A new AI program observed physical phenomena and uncovered relevant variables—a necessary precursor to any physics theory.
All physical laws are described as mathematical relationships between state variables. These variables give a complete and non-redundant description of the relevant system. However, despite the prevalence of computing power and artificial intelligence, the process of identifying the hidden state variables themselves has resisted automation. Most data-driven methods for modeling physical phenomena still rely on the assumption that the relevant state variables are already known. A longstanding question is whether it is possible to identify state variables from only high-dimensional observational data.
Scientists now created a principle for determining how many state variables an observed system is likely to have, and what these variables might be. They were able to demonstrate the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables.
The world is expanding and so is the data around. The concept of big data has never looked more fascinating than now. Businesses are now looking for patterns to implement big data technology directly into their business applications and software. The term has moved from being just a buzzword to one of the most essential components of a company’s IT infrastructure.
Organizations are taking the next step to identify the current as well as future developments in big data deployments. Massive data sets from an ever-expanding list of sources are what define big data in the simplest way. Organizations are trying to create a culture where they can embed the technology in applications so that it can truly empower their business. Truly, big data has taken the business world by storm, but what next! How big is it going to get; will businesses using data see productivity benefits, are there any security concerns? Questions abound and so does their answers. However, differentiating between what will sustain and what will pass will save you time and most importantly, a wrong investment.
Since long, big data solutions, has been introduced as massive sets built around centralized data lakes. The reasons were quite simple. The data became difficult to duplicate and management was easier. In 2016 though, organizations are thinking of moving to distributed big data processing, not to mention managing multiple data location centers and multiple devices. Further, the continued growth of the Internet of Things is increasingly going to affect the deployment of distributed data processing frameworks.
The ever-expanding list of resources continues generating larger and larger volumes of data. There are a lot of data and there is going to be more. Data-driven companies like Google are stressing on data analysis and how it must be grounded in sound values and practices.
Terabytes, petabytes, or exabytes - big data means huge amounts of data getting transferred between applications. The consistent back and forth of information is creating huge security concerns. Intrusions are detected on a daily basis and what’s worse is organizations are keeping security as their second or third priority. Hackers can breach any database if your security system gets easily defeated.
Organizations with weak protection solutions will end up being victims of thousands of hackers out there. Get a top-notch anti-malware software program that will safeguard your perimeter and it won’t be pregnable.
Experts debate when it comes to quality of data. According to them, big isn’t necessary whatsoever and business doesn’t use even a fraction of the data they have access to. The idea is moving from big to fast and actionable data that answer questions and produces effective uses of the data.
Big data is going to get bigger no matter what. Don’t get left behind and adopt it as soon as possible. For effective data management and protection from intrusion, make sure you are securing your enterprise with top performance anti-malware solutions. If big data is expanding, then so has to be the scale, speed, security and integration requirements of your organization.
Members of the public can search a newly released database of 1,600 stars to find signs of undiscovered exoplanets. The dataset, taken over two decades by the W.M. Keck Observatory in Hawaii, comes with an open-source software package and an online tutorial.
The search for planets beyond our solar system is about to gain some new recruits. Just recently, a team that includes MIT and is led by the Carnegie Institution for Science has released the largest collection of observations made with a technique called radial velocity, to be used for hunting exoplanets. The huge dataset, taken over two decades by the W.M. Keck Observatory in Hawaii, is now available to the public, along with an open-source software package to process the data and an online tutorial.
By making the data public and user-friendly, the scientists hope to draw fresh eyes to the observations, which encompass almost 61,000 measurements of more than 1,600 nearby stars.
“This is an amazing catalog, and we realized there just aren’t enough of us on the team to be doing as much science as could come out of this dataset,” says Jennifer Burt, a Torres Postdoctoral Fellow in MIT’s Kavli Institute for Astrophysics and Space Research. “We’re trying to shift toward a more community-oriented idea of how we should do science, so that others can access the data and see something interesting.”
Burt and her colleagues have outlined some details of the newly available dataset in a paper to appear in The Astronomical Journal. After taking a look through the data themselves, the researchers have detected over 100 potential exoplanets, including one orbiting GJ 411, the fourth-closest star to our solar system. “There seems to be no shortage of exoplanets,” Burt says. “There are a ton of them out there, and there is ton of science to be done.”
Sharing genetic information from millions of cancer patients around the world could revolutionize cancer prevention and care, according to a paper in Nature Medicine by the Cancer Task Team of the Global Alliance for Genomics and Health (GA4GH). Hospitals, laboratories and research facilities around the world hold huge amounts of this data from cancer patients, but it’s currently held in isolated “silos” that don’t talk to each other, according to GA4GH, a partnership between scientists, clinicians, patients, and the IT and Life Sciences industry, involving more than 400 organizations in over 40 countries. GA4GH intends to provide a common framework for the responsible, voluntary and secure sharing of patients’ clinical and genomic data.
“Imagine if we could create a searchable cancer database that allowed doctors to match patients from different parts of the world with suitable clinical trials,” said GA4GH co-chair professor Mark Lawler, a leading cancer expert fromQueen’s University Belfast. “This genetic matchmaking approach would allow us to develop personalized treatments for each individual’s cancer, precisely targeting rogue cells and improving outcomes for patients.
“This data sharing presents logistical, technical, and ethical challenges. Our paper highlights these challenges and proposes potential solutions to allow the sharing of data in a timely, responsible and effective manner. We hope this blueprint will be adopted by researchers around the world and enable a unified global approach to unlocking the value of data for enhanced patient care.”
GA4GH acknowledges that there are security issues, and has created a Security Working Group and a policy paper that documents the standards and implementation practices for protecting the privacy and security of shared genomic and clinical data.
Examples of current initiatives for clinico-genomic data-sharing include the U.S.-based Precision Medicine Initiative and the UK’s 100,000 Genomes Project, both of which have cancer as a major focus.
Pharmaceutical companies spend billions testing prospective drugs by conducting “wet lab” experiments that can take years to complete. But what if the same results could be obtained in a matter of minutes by running computer model simulations instead? A Silicon Valley startup says it has created a novel machine learning algorithm that does just that.
TwoXar (pronounced “two-czar”) was founded last year by two men both named Andrew Radin (more on that later). The Radins were interested in using advances in data science and large-scale computing to speed up the pace of drug discovery, which would give pharmaceutical companies better candidates for clinical studies.
“The traditional method of drug discovery is you examine a disease, you devise that there’s a protein of interest to you, and if you can regulate an activity with a drug, then perhaps it will help out,” TwoXar CEO Andrew A. Radin tells Datanami. “That’s a multi-year process to do all that work.”
TwoXar’s DUMA Drug Discovery platform can replace years’ worth of biological lab work with a digital equivalent that leans heavily on data science and leverages the incredible amount of medical, biological, and drug data that already exists in the public sphere.
“Our core IP [intellectual property], if you will, is this ability to take extremely diverse data sets and draw relationships between those data sets,” Radin says. “We’re combining clinical data in combination with gene expression assays, protein interaction networks, drug protein binding databases, and physical attributes about the molecules themselves.”
The hardest part is actually obtaining the relevant data. Most of the data is in the public sphere, thanks to the billions in spent by the NIH, the FDA, the European Union, and the Canadian government to back primary medical, chemical, and biological research. Once the right data is loaded into the system, it’s as simple as pressing the “go” button and waiting for the system to spit out its answer.
When looking at this map there area few things that stick out to me and not just the colors. Fistly what I founf interesting was that South America in relation to where we live is quite different. For example, The US economic status is High Class at $12195 or more for most of the East and West Coast and then it is dull in the middle. These facts compared to South America where they are mostly upper middle class at around $3946-12185 and a portion of them are the lower middle class which rings in at around $886-3945.
On map 33, it shows the religious borders map of the different religions that are occupying certain areas of the Middle East. The area of Baghdad and east is mostly Shiite Islam and west of Baghdad is Sunni Islam. What I found to be most interesting is that even though Jerusalem is surrounded by many different religions they still celebrate Judaism. They are religiously protected by its borders. There is some sign of Sunni Islam being practices within their borders but it is mostly dominated by Judaism.
Networks are typically visualized with force-based or spectral layouts. These algorithms lack reproducibility and perceptual uniformity because they do not use a node coordinate system. The layouts can be difficult to interpret and are unsuitable for assessing differences in networks.
To address these issues, we introduce hive plots ( http://www.hiveplot.com ) for generating informative, quantitative and comparable network layouts. Hive plots depict network structure transparently, are simple to understand and can be easily tuned to identify patterns of interest. The method is computationally straightforward, scales well and is amenable to a plugin for existing tools.
Scientists at the National Cancer Institute (NCI) have created a dataset of cancer-specific genetic coding variants that includes six billion data points linking drugs with the changes in the genome.
This source of big data, reported to be the most extensive cancer pharmacology database in the world, provides an opportunity for data mining in cancer drug discovery, and could help researchers understand why patients’ responses to cancer drugs vary and how tumours become resistant. It also has potential to help move forward the quest for personalised medicine.
The team carried out whole-exome sequencing of the 60 cell lines in the NCI-60 human cancer cell line panel and catalogued the genetic coding variants, including type I variants (found in the normal population) and type II variants (cancer-specific). The NCI-60 cell lines have been studied extensively, and include cells from nine tissues of origin, including breast, ovary, prostate, colon, lung, kidney, brain, blood, and skin. They used algorithms to predict the sensitivity of the cells with the type II variants and 103 approved anticancer drugs and 207 drugs in development, to see if this could be used to predict response.
According to James Doroshow, the director of the NCI’s Division of Cancer Treatment and Diagnosis, in an interview in Shots, thousands of drugs have been screened using the NCI-60 panel, to see their impact on the cells. The researchers have also analysed 5000 different combinations of approved drugs to see if they can find drugs that work well together.
In an interview with the American Association of Cancer Research, Yves Pommier, chief of the Laboratory of Molecular Pharmacology at the NCI in Bethesda, explained that the team is making the data set public.
“Opening this extensive data set to researchers will expand our knowledge and understanding of tumorigenesis, as more and more cancer-related gene aberrations are discovered,” Pommier said in the interview. “This comes at a great time, because genomic medicine is becoming a reality, and I am very hopeful this valuable information will change the way we use drugs for precision medicine.”
The research was published in Cancer Research, and the genomic data is available through the CellMiner website, amongst others.
While not involved in this study, GenoKey focuses on data mining and analytics, and has used a combination of genetic and clinical data, along with combinatorial analysis, to find links between genetic changes and bipolar disorder. The NCI’s dataset of cancer-specific genetic coding variants, and GenoKey’s work, show the power of using big data and healthcare analytics in medical and biopharma research, particularly when combining genetic and clinical data.
Acquista Online La Prescrizione Di Perdita Di Peso Crediamo che i farmaci a volte possano essere molto urgenti da assumere. Se hai urgente bisogno di farmaci, possiamo anche fornirti una consegna espressa,
It wasn’t malicious. The file itself was the size of a small JPEG. It was given the absolute lowest priority. And it was set to self-destruct if anything went wrong. But this small file allowed one single hacker to measure the Internet activity of nearly half a million connected devices around the world, then share the results with everyone.
How was this even possible? The "hacker" barely hacked anything. In reality, they gained access to all these systems because each had the default "root" set as a password. With this access in hand, they ran several tests focusing on Internet structure and activity. And what they created from all this data is a spectacular map that captures a day in the life of the Internet (and all of its users).
David McCandlees, the author of the book Information is Beautiful celebrates great data visualization and information design work through the Information is Beautiful Awards.
Together with a jury of experts like Brian Eno, Paola Antonelli, Maria Popova, Simon Rogers and Aziz Kami, he has curated a unique selection of 300 designs and a short list of finalists in the following categories:
» Data visualization– A singular visualisation of data or information.» Infographic – Using multiple data visualisations in service to a theme or story
» Interactive visualization – Any viz where you can dynamically filter or explore the data.
» Data journalism – A combination of text and visualizations in a journalistic format.
» Motion infographic – Moving and animated visualizations along a theme or story.
» Tool or website – Online tools & apps to aid datavizzing.
The selection itself is worth a tour of the site and of this initiative.
Data visualizations and infographics can make complex datasets easier to understand and comprehend. By creating a graphical represenatation of data and statistics, complicated concepts and information can make more sense in less time.
Many visualizations focus on representing a specific set of data or statistical information. Others focus on less-concrete topics, providing a visual representation of abstract concepts. Generally speaking, the first type appear more like graphs or charts and the latter are often more creative and imaginative.
But visualizations and infographics can be used poorly, too. Putting in too much information (or not enough), using improper formats for the information provided, and other failures are common. Many useful resources for infographics and data visualization are compiled. Most are galleries of effective graphics though some also provide how-to information for information designers.
Also check out the intelligent continuously updated wall charts based on scoop.it information:
Fritjof Capra, in his book ‘The Hidden Connections’ applies aspects of complexity theory, particularly the analysis of networks, to global capitalism and the state of the world; and eloquently argues the case that social systems such as organisations and networks are not just like living systems – they are living systems. The concept and theory of living systems (technically known as autopoiesis) was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela.
This is a complete version of a ‘long-blog’ written by Al Kennedy on behalf of ‘The Nature of Business’ blog and BCI: Biomimicry for Creative Innovation www.businessinspired...
Circos is a software package conceived and created by Martin Krzywinski to visualize large amounts of data in a circular layout.
Circos is ideal for creating publication-quality infographics and illustrations with a high data-to-ink ratio, richly layered data and pleasant symmetries.
(You will need Perl to run Circos. Perl is an interpretive language, like Python or Ruby. It is available for nearly every operating system and if you're on UNIX or Mac OS X, you very likely already have it installed. Perl 5.8.x, or newer, is recommended.)
The latest in high-density ultra-durable data storage has been perfected over billions of years by nature itself.
Now ‘Smoke on the Water’ is making history again. This September, it was one of the first items from the Memory Of the World archive to be stored in the form of DNA and then played back with 100% accuracy. The project was a joint effort between the University of Washington, Microsoft and Twist Bioscience, a San Francisco-based DNA manufacturing company.
The demonstration was billed as a ‘proof of principle’ – which is shorthand for successful but too expensive to be practical. At least for now. Many pundits predict it’s just a matter of time till DNA pips magnetic tape as the ultimate way to store data. It’s compact, efficient and resilient. After all, it has been tweaked over billions of years into the perfect repository for genetic information. It will never become obsolete, because as long as there is life on Earth, we will be interested in decoding DNA. “Nature has optimised the format,” says Twist Bioscience’s chief technology officer Bill Peck.
It’s a big problem that’s getting bigger by the minute. According to a 2016 IBM Marketing Cloud report, 90% of the data that exists today was created in just the past two years. Every day, we generate another 2.5 quintillion (2.5 × 1018) bytes of information. It pours in from high definition video and photos, Big Data from particle physics, genomic sequencing, space probes, satellites, and remote sensing; from think tanks, covert surveillance operations, and Internet tracking algorithms. EVERY DAY, WE GENERATE ANOTHER 2.5 QUINTILLION BYTES OF INFORMATION.
Right now all those bits and bytes flow into gigantic server farms, onto spinning hard drives or reels of state-of-the-art magnetic tape. These physical substrates occupy a lot of space. Compare this to DNA. The entire human genome, a code of three billion DNA base pairs, or in data speak, 3,000 megabytes, fits into a package that is invisible to the naked eye – the cell’s nucleus. A gram of DNA — the size of a drop of water on your fingertip — can store at least the equivalent of 233 computer hard drives weighing more than 150 kilograms. To store the all the genetic information in a human body — 150 zettabytes — on tape or hard drives, you’d need a facility covering thousands, if not millions of square feet.
And then there’s durability. Of the current storage contenders, magnetic tape has the best lifespan, at about 10-20 years. Hard drives, CDs, DVDs and flash drives are less reliable, often failing within five to ten years. DNA has proven that it can survive thousands of years unscathed. In 2013, for example, the genome of an early horse relative was reconstructed from DNA from a 700,000-year-old bone fragment found in the Alaskan permafrost.
If you take 10 communities and run a simulation, it’s easy to see why we need as many members of the ‘herd’ as possible to get vaccines – before it’s too late.
Measles are back in the US – and spreading. More than 100 cases across 14 states and Washington DC have been confirmed by US health officials since an outbreak began at Disneyland last December. With a majority of those infections in unvaccinated people, widespread blame – from Washington to the rest of the world – has fallen on parents who chose not to vaccinate their children.
Part of the problem, according to Dr Elizabeth Edwards, professor of pediatrics and director of the Vanderbilt Vaccine Research Program, is just that: vaccination is understood by many as an individual choice, when science makes clear that the choice – to vaccinate or not to vaccinate – can affect an entire community.
“When you immunize your child, you’re not only immunizing your child. That child’s immunization is contributing to the control of the disease in the population,” Edwards explained. That sheltering effect is called herd immunity: a population that is highly immunized makes for a virus that can’t spread easily, providing protection to the community – or the herd – as a whole.
Despite the high overall measles vaccination rate in the US, vaccine skeptics – and their unimmunized kids – often congregate in like-minded communities, creating pockets of under-immunization.
California, where the bulk of current measles cases can still be found, is a prime example. It's one of 20 states that allow parents to skip vaccination based on their personal, philosophical beliefs – even though legislators introduced a bill that would ban such an opt-out provision.
A new study reveals the full extent of globalization in our food supply. More than two-thirds of the crops that underpin national diets originally came from somewhere else — often far away.
Previous work by the same authors had shown that national diets have adopted new crops and become more and more globally alike in recent decades. The new study shows that those crops are mainly foreign.
The idea that crop plants have centers of origin, where they were originally domesticated, goes back to the 1920s and the great Russian plant explorer Nikolai Vavilov. He reasoned that the region where a crop had been domesticated would be marked by the greatest diversity of that crop, because farmers there would have been selecting different types for the longest time. Diversity, along with the presence of that crop's wild relatives, marked the center of origin.
The Fertile Crescent, with its profusion of wild grasses related to wheat and barley, is the primary center of diversity for those cereals. Thai chilies come originally from Central America and tropical South America, while Italian tomatoes come from the Andes.
Khoury and his colleagues extended Vavilov's methods to look for the origins of 151 different crops across 23 geographical regions. They then examined national statistics for diet and food production in 177 countries, covering 98.5 percent of the world's population.
One of the reasons why IoT has gained momentum in the recent past is the rise of cloud services. Though the concept of M2M existed for over a decade, organizations never tapped into the rich insights derived from the datasets generated by sensors and devices. Existing infrastructure was just not ready to deal with the massive scale demanded by the connected devices architecture. That’s where cloud becomes an invaluable resource for enterprises.
With abundant storage and ample computing power, cloud became an affordable extension to the enterprise data center. The adoption of cloud resulted in increased usage of Big Data platforms and analytics. Organizations are channelizing every bit of data generated from a variety of sources and devices to the cloud where it is stored, processed, and analyzed for deriving valuable insights. The combination of cloud and Big Data is the key enabler of Internet of Things. IoT is all set to become the killer use case for distributed computing and analytics.
Cloud service providers such as Amazon, Google, IBM,Microsoft, Salesforce, and Oracleare offering managed IoT platforms that deliver the entire IoT stack as a service. Customers can on-board devices, ingest data, define data processing pipelines that analyze streams in real-time, and derive insights from the sensor data. Cloud-based IoT platforms are examples of verticalized PaaS offerings, which are designed for a specific use case.
While cloud is a perfect match for the Internet of Things, not every IoT scenario can take advantage of it. Industrial IoT solutions demand low-latency ingestion and immediate processing of data. Organizations cannot afford the delay caused by the roundtrip between the devices layer and cloud-based IoT platforms. The solution demands instant processing of data streams with quick turnaround. For example, it may be too late before the IoT cloud shuts down an LPG refilling machine after detecting an unusual combination of pressure and temperature thresholds. Instead, the anomaly should be detected locally within milliseconds followed by an immediate action trigged by a rule. The other scenario that demands local processing is healthcare. Given the sensitivity of data, healthcare companies don’t want to stream critical data points generated by life-saving systems. That data needs to be processed locally not only for faster turnaround but also for anonymizing personally identifiable patient data.
The demand for distributing the IoT workloads between the local data center and cloud has resulted in an architectural pattern called Fog computing. Large enterprises dealing with industrial automation will have to deploy infrastructure within the data center that’s specifically designed for IoT. This infrastructure is a cluster of compute, storage, and networking resources delivering sufficient horsepower to deal with the IoT data locally. The cluster that lives on the edge is called the Fog layer. Fog computing mimics cloud capabilities within the edge location, while still taking advantage of the cloud for heavy lifting. Fog computing is to IoT what hybrid cloud is to enterprise IT. Both the architectures deliver best of both worlds.
Cisco is one of the early movers in the Fog computing market. The company is credited with coining the term even before IoT became a buzzword. Cisco positioned Fog as the layer to reduce the latency in hybrid cloud scenarios. With enterprises embracing converged infrastructure in data centers and cloud for distributed computing, Cisco had vested interest in pushing Fog to stay relevant in the data center. Almost after five years of evangelizing Fog computing with little success, Cisco finally found a legitimate use case in the form of IoT.
Two researchers at Cold Spring Harbor Laboratory challenge the most recent advances in this Big Data analysis, using a classic mathematical concept to tackle the outstanding problems in this field. Mutual information is able to uncover patterns in large lists of numbers, revealing entirely new, unexpected patterns.
Big Data is everywhere, and we are constantly told that it holds the answers to almost any problem we want to solve. Companies collect information on how we shop, doctors and insurance companies gather our medical test results, and governments compile logs of our phone calls and emails. In each instance, the hope is that critical insights are hidden deep within massive amounts of information, just waiting to be discovered.
But simply having lots of data is not the same as understanding it. Increasingly, new mathematical tools are needed to extract meaning from enormous data sets. In work published online today, two researchers at Cold Spring Harbor Laboratory (CSHL) now challenge the most recent advances in this field, using a classic mathematical concept to tackle the outstanding problems in Big Data analysis.
What does it mean to analyze Big Data? A major goal is to find patterns between seemingly unrelated quantities, such as income and cancer rates. Many of the most common statistical tools are only able to detect patterns if the researcher has some expectation about the relationship between the quantities. Part of the lure of Big Data is that it may reveal entirely new, unexpected patterns. Therefore, scientists and researchers have worked to develop statistical methods that will uncover these novel relationships.
In 2011, a distinguished group of researchers from Harvard University published a highly influential paper in the journal Science that advanced just such a tool. But in a paper published today in Proceedings of the National Academy of Sciences, CSHL Quantitative Biology Fellow Justin Kinney and CSHL Assistant Professor Gurinder "Mickey" Atwal demonstrate that this new tool is critically flawed. "Their statistical tool does not have the mathematical properties that were claimed," says Kinney.
Kinney and Atwal show that the correct tool was hiding in plain sight all along. The solution, they say, is a well known mathematical measure called "mutual information," first described in 1948. It was initially used to quantify the amount of information that could be transmitted electronically through a telephone cable; the concept now underlies the design of the world's telecommunications infrastructure. "What we've found in our work is that this same concept can also be used to find patterns in data," Kinney explains. "This beautiful mathematical concept has the potential to greatly benefit modern data analysis, in biology and in biology and many other important fields.
ZocDoc, Healthgrades, Vitals, Yelp and other sites can tell you what patients think of their doctors. But finding out in any aggregate way what doctors think of their peers has been much harder, if not near impossible, for patients — up until now.
By accessing information in government databases through FOIA (Freedom of Information Act) requests, healthcare innovators are now able to share connections between doctors that are based on millions of physician referrals — a valuable indicator of who doctors hold in esteem.
HealthTap, a Palo Alto-based startup that connects patients with an online network of 17,000 doctors, also this week launched a new feature based partly on Trotter’s data. Called “DOConnect,” it combines Trotter’s Medicare data with physician data from its own site and other sources to give patients a new window into their doctors’ networks.
“This isn’t just friendships and business connections. This is who doctors trust,” said HealthTap co-founder and CEO Ron Gutman. “If you could know who your doctor’s doctor is, if you knew who they would choose, this lets you see that for the first time.”
Using Circos, a circular visualization software, Cristian Ilies Vasile and Martin Krzywinski created these vibrant representations of π, φ and e.
Cristian Ilies Vasile had the idea of connecting each digit of π to its successive digit with links to the position of the numerically corresponding segments. Martin Krzywinski added to Vasile’s visualization “by showing the transition probabilities for each digit across bins of 10 digits” and did this for φ and e as well for the first 1,000 digits, followed by the first 2,000 digits.
Next, Krzywinski created a lovely distribution plot of the numbers using red-yellow-blue Brewer palette to map their digits and plot them on an Archimedean spiral.
Until recently, the wet lab has been a crucial component of every biologist. Today's advances in the production of massive amounts of data and the creation of machine-learning algorithms for processing that data are changing the face of biological science—making it possible to do real science without a wet lab. David Heckerman shares several examples of how this transformation in the area of genomics is changing the pace of scientific breakthroughs.
This 36 min video is well worth the time spent - to get an idea (hopefully a transferrable one) about Big Data and the frontiers of science. In this case both "wet lab" (test tubes microscopes) and "dry lab" (computer modeling with machine learning) and needed and so is content as well as computational literacy.
ZocDoc, Healthgrades, Vitals, Yelp and other sites can tell you what patients think of their doctors. But finding out in any aggregate way what doctors think of their peers has been much harder, if not near impossible, for patients — up until now. By accessing information in government databases through FOIA (Freedom of Information Act) requests, healthcare innovators are now able to share connections between doctors that are based on millions of physician referrals — a valuable indicator of who doctors hold in esteem.
Last month, Fred Trotter, a self-identified “hacktivist,” revealed that he had obtained a dataset of Medicare physician referrals through a FOIA request and was making the initial data available to those who supported a Medstartr crowdfunding campaign meant to build out his “DocGraph” and make it freely available. This week, he announced that he not only blew past his $15,000 funding goal, but was launching a second campaign to integrate his current data with an additional dataset.
The new tool, which reflects 25 million doctor referral connections, enables patients to see how many doctors are linked to a particular doctor, as well as their locations. As patients search for new physicians and specialists, being able to see who their current doctors are linked with could help them decide who to visit. It also gives doctors an opportunity to build online networks that reflect their offline networks, Gutman said. In a post about his “DocGraph” project, Trotter said that his data wasn’t strictly a “referral” data set because, in some cases, doctors might be linked through a patient they both happened to see at the same time, not through an active referral. But Gutman emphasized that HealthTap’s DOConnect considered more than Medicare referrals in mapping connections between doctors.
Located on Jaffe Drive at Lincoln Center in New York, the THINK exhibit combined three unique experiences to engage visitors in a conversation about how we can improve the way we live and work.
The IBM data wall was the introduction to the Think Exhibit at the Lincoln Center. This exhibit celebrated its centennial year, and 100 years of human progress.
The wall aimed to educate the public about five areas of interest to the New York community. These included air quality, water waste, potential solar energy, fraud detection, and traffic sensing.
Visit the portfolio link to view detail images of the Data Wall, as well as concept sketches and explanations for the design for the solar and traffic sections.
The Internet Map [ www.internet-map.net ] encompasses over 350,000 websites based in 196 countries, which are clustered according to about 2 million mutual links between them. Developed by a small team of Russian enthusiasts, the interactive Internet map is an "attempt to look into the hidden structure of the network, fathom its colossal scale, and examine that which is impossible to understand from the bare figures of statistics." Every circle on the map stands for a unique website, with its size determined by website web traffic. Its color depends on the country of origin, with red for Russia, yellow for China, purple for Japan, and light-blue for the US.
Described as a “knowledge collider,” and now with a pledge of one billion euros from the European Union, the Living Earth Simulator is a new big data and supercomputing project that will attempt to uncover the underlying sociological and psychological laws that underpin human civilization. In the same way that CERN’s Large Hadron Collider smashes together protons to see what happens, the Living Earth Simulator (LES) will gather knowledge from a Planetary Nervous System (PNS — yes, really) to try to predict societal fluctuations such as political unrest, economic bubbles, disease epidemics, and so on.
Orchestrated by FuturICT, which is basically a consortium of preeminent scientists, computer science centers around the world, and high-power computing (HPC) installations, the Living Earth Simulator hopes to correlate huge amounts of data — including real-time sources such as Twitter and web news — and extant, but separate approaches currently being used by other institutions, into a big melting pot of information. To put it into scientific terms, the LES will analyze techno-socio-economic-environmental (!) systems. From this, FuturICT hopes to reveal the tacit agreements and hidden laws that actually govern society, rather than the explicit, far-removed-from-reality bills and acts that lawmakers inexorably enact.
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