Summary of “The smartphone app that can tell you’re depressed before you know it yourself”

There is something most of those people have in common: a smartphone.
Mindstrong Health is using a smartphone app to collect measures of people’s cognition and emotional health as indicated by how they use their phones.
With details gleaned from the app, Mindstrong says, a patient’s doctor or other care manager gets an alert when something may be amiss and can then check in with the patient by sending a message through the app.
Subjects went home with an app that measured the ways they touched their phone’s display, which Dagum hoped would be an unobtrusive way to log these same kinds of behavior on a smartphone.
Brain-disorder treatment has stalled in part because doctors simply don’t know that someone’s having trouble until it’s well advanced; Dagum believes Mindstrong can figure it out much sooner and keep an eye on it 24 hours a day.
In its current form, the Mindstrong app that patients see is fairly sparse.
“There are people who are high utilizers of health care and they’re not getting the benefits, so we’ve got to figure out some way to get them something that works better.” Actually predicting that a patient is headed toward a downward spiral is a harder task, but Dagum believes that having more people using the app over time will help cement patterns in the data.
About 1,500 of the 2,000 participants also let a Mindstrong keyboard app run on their smartphones to collect data about the ways they type and figure out how their cognition changes throughout the year.

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Summary of “3 Ways to Build a Data-Driven Team”

Foster critical thinking: While much of the current discussions around data focus on the role of technology and AI, it is really the human side of the equation that will remain the biggest differentiator for teams and organizations.
As organization turbocharge their ability to gather more and more data – and it’s not so much about size, but rather about quality – what matters most is having people who can ask the right questions to the data.
Although people will differ in their general predisposition towards critical thinking, you can help them develop whatever potential they have if you put in place the right incentives, give people accurate feedback, and establish an informal and non-hierarchical learning culture where people can share views and ideas.
The implications are obvious: if you want your team to embrace, or at least keep up with, the current data revolution, and approach work in a more evidence-based way, you will need to train them.
Many top universities – including the Ivy Leagues – offer free online courses on AI, data visualization, and data science, and leading corporations in this space, such as Google, offer a wide range of free resources and online courses on AI, analytics, and big data.
Hire the right people: When it comes to the training of quantitative, data-driven, or fact-based reasoning skills, there is well-established evidence for the competencies that predict individuals’ likelihood to learn and display these skills.
More specifically, individuals with higher quantitative or numerical ability levels will find it much easier to pick up any training related to data analytics.
This may sound obvious, but the practical implication is that if you want your team to be quantitatively skilled, your best bet is to avoid hiring people with lower levels of numerical reasoning ability.

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Summary of “Cornell Food Researcher Brian Wansink’s Downfall Raises Larger Questions For Science”

Cornell Food Researcher Brian Wansink’s Downfall Raises Larger Questions For Science : The Salt Brian Wansink made a name for himself producing pithy, palatable studies that connected people’s eating habits with cues from their environment.
Brian Wansink, the head of the Food and Brand Lab at Cornell University, announced last week that he would retire from the university at the end of the academic year.
Less than 48 hours earlier, JAMA, a journal published by the American Medical Association, had retracted six of Wansink’s studies, after Cornell told the journal’s editors that Wansink had not kept the original data and the university could not vouch for the validity of his studies.
In an internal review spurred by a wide range of allegations of research misconduct, a Cornell faculty committee reported a litany of faults with Wansink’s work, including “Misreporting of research data, problematic statistical techniques, failure to properly document and preserve research results, and inappropriate authorship.” Cornell apologized for Wansink’s “Academic misconduct,” removed him from his teaching and research posts, and obligated him to spend the remainder of his time there “Cooperating with the university in its ongoing review of his prior research.”
The most promising postdoctoral students, Wansink wrote, “Unhesitatingly say ‘Yes'” to research projects, “Even if they are not exactly sure how they’ll do it.”
While Wansink is perhaps the most prominent researcher in recent history to be brought down by allegations of p-hacking, this type of academic malpractice is not specific to one lab at one university, say van der Zee and Althouse.
“We never kept the surveys once their data was entered into spreadsheets. None of us have ever heard that a person was expected to keep all of those old surveys,” Wansink told NPR in an email last week.
For all of Wansink’s influence in the field of food and marketing Althouse says he worries that the lessons of Wansink’s mistakes will not be a wakeup call to the broader scientific community.

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Summary of “How Big Data Is Changing Genetic Research”

New biomedical techniques, like next-generation genome sequencing, are creating vast amounts of data and transforming the scientific landscape.
This is true everywhere, says Gil McVean, a professor of statistical genetics at the University of Oxford’s Big Data Institute.
Now you just collect a lot of data and let the data decide what the hypothesis should be, says McVean.
‘ “We got so good at producing data,” says Nicole Wheeler, a data scientist at the Sanger Institute who looks at the genomes of pathogenic bacteria, “That we ended up with too much of it.” McVean agrees.
“The growth of biomedical data capture – through sequencing genomes, but also through medical imaging or digital pathology – is much faster than that. We’re super-Moore’s-Law-ing in biomedical data.”
“We had lots of data, but we didn’t know what to do with it. So algorithms had to be invented on the fly, to deal with the data and maximize it,” she continues.
His work is in the field of epigenetics, looking at how the chemical environment of a cell affects the expression of genes; he sequences RNA, the messenger molecule that allows DNA to be read and proteins made, to see how it differs from cell to cell.
It may be too much to hope that big data will help us all live for ever.

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Summary of “The American Dream Is Harder To Find In Some Neighborhoods”

The American Dream Is Harder To Find In Some Neighborhoods A new data tool finds a strong correlation between where people grew up and their chances of climbing the economic ladder.
Does the neighborhood you grow up in determine how far you move up the economic ladder?
At first glance, it looks a lot like a Google map, where users can see the whole country, or zoom in to local neighborhoods.
The difference is in the amount of data that pops up when a neighborhood is highlighted.
It’s located in a majority white neighborhood not far from downtown Charlotte, but the school’s population doesn’t reflect the neighborhood.
That’s because many white students attend private schools or public schools outside their neighborhoods.
The Sedgefield neighborhood is more affluent than a nearby majority black neighborhood called Southside Park.
Harvard’s Chetty says he hopes the Opportunity Atlas will help communities across the country revive the American dream in their neighborhoods.

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Summary of “Exclusive: Tim Berners-Lee tells us his radical new plan to upend the”

5 minute Read. Last week, Tim Berners-Lee, inventor of the World Wide Web, asked me to come and see a project he has been working on almost as long as the web itself.
It’s a crisp autumn day in Boston, where Berners-Lee works out of an office above a boxing gym.
For years now, Berners-Lee and other internet activists have been dreaming of a digital utopia where individuals control their own data and the internet remains free and open.
Like with Netscape, Berners-Lee hopes Inrupt will be just the first of many companies to emerge from Solid.
The app, using Solid’s decentralized technology, allows Berners-Lee to access all of his data seamlessly-his calendar, his music library, videos, chat, research.
One idea Berners-Lee is currently working on is a way to create a decentralized version of Alexa, Amazon’s increasingly ubiquitous digital assistant.
Berners-Lee believes Solid will resonate with the global community of developers, hackers, and internet activists who bristle over corporate and government control of the web.
For now, the company consists of Berners-Lee; his partner John Bruce, who built Resilient, a security platform bought by IBM; a handful of on-staff developers contracted to work on the project; and a community of volunteer coders.

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Summary of “Google’s Tool to Help Cities Fight Climate Change”

The company has only released estimates for five cities, including Pittsburgh, Buenos Aires, and Mountain View, California.
As part of this initiative, Google says it will also release its proprietary estimates of a city’s annual driving, biking, and transit ridership, generated from information collected by its popular mapping apps, Google Maps and Waze.
Google made the announcement earlier this month as part of the Global Climate Action Summit in San Francisco.
The summit, organized in part by California Governor Jerry Brown, was meant to encourage states and cities that have advanced climate policy since President Donald Trump took office.
These local programs do much, but they have not replaced climate policies revoked by Trump: A recent report from Yale and a number of European think tanks found that these “Subnational” programs could make up about half of the United States’ pledged carbon cuts under the Paris Agreement.
Google has framed the new project, called the Environmental Insights Explorer, as a way for leaders to focus and improve local climate programs.
The explorer remains a better tool for getting a glancing sense of a city’s carbon emissions than it is for making meticulous policy.
Google is also hampered by the age and quality of some data: To estimate how much carbon is emitted to power a given city, it must use a six-year-old data set from the EPA. But it can still provide useful information.

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Summary of “An insurance company wants you to hand over your Fitbit data so it can make more money. Should you?”

Life insurance company John Hancock made a splash last week with the news that soon all its policies would come bundled with the option to let the company track your fitness – via either a website and app, or through the use of a fitness tracker like an Apple Watch or Fitbit.
The move underscores how fitness tracker data is an as-yet largely untapped gold mine for businesses – particularly in industries like insurance, whose financial bottom line directly depends on the health of their customers.
The published research on Fitbits and similar devices has yet to uncover a clear link between fitness tracking and fitness, to say nothing of longevity and mortality, or of insurance companies’ profits.
A randomized controlled trial based in Singapore, one of the largest such studies on fitness trackers done to date, found in 2016 “No evidence of improvements in health outcomes” relative to a control group, among people who were randomly assigned to use a Fitbit.
A similar study published in the Journal of the American Medical Association in 2016 found that among 470 overweight young adults, people randomly assigned a fitness tracker actually lost slightly less weight over a two-year period than the group that did not receive a tracker.
The big one is this: The more active people are, the more their insurance premiums go down, up to a savings of 15 percent on annual premium costs, or $300 a year for a typical term life insurance policyholder.
In the long run, there may be real benefits for consumers willing to share their fitness data with insurers, particularly if the practice spreads to health insurance providers, as seems inevitable at this point.
People who track their fitness may end up in better health and receive cheaper insurance relative to those who do not.

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Summary of “A forgotten 200-year-old guide to color, redesigned for the internet”

The nomenclature of colors we use today is really a machine language-numerical hex codes crafted to communicate with software on computers and printers.
Before the age of CMYK and RBG artists and scientists created their own languages for talking about and categorizing color.
Published in 1814 by the painter Patrick Syme based on the work of geologist Abraham Gottlob Werner, Werner’s is a working dictionary of colors found in the natural world, complete with swatches and examples of animals, minerals, and vegetables where a particular hue could be found in the wild.
Compared to the precise, computerized way color is defined today, Werner’s is less than practical, yet there’s something lovely about its definitions.
Earlier this year, the Smithsonian rekindled public interest in Werner’s after it reissued the 200-year-old book-and now, the tome has a digital facsimile thanks to the designer Nicholas Rougeux, who recently launched an interactive version with additions like data visualizations of its 110 colors and internet-sourced photographs of the animals and minerals that the book references.
Thanks to the Internet Archive’s free scans of the book, Rougeux was able to extract its data and rethink its presentation for contemporary times.
The finished product reframes 18th-century color nomenclature with contemporary data visualization and web design-it’s calming to breeze through its contents, but also a usable tool for designers and artists.
On a public Google Doc, Rougeux compiled Syme’s colors and added a few 21st-century definitions of his own: The hex codes for each of the 18th-century hues, from Skimmed Milk White to Veinous Blood Red..

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