Ease of access to secure data.

New Methods. Taking advantage of new data and infrastructure, collaboration, and new forms of interpretative research.

New Capability. Increased capability in tools, resources and services and the emerging
e-Infrastructure.

New Studies. Study of e‑Science, understanding innovation pathways and assessment of impact.

New Practice. Increasing scale and diversity of information and range of collaborative tools, evolving publishing models.

New Scale. Increasing internationalisation and interdisciplinary working.

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What do you mean... open?... from Davecormier.com

What do you mean... open?... from Davecormier.com | e-Xploration | Scoop.it

Via Pierre Levy
luiy's insight:

This post is a mixture of my own research and lots of v. interesting input I’ve gotten from colleagues on Twitter. I’ll make my best effort to mention those people who contributed… feel free to let me know if I’ve forgotten you...

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Pierre Levy's curator insight, April 12, 2013 8:23 AM

The story that I’m trying to tell here is about the values that underpin the word ‘open’. I know many of the people involved in open education/learning/educational resources as deeply principled people who are engaged in the idea of openness for reasons that are important to them. In examining these values i have found two strands: one openness that speaks of valuing the creator/teacher/artifact, and another sense of openness that speaks of the user/learner. Most of us, I would imagine, borrow from both sides. But this story is particularly about how the ideas of around ‘open source’ influence a pull towards valuing the creator over the user, and how that pull might affect the field of learning going forward

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Citizen science: Amateur experts

Equipped with smartphones, computers and do-it-yourself sampling kits, lay volunteers are tweeting about snowfall, questing for comets and measuring the microbes in their guts. They are part of a growing group of 'citizen scientists', networks of non-scientists who help to analyse or collect data as part of a researcher-led project. They learn about science and get a chance to participate, but the scientists involved stand to gain too.

 

Citizen science: Amateur experts

Trisha Gura
Nature 496, 259–261 (11 April 2013) http://dx.doi.org/10.1038/nj7444-259a


Via Complexity Digest
luiy's insight:

Publications using data from citizen science are becoming more common, and even encouraged. Researchers at Princeton University in New Jersey, for example, have used data from Nature's Notebook to expand a model of the timing of leaf-bud bursting from the Harvard Forest area in Massachusetts to the entire eastern seaboard of the United States. The team published its expanded model this year (S.-J. Jeonget al. Geophys. Res. Lett. 40, 359–364; 2013). Not only did peer reviewers welcome the citizen-science data, but one actually gave advice on how to use the citizen-science model more effectively, says Weltzin.

 

If all goes well, citizen science is a way to communicate science, engage in outreach and accomplish research aims. “You are getting the information that you need at the same time that you are getting people involved,” says Weltzin. “It is like playing Whack-a-Mole with all hammers out. You meet all of your objectives at one time.”

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Adam Smith hates Bitcoin

Adam Smith hates Bitcoin | e-Xploration | Scoop.it

Paul Krugman blazes a high-tech path to the 18th century.


Via Luca Baptista
luiy's insight:

There have been many good pieces written on the dubious economics of Bitcoin; I especially liked this one by Neil Irwin. One thing I haven’t seen emphasized, however, is the extent to which the whole concept of having to “mine” Bitcoins by expending real resources amounts to a drastic retrogression — a retrogression that Adam Smith would have scorned.

Smith actually wrote eloquently about the fundamental foolishness of relying on gold and silver currency, which — as he pointed out — serve only a symbolic function, yet absorbed real resources in their production, and why it would be smart to replace them with paper currency:

The gold and silver money which circulates in any country, and by means of which, the produce of its land and labour is annually circulated and distributed to the proper consumers, is, in the same manner as the ready money of the dealer, all dead stock. It is a very valuable part of the capital of the country, which produces nothing to the country. The judicious operations of banking, by substituting paper in the room of a great part of this gold and silver, enable the country to convert a great part of this dead stock into active and productive stock; into stock which produces something to the country. The gold and silver money which circulates in any country may very properly be compared to a highway, which, while it circulates and carries to market all the grass and corn of the country, produces itself not a single pile of either. The judicious operations of banking, by providing, if I may be allowed so violent a metaphor, a sort of waggon-way through the air, enable the country to convert, as it were, a great part of its highways into good pastures, and corn fields, and thereby to increase, very considerably, the annual produce of its land and labour.

And now here we are in a world of high information technology — and people think it’s smart, nay cutting-edge, to create a sort of virtual currency whose creation requires wasting real resources in a way Adam Smith considered foolish and outmoded in 1776.

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BigML - Create a Model then Generate a Prediction

BigML - Create a Model then Generate a Prediction | e-Xploration | Scoop.it
BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

Via Ana Cristina Pratas
luiy's insight:

BigML's predictive models will show you the patterns that are hidden in your data. You can adjust the granularity of the model by using the "live pruning" slider to prune away branches with less data, allowing you to focus on the most common cases, or explore fine details. Click on a decision node and follow the path to increasingly more specific cases by following the "branches" downward. See which values create the split points at every node. At every node, the model shows what the most likely prediction is.

 

 

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Jean Jacoby's curator insight, April 28, 2013 1:33 PM

Not sure that I'd ever need to use this, but wow, it looks impressive!

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When Atoms Meet Bits: Social Media, the Mobile Web and Augmented Revolution

When Atoms Meet Bits: Social Media, the Mobile Web and Augmented Revolution | e-Xploration | Scoop.it
The rise of mobile phones and social media may come to be historically coupled with a growing atmosphere of dissent that is enveloping much of the globe.
luiy's insight:
Abstract: The rise of mobile phones and social media may come to be historically coupled with a growing atmosphere of dissent that is enveloping much of the globe. The Arab Spring, UK Riots, Occupy and many other protests and so-called “flash-mobs” are all massive gatherings of digitally-connected individuals in physical space; and they have recently become the new normal. The primary role of technology in producing this atmosphere has, in part, been to effectively link the on and the offline. The trend to view these as separate spaces, what I call “digital dualism”, is faulty. Instead, I argue that the digital and physical enmesh to form an “augmented reality”. Linking the power of the digital–creating and disseminating networked information–with the power of the physical–occupying geographic space with flesh-and-blood bodies–is an important part of why we have this current flammable atmosphere of augmented revolution. Keywords: augmented reality; collective action; mobile phones; occupy; protest; social media
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Digital mapping at Stanford reveals social networks of 18th-century travelers

Digital mapping at Stanford reveals social networks of 18th-century travelers | e-Xploration | Scoop.it
Through a digital analysis of correspondence from elite tourists in Europe, classicist Giovanna Ceserani is discovering how international travel fostered cultural and academic trends.

Via Pierre Levy, juandoming, Rui Guimarães Lima
luiy's insight:

We live in a world of networks, of nonstop messaging and degrees of separation. So did intellectuals of the early modern age, according to new research at Stanford.

 

During the 18th century, thousands of letters, often on academic subjects like mathematics, were exchanged between scholars across Europe. Wealthy aristocrats and their tutors penned many of those letters when they were on the famed "Grand Tour" of ancient sites in Europe.

 

A pioneering digital visualization project has allowedGiovanna Ceserani, an associate professor of classics, to map the routes of thousands of British and Irish elite travelers who went to Italy in the heyday of the Grand Tour.

Ceserani's digital humanities project, the Grand Tour Travelers, has uncovered unexpectedly close connections between intellectuals, illuminated the rise and fall of cities, and occasionally offered warnings about how visualization can sometimes prove misleading.

Analysis of digital interpretations of the records of over 6,000 travelers from the British Isles illustrate just how small the elite world of tourists in this period was, as well as how, "irrespective of profession and social status, travel abroad seems to have lowered social boundaries and enabled otherwise unlikely connections,"

 

Ceserani said.

The project began with the encoding of a digitized version of the Dictionary of British and Irish Travelers to Italy, 1701-1800, generously supplied by the Paul Mellon Centre in London. For each traveler, Ceserani and her team recorded the sites they visited, the dates of their visits and their birthplace and year, as well as their area of expertise, educational background and social status, among other variables.

 

A scholar with an interest in how classical sites in Italy influenced broader European culture, Ceserani wanted to trace "the actual movements of scholars, of travelers," as they undertook journeys across Europe, often coming into contact with other travelers as they did so.

 

Digital humanities experts within the Mapping the Republic of Letters project, of which Ceserani is a core member, helped Ceserani build the platforms "to place these objects and events onto maps and graphs, visualizing in revealing ways our material."

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Rui Guimarães Lima's curator insight, April 12, 2013 9:07 AM

Afinal ainda faltava mais esta... Bom  fim-de-semana!

Luc Gauvreau's comment, April 12, 2013 11:04 AM
Dommage... Il semble que le site, les données, le module de visualisation de ce projet sont dans un site réservé. Le mouvement de l'open access est conçu et pratiqué différemment, parfois même au sein de la même institution...
Pierre Levy's comment, April 12, 2013 2:50 PM
@luc Gauvreau : Hélas!
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How big data can revolutionize pharmaceutical R & D | McKinsey & Company

How big data can revolutionize pharmaceutical R & D | McKinsey & Company | e-Xploration | Scoop.it
Pharmaceutical R&D suffers from declining success rates and a stagnant pipeline. Big data and the analytics that go with it could be a key element of the cure. A McKinsey & Company article.

Via AnalyticsInnovations
luiy's insight:

Effectively utilizing these data will help pharmaceutical companies better identify new potential drug candidates and develop them into effective, approved and reimbursed medicines more quickly.

Imagine a future where the following is possible:

Predictive modeling of biological processes and drugs becomes significantly more sophisticated and widespread. By leveraging the diversity of available molecular and clinical data, predictive modeling could help identify new potential-candidate molecules with a high probability of being successfully developed into drugs that act on biological targets safely and effectively.Patients are identified to enroll in clinical trials based on more sources—for example, social media—than doctors’ visits. Furthermore, the criteria for including patients in a trial could take significantly more factors (for instance, genetic information) into account to target specific populations, thereby enabling trials that are smaller, shorter, less expensive, and more powerful.Trials are monitored in real time to rapidly identify safety or operational signals requiring action to avoid significant and potentially costly issues such as adverse events2 and unnecessary delays.Instead of rigid data silos that are difficult to exploit, data are captured electronically and flow easily between functions, for example, discovery and clinical development, as well as to external partners, for instance, physicians and contract research organizations (CROs). This easy flow is essential for powering the real-time and predictive analytics that generate business value.
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Cultural Analytics y producción algorítmica de metateoría (i) | francescllorens.eu

Cultural Analytics y producción algorítmica de metateoría (i) | francescllorens.eu | e-Xploration | Scoop.it

Via Pierre Levy
luiy's insight:

Se conceptualice como era del petabyte, era del Big Data, era de los algoritmos o comoneocuantitativismo, lo cierto es que, desde que la evolución de la tecnología ha convertido en despreciable el coste de almacenamiento de la información y ha vuelto más barato guardar todos los datos que decidir cuáles se guardan y cuáles no, estamos asistiendo a una revolución, quizás aún subterránea para la mayoría, pero muy evidente para las empresas tecnológicas y para determinados paradigmas de explicación de la cultura que, como la analítica cultural, se plantean una revisión, o en todo caso una extensión de los modos en que puede ser generado el conocimiento que comúnmente llamamos científico.

Chris Anderson[i] declaró muerto el método científico tradicional, allá por 2008, a manos de los nuevos sistemas algorítmicos de obtención de patrones y modelos predictivos. Concordemos o disintamos, las humanidades digitales, la investigación semiótica, la historia de la literatura, la teoría de la comunicación, la geografía, las visualizaciones de datos, el diseño gráfico y un amplio abanico de disciplinas, tradicionales o embrionarias, se apropian a velocidad creciente de métodos de investigación cuantitativos, que recurren generalmente a visibilizar relaciones numéricas que permanecían ocultas en la producción gigantesca de raw data (datos brutos) a través de Internet.

 

Un repaso, siquiera somero, a los proyectos de Lev Manovich[ii], a las líneas de trabajo de Alejandro Piscitelli[iii] o Carlos Escolari[iv], a la reflexión crítico-filosófica de Pierre Lévy[v] o a los estudios numéricos “distantes” de Franco Moretti[vi] bastarán para darnos cuenta de que estamos ante algo más que una moda o un producto colateral de las tecnologías de tratamiento de datos. Posiblemente debamos reconsiderar el papel de los modelos positivistas en la formación de determinado tipo de conocimiento futuro (y, obviamente, su más que probable ligazón con la ideología), revisar nuestro concepto de determinismo y prepararnos para recibir, combatir, o ambas cosas, una epistemología imparable que, por cierto, procede en algún sentido de aunar lo consciente y lo inconsciente, lo verdadero y lo falso, y que aún está por ver a qué tipo de exigencia validatoria someterá sus hipótesis o qué tolerancia a errores considerará aceptable. A toda teoría de la verdad debe yuxtaponerse la correspondiente teoría del error.

 

Este artículo, sin embargo, no explorará el marco teórico que subyace a la generalización de los modelos algorítmicos, ni particularmente algún aspecto filosófico asociado a él. En cambio, mi objetivo aquí es alcanzar ciertas reflexiones finales a partir del estudio de caso de un instrumento de análisis de patrones basados en información numérica ingente[vii]. Tal instrumento es el software de análisis de referencias bibliográficas de Google denominado Ngram Viewer[viii].

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Pierre Levy's curator insight, April 11, 2013 1:13 PM

En una observación superficial, este gráfico y el anterior presentan un rasgo semejante. Una elevación extrema, en forma de meseta, para uno de los gramas. Ahora es el grama “Einstein” el que muestra valores elevados entre los años 1694 y 1700. (Ngram)