The sharing economy comes to scientific research


 at the Conversation: “…to perform top-quality and cost-effective research, scientists need these technologies and the technical knowledge of experts to run them. When money is tight, where can scientists turn for the tools they need to complete their projects?

Sharing resources

An early solution to this problem was to create what the academic world calls “resource labs” that specialize in one or more specific type of science experiments (e.g., genomics, cell culture, proteomics). Researchers can then order and pay for that type of experiment from the resource lab instead of doing it on their own.

By focusing on one area of science, resource labs become the experts in that area and do the experiments better, faster and cheaper than most scientists could do in their own labs. Scientists no longer stumble through failed experiments trying to learn a new technique when a resource lab can do it correctly from the start.

The pooled funds from many research projects allow resource labs to buy better and faster equipment than any individual scientist could afford. This provides more researchers access to better technology at lower costs – which also saves taxpayers money, since many grants are government-backed….

Connecting people on a scientific Craigslist

This is a common paradox, with several efforts under way to address it. For example, MIT has created several “remote online laboratories” running experiments that can be controlled via the internet, to help enrich teaching in places that can’t afford advanced equipment. Harvard’s eagle-i system is a directory where researchers can list information, data and equipment they are willing to share with others – including cell lines, research mice, and equipment. Different services work for different institutions.

In 2011, Dr. Elizabeth Iorns, a breast cancer researcher, developed a mouse model to study how breast cancer spreads, but her institution didn’t have the equipment to finish one part of her study. My resource lab could complete the project, but despite significant searching, Dr. Iorns did not have an effective way to find labs like mine.

Actively connecting scientists with resource labs, and helping resource labs keep their equipment optimally busy, is a model Iorns and cofounder Dan Knox have developed into a business, called Science Exchange. (I am on its Lab Advisory Board, but have no financial interest in the company.) A little bit Craigslist and Travelocity for science rolled into one, Science Exchange provides scientists and expert resource labs a way to find each other to keep research progressing.

Unlike Starbucks, resource labs are not found on every corner and can be difficult for scientists to find. Now a simple search provides scientists a list of multiple resource labs that could do the experiments, including estimated costs and speed – and even previous users’ reviews of the choices.

I signed onto Science Exchange soon after it went live and Iorns immediately sent her project to my lab. We completed the project quickly, resulting in the first peer-reviewed publication made possible through Science Exchange….(More).

Can Data Literacy Protect Us from Misleading Political Ads?


Walter Frick at Harvard Business Review: “It’s campaign season in the U.S., and politicians have no compunction about twisting facts and figures, as a quick skim of the fact-checking website Politifact illustrates.

Can data literacy guard against the worst of these offenses? Maybe, according to research.

There is substantial evidence that numeracy can aid critical thinking, and some reason to think it can help in the political realm, within limits. But there is also evidence that numbers can mislead even data-savvy people when it’s in service of those people’s politics.

In a study published at the end of last year, Vittorio Merola of Ohio State University and Matthew Hitt of Louisiana State examined how numeracy might guard against partisan messaging. They showed participants information comparing the costs of probation and prison, and then asked whether participants agreed with the statement, “Probation should be used as an alternative form of punishment, instead of prison, for felons.”

Some of the participants were shown highly relevant numeric information arguing for the benefits of probation: that it costs less and has a better cost-benefit ratio, and that the cost of U.S. prisons has been rising. Another group was shown weaker, less-relevant numeric information. This message didn’t contain anything about the costs or benefits of parole, and instead compared prison costs to transportation spending, with no mention of why these might be at all related. The experiment also varied whether the information was supposedly from a study commissioned by Democrats or Republicans.

The researchers scored participants’ numeracy by asking questions like, “The chance of getting a viral infection is 0.0005. Out of 10,000 people, about how
many of them are expected to get infected?”

For participants who scored low in numeracy, their support depended more on the political party making the argument than on the strength of the data. When the information came from those participants’ own party, they were more likely to agree with it, no matter whether it was weak or strong.

By contrast, participants who scored higher in numeracy were persuaded by the stronger numeric information, even when it came from the other party. The results held up even after accounting for participants’ education, among other variables….

In 2013, Dan Kahan of Yale and several colleagues conducted a study in which they asked participants to draw conclusions from data. In one group, the data was about a treatment for skin rashes, a nonpolitical topic. Another group was asked to evaluate data on gun control, comparing crime rates for cities that have banned concealed weapons to cities that haven’t.

Additionally, in the skin rash group some participants were shown data indicating that the use of skin cream correlated with rashes getting better, while some were shown the opposite. Similarly, some in the gun control group were shown less crime in cities that have banned concealed weapons, while some were shown the reverse…. They found that highly numerate people did better than less-numerate ones in drawing the correct inference in the skin rash case. But comfort with numbers didn’t seem to help when it came to gun control. In fact, highly numerate participants were more polarized over the gun control data than less-numerate ones. The reason seemed to be that the numerate participants used their skill with data selectively, employing it only when doing so helped them reach a conclusion that fit with their political ideology.

Two other lines of research are relevant here.

First, work by Philip Tetlock and Barbara Mellers of the University of Pennsylvania suggests that numerate people tend to make better forecasts, including about geopolitical events. They’ve also documented that even very basic training in probabilistic thinking can improve one’s forecasting accuracy. And this approach works best, Tetlock argues, when it’s part of a whole style of thinking that emphasizes multiple points of view.

Second, two papers, one from the University of Texas at Austin and one from Princeton, found that partisan bias can be diminished with incentives: People are more likely to report factually correct beliefs about the economy when money is on the line…..(More)”

Open data and the API economy: when it makes sense to give away data


 at ZDNet: “Open data is one of those refreshing trends that flows in the opposite direction of the culture of fear that has developed around data security. Instead of putting data under lock and key, surrounded by firewalls and sandboxes, some organizations see value in making data available to all comers — especially developers.

The GovLab.org, a nonprofit advocacy group, published an overview of the benefits governments and organizations are realizing from open data, as well as some of the challenges. The group defines open data as “publicly available data that can be universally and readily accessed, used and redistributed free of charge. It is structured for usability and computability.”…

For enterprises, an open-data stance may be the fuel to build a vibrant ecosystem of developers and business partners. Scott Feinberg, API architect for The New York Times, is one of the people helping to lead the charge to open-data ecosystems. In a recent CXOTalk interview with ZDNet colleague Michael Krigsman, he explains how through the NYT APIs program, developers can sign up for access to 165 years worth of content.

But it requires a lot more than simply throwing some APIs out into the market. Establishing such a comprehensive effort across APIs requires a change in mindset that many organizations may not be ready for, Feinberg cautions. “You can’t be stingy,” he says. “You have to just give it out. When we launched our developer portal there’s a lot of questions like, are people going to be stealing our data, questions like that. Just give it away. You don’t have to give it all but don’t be stingy, and you will find that first off not that many people are going to use it at first. you’re going to find that out, but the people who do, you’re going to find those passionate people who are really interested in using your data in new ways.”

Feinberg clarifies that the NYT’s APIs are not giving out articles for free. Rather, he explains, “we give is everything but article content. You can search for articles. You can find out what’s trending. You can almost do anything you want with our data through our APIs with the exception of actually reading all of the content. It’s really about giving people the opportunity to really interact with your content in ways that you’ve never thought of, and empowering your community to figure out what they want. You know while we don’t give our actual article text away, we give pretty much everything else and people build a lot of really cool stuff on top of that.”

Open data sets, of course, have to worthy of the APIs that offer them. In his post, Borne outlines the seven qualities open data needs to have to be of value to developers and consumers. (Yes, they’re also “Vs” like big data.)

  1. Validity: It’s “critical to pay attention to these data validity concerns when your organization’s data are exposed to scrutiny and inspection by others,” Borne states.
  2. Value: The data needs to be the font of new ideas, new businesses, and innovations.
  3. Variety: Exposing the wide variety of data available can be “a scary proposition for any data scientist,” Borne observes, but nonetheless is essential.
  4. Voice: Remember that “your open data becomes the voice of your organization to your stakeholders.”
  5. Vocabulary: “The semantics and schema (data models) that describe your data are more critical than ever when you provide the data for others to use,” says Borne. “Search, discovery, and proper reuse of data all require good metadata, descriptions, and data modeling.”
  6. Vulnerability: Accept that open data, because it is so open, will be subjected to “misuse, abuse, manipulation, or alteration.”
  7. proVenance: This is the governance requirement behind open data offerings. “Provenance includes ownership, origin, chain of custody, transformations that been made to it, processing that has been applied to it (including which versions of processing software were used), the data’s uses and their context, and more,” says Borne….(More)”

How to Win a Science Contest


 at Pacific Standard: “…there are contests like the DARPA Robotics Challenge, which gives prizes for solving particularly difficult problems, like how to prevent an autonomous vehicle from crashing.

But who wins such contests, and how? One might think it’s the science insiders, since they have the knowledge and background to solve difficult scientific problems. It’s hard to imagine, for example, a political scientist solving a major problem in theoretical physics. At the same time, insiders can become inflexible, having been so ensconced in a particular way of thinking that they can’t see outside of the box, let alone think outside it.

Unfortunately, most of what we know about insiders, outsiders, and scientific success is anecdotal. (Hedy Lamarr, the late actress and co-inventor of a key wireless technology, is a prominent anecdote, but still just an anecdote.) To remedy that, Oguz Ali Acar and Jan van den Ende decided to conduct a proper study. For data, they looked to InnoCentive, an online platform that “crowdsource[s] innovative solutions from the world’s smartest people, who compete to provide ideas and solutions to important business, social, policy, scientific, and technical challenges,” according to its website.

Acar and van den Ende surveyed 230 InnoCentive contest participants, who reported how much expertise they had related to the last problem they’d solved, along with how much experience they had solving similar problems in the past, regardless of whether it was related to their professional expertise. The researchers also asked how many different scientific fields problem solvers had looked to for ideas, and how much effort they’d put into their solutions. For each of the solvers, the researchers then looked at all the contests that person won and computed their odds of winning—a measure of creativity, they argue, since contests are judged in part on the solutions’ creativity.

That data revealed an intuitive, though not entirely simple pattern. Insiders (think Richard Feynman in physics) were more likely to win a contest when they cast a wide net for ideas, while outsiders (like Lamarr) performed best when they focused on one scientific or technological domain. In other words, outsiders—who may bring a useful new perspective to bear—should bone up on the problem they’re trying to solve, while insiders, who’ve already done their homework, benefit from thinking outside the box.

Still, there’s something both groups can’t do without: hard work. “[I]f insiders … spend significant amounts of time seeking out knowledge from a wide variety of other fields, they are more likely to be creative in that domain,” Acar and van den Ende write, and if outsiders work hard, they “can turn their lack of knowledge in a domain into an advantage.”….(More)”

The Social Intranet: Insights on Managing and Sharing Knowledge Internally


Paper by Ines Mergel for IBM Center for the Business of Government: “While much of the federal government lags behind, some agencies are pioneers in the internal use of social media tools.  What lessons and effective practices do they have to offer other agencies?

Social intranets,” Dr. Mergel writes, “are in-house social networks that use technologies – such as automated newsfeeds, wikis, chats, or blogs – to create engagement opportunities among employees.”  They also include the use of internal profile pages that help people identify expertise and interest (similar to Facebook or LinkedIn profiles), and that are used in combination with other social Intranet tools such as on-line communities or newsfeeds.

The report documents four case studies of government use of social intranets – two federal government agencies (the Department of State and the National Aeronautics and Space Administration) and two cross-agency networks (the U.S. Intelligence Community and the Government of Canada).

The author observes: “Most enterprise social networking platforms fail,” but identifies what causes these failures and how successful social intranet initiatives can avoid that fate and thrive.  She offers a series of insights for successfully implementing social intranets in the public sector, based on her observations and case studies. …(More)”

Can Big Data Help Measure Inflation?


Bourree Lam in The Atlantic: “…As more and more people are shopping online, calculating this index has gotten more difficult, because there haven’t been any great ways of recording prices from the sites disparate retailers.Data shared by retailers and compiled by the technology firm Adobe might help close this gap. The company is perhaps known best for its visual software,including Photoshop, but the company has also become a provider of software and analytics for online retailers. Adobe is now aggregating the sales data that flows through their software for its Digital Price Index (DPI) project, an initiative that’s meant to answer some of the questions that have been dogging researcher snow that e-commerce is such a big part of the economy.

The project, which tracks billions of online transactions and the prices of over a million products, was developed with the help of the economists Austan Goolsbee, the former chairman of Obama’s Council of Economic Advisors and a professor at the University of Chicago’s Booth School of Business, and Peter Klenow, a professor at Stanford University. “We’ve been excited to help them setup various measures of the digital economy, and of prices, and also to see what the Adobe data can teach us about some of the questions that everybody’s had about the CPI,” says Goolsbee. “People are asking questions like ‘How price sensitive is online commerce?’ ‘How much is it growing?’ ‘How substitutable is itf or non-electronic commerce?’ A lot issues you can address with this in a way that we haven’t really been able to do before.” These are some questions that the DPI has the potential to answer.

…While this new trove of data will certainly be helpful to economists and analysts looking at inflation, it surely won’t replace the CPI. Currently, the government sends out hundreds of BLS employees to stores around the country to collect price data. Online pricing is a small part of the BLS calculation, which is incorporated into its methodology as people increasingly report shopping from retailers online, but there’s a significant time lag. While it’s unlikely that the BLS would incorporate private sources of data into its inflation calculations, as e-commerce grows they might look to improve the way they include online prices.Still, economists are optimistic about the potential of Adobe’s DPI. “I don’t think we know the digital economy as well as we should,” says Klenow, “and this data can help us eventually nail that better.”…(More)

From waterfall to agile: How a public-sector agency successfully changed its system-development approach to become digital


Martin Lundqvist and PeterBraad Olesen at McKinsey: “Government agencies around the world are under internal and external pressure to become more efficient by incorporating digital technologies and processes into their day-to-day operations. For a lot of public-sector organizations, however, the digital transformation has been bumpy. In many cases, agencies are trying to streamline and automate workflow and processes using antiquated systems-development approaches. Such methods make direct connections between citizens and governments over the Internet more difficult. They also prevent IT organizations from quickly adapting to ever-changing systems requirements or easily combining information from disparate systems. Despite the emergence, over the past decade, of a number of productivity-enhancing technologies, many government institutions continue to cling to old, familiar ways of developing new processes and systems. Nonetheless, a few have been able to change mind-sets internally, shed outdated approaches to improving processes and developing systems, and build new ones. Critically, they have embraced newer techniques, such as agile development, and succeeded in accelerating the digital transformation in core areas of their operations. The Danish Business Authority is one of those organizations.…(More)”

Building an Enterprise Government


IBM Center for the Business of Government: “In January 2017, the next administration will begin the hard work of implementing the President’s priorities. Regardless of the specific policies, implementation in many cases will require working across agency boundaries. By taking an “enterprise government” approach – starting in the transition and continuing into the White House – the next administration can deliver on their promises more effectively. In a new report, Jane Fountain lays out recommendations and a framework for how the new administration can build an enterprise government.

Learn more about our Management Roadmap for the Next Administration, which includes highlights of our transition roundtables, blogs on transition topics, and reports that include recommendations and next steps….(Full report: Building An Enterprise Government.pdf)

Changing views of how to change the world


World leaders concluded three large agreements last year. Each represents a vision of how to change the world. The Addis Ababa Action Agenda on financing for development agreed to move from “billions to trillions” of cross-border flows to developing countries. The agreement on universal sustainable development goals (SDGs) sets out priorities (albeit a long list) for what needs to change. The Paris Agreement on climate change endorses a shift to low-carbon (and ultimately zero carbon) economic growth trajectories.

There is a common thread to these agreements. They each reflect a new theory of how to change the world that is not made explicit but has evolved as a matter of practice. Understanding this new theory is crucial to successful implementation strategies of the three agreements.

In the past, when governments have wanted to change the world, they negotiated intergovernmentalagreements….

The new theory of how to change the world can be stripped down to three elements.

  • Use market forces to drive business towards scalable investments that simultaneously generate sustainable solutions to development challenges;
  • Create more data from more sources with more disaggregation, and make these more easily transparent and accessible, to drive towards evidence-based reforms and accountability;
  • Encourage innovations (technical, organizational, and business-model) to drive the world away from business-as-usual…(More)”

 

The New ABCs of Research: Achieving Breakthrough Collaborations


Book by Ben Shneiderman: “The problems we face in the 21st century require innovative thinking from all of us. Be it students, academics, business researchers of government policy makers. Hopes for improving our healthcare, food supply, community safety and environmental sustainability depend on the pervasive application of research solutions.

The research heroes who take on the immense problems of our time face bigger than ever challenges, but if they adopt potent guiding principles and effective research lifecycle strategies, they can produce the advances that will enhance the lives of many people. These inspirational research leaders will break free from traditional thinking, disciplinary boundaries, and narrow aspirations. They will be bold innovators and engaged collaborators, who are ready to lead, yet open to new ideas, self-confident, yet empathetic to others.

In this book, Ben Shneiderman recognizes the unbounded nature of human creativity, the multiplicative power of teamwork, and the catalytic effects of innovation. He reports on the growing number of initiatives to promote more integrated approaches to research so as to promote the expansion of these efforts. It is meant as a guide to students and junior researchers, as well as a manifesto for senior researchers and policy makers, challenging widely-held beliefs about how applied innovations evolve and how basic breakthroughs are made, and to help plotting the course towards tomorrow’s great advancements….(More)”