Woodrow Wilson International Center for Scholars: “The Commons Lab of the Science and Technology Innovation Program is proud to announce the release of The Power of Hackathons: A Roadmap for Sustainable Open Innovation. Hackathons are collaborative events that have long been part of programmer culture, where people gather in person, online or both to work together on a problem. This could involve creating an application, improving an existing one or testing a platform.
In recent years, government agencies at multiple levels have started holding hackathon events of their own. For this brief, author Zachary Bastian interviewed agency staff, hackathon planners and hackathon participants to better understand how these events can be structured. The fundamental lesson was that a hackathon is not a panacea, but instead should be part of a broader open data and innovation centric strategy.
The full brief can be found here”
Why you should never trust a data visualisation
John Burn-Murdoch in The Guardian: “An excellent blogpost has been receiving a lot of attention over the last week. Pete Warden, an experienced data scientist and author for O’Reilly on all things data, writes:
The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries … I thought I was publishing an entertaining view of some data I’d extracted, but it was treated like a scientific study.
This is an important acknowledgement of a very real problem, but in my view Warden has the wrong target in his crosshairs. Data presented in any medium is a powerful tool and must be used responsibly, but it is when information is expressed visually that the risks are highest.
The central example Warden uses is his visualisation of Facebook friend networks across the United States, which proved extremely popular and was even cited in the New York Times as evidence for growing social division.
As he explains in his post, the methodology behind his underlying network graph is perfectly defensible, but the subsequent clustering process was “produced by me squinting at all the lines, coloring in some areas that seemed more connected in a paint program, and picking silly names for the areas”. The exercise was only ever intended as a bit of fun with a large and interesting dataset, so there really shouldn’t be any problem here.
But there is: humans are visual creatures. Peer-reviewed studies have shown that we can consume information more quickly when it is expressed in diagrams than when it is presented as text.
Even something as simple as colour scheme can have a marked impact on the perceived credibility of information presented visually – often a considerably more marked impact than the actual authority of the data source.
Another great example of this phenomenon was the Washington Post’s ‘map of the world’s most and least racially tolerant countries‘, which went viral back in May of this year. It was widely accepted as an objective, scientific piece of work, despite a number of social scientists identifying flaws in the methodology and the underlying data itself.”
Data Science for Social Good
Data Science for Social Good: “By analyzing data from police reports to website clicks to sensor signals, governments are starting to spot problems in real-time and design programs to maximize impact. More nonprofits are measuring whether or not they’re helping people, and experimenting to find interventions that work.
None of this is inevitable, however.
We’re just realizing the potential of using data for social impact and face several hurdles to it’s widespread adoption:
- Most governments and nonprofits simply don’t know what’s possible yet. They have data – but often not enough and maybe not the right kind.
- There are too few data scientists out there – and too many spending their days optimizing ads instead of bettering lives.
To make an impact, we need to show social good organizations the power of data and analytics. We need to work on analytics projects that have high social impact. And we need to expose data scientists to the problems that really matter.
The fellowship
That’s exactly why we’re doing the Eric and Wendy Schmidt Data Science for Social Good summer fellowship at the University of Chicago.
We want to bring three dozen aspiring data scientists to Chicago, and have them work on data science projects with social impact.
Working closely with governments and nonprofits, fellows will take on real-world problems in education, health, energy, transportation, and more.
Over the next three months, they’ll apply their coding, machine learning, and quantitative skills, collaborate in a fast-paced atmosphere, and learn from mentors in industry, academia, and the Obama campaign.
The program is led by a strong interdisciplinary team from the Computation institute and the Harris School of Public Policy at the University of Chicago.”
‘Medical Instagram’ helps build a library of reference photos for doctors
Springwise: “The power of the visual sharing that makes platforms such as Instagram so popular has been harnessed by retailers like Ask CT Food to share knowledge about cooking, but could the same be done for the medical world? Figure1 enables health professionals to upload and share photos of conditions, creating online discussion as well as crowdsourcing a database of reference images.
Developed by healthcare tech startup Movable Science, the platform is designed in a similar vein to Instagram and enables medical professionals to create their own feed of images from the cases they deal with. In order to protect patients’ identities, the app uses facial recognition to block out faces, while users can add their own marks to cover up other indentifiable marks. They can also add pointers and annotations, as well as choosing who sees it, before uploading the image. Photos can be tagged with relevant terms to allow the community to easily find them through search and others can comment on the images, fostering discussion among users. Images can also be starred, which acts simultaneously as an indication of quality as well as enabling users to save useful images for later reference. …
Although Instagram was developed with the broad purpose of entertainment and social sharing, Figure1 has tweaked the platform’s functions to provide a tool that could help doctors and students share their knowledge and learn from others in an engaging way…”
Let’s Shake Up the Social Sciences
Nicholas Christakis in The New York Times:”TWENTY-FIVE years ago, when I was a graduate student, there were departments of natural science that no longer exist today. Departments of anatomy, histology, biochemistry and physiology have disappeared, replaced by innovative departments of stem-cell biology, systems biology, neurobiology and molecular biophysics. Taking a page from Darwin, the natural sciences are evolving with the times. The perfection of cloning techniques gave rise to stem-cell biology; advances in computer science contributed to systems biology. Whole new fields of inquiry, as well as university departments and majors, owe their existence to fresh discoveries and novel tools.
In contrast, the social sciences have stagnated. They offer essentially the same set of academic departments and disciplines that they have for nearly 100 years: sociology, economics, anthropology, psychology and political science. This is not only boring but also counterproductive, constraining engagement with the scientific cutting edge and stifling the creation of new and useful knowledge. Such inertia reflects an unnecessary insecurity and conservatism, and helps explain why the social sciences don’t enjoy the same prestige as the natural sciences.
One reason citizens, politicians and university donors sometimes lack confidence in the social sciences is that social scientists too often miss the chance to declare victory and move on to new frontiers. Like natural scientists, they should be able to say, “We have figured this topic out to a reasonable degree of certainty, and we are now moving our attention to more exciting areas.” But they do not.”
Transforming Our Conversation of Information Architecture with Structure
Nathaniel Davis: Information architecture has been characterized as both an art and a science. Because there’s more evidence of the former than the latter, the academic and research community is justified in hesitating to give the practice of information architecture more attention.
If you probe the history of information architecture for the web, its foundation appears to be rooted in library science. But you’ll also find a pattern of borrowing methods and models from many other disciplines like architecture and urban planning, linguistics and ethnography, cognition and psychology, to name a few. This history leads many to wonder if the practice of information architecture is anything other than an art of induction for solving problems of architecture and design for the web…
Certainly, there is one concept that has persisted under the radar for many years with limited exploration. It is littered throughout countless articles, books and papers and is present in the most cited IA practice definitions. It may be the single concept that truly bridges practitioner and academic interests around a central and worthwhile topic. That concept is structure.”
Crowdsourcing—Harnessing the Masses to Advance Health and Medicine
A Systematic Review of the literature in the Journal of General Internal Medicine: “Crowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research..
Twenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. …
Utilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods.”
Open Data Tools: Turning Data into ‘Actionable Intelligence’
Shannon Bohle in SciLogs: “My previous two articles were on open access and open data. They conveyed major changes that are underway around the globe in the methods by which scientific and medical research findings and data sets are circulated among researchers and disseminated to the public. I showed how E-science and ‘big data’ fit into the philosophy of science though a paradigm shift as a trilogy of approaches: deductive, empirical, and computational, which was pointed out, provides a logical extenuation of Robert Boyle’s tradition of scientific inquiry involving “skepticism, transparency, and reproducibility for independent verification” to the computational age…
This third article on open access and open data evaluates new and suggested tools when it comes to making the most of the open access and open data OSTP mandates. According to an article published in The Harvard Business Review’s “HBR Blog Network,” this is because, as its title suggests, “open data has little value if people can’t use it.” Indeed, “the goal is for this data to become actionable intelligence: a launchpad for investigation, analysis, triangulation, and improved decision making at all levels.” Librarians and archivists have key roles to play in not only storing data, but packaging it for proper accessibility and use, including adding descriptive metadata and linking to existing tools or designing new ones for their users. Later, in a comment following the article, the author, Craig Hammer, remarks on the importance of archivists and international standards, “Certified archivists have always been important, but their skillset is crucially in demand now, as more and more data are becoming available. Accessibility—in the knowledge management sense—must be on par with digestibility / ‘data literacy’ as priorities for continuing open data ecosystem development. The good news is that several governments and multilaterals (in consultation with data scientists and – yep! – certified archivists) are having continuing ‘shared metadata’ conversations, toward the possible development of harmonized data standards…If these folks get this right, there’s a real shot of (eventual proliferation of) interoperability (i.e. a data platform from Country A can ‘talk to’ a data platform from Country B), which is the only way any of this will make sense at the macro level.”
The Science of Familiar Strangers: Society’s Hidden Social Network
The Physics arXiv Blog “We’ve all experienced the sense of being familiar with somebody without knowing their name or even having spoken to them. These so-called “familiar strangers” are the people we see every day on the bus on the way to work, in the sandwich shop at lunchtime, or in the local restaurant or supermarket in the evening.
These people are the bedrock of society and a rich source of social potential as neighbours, friends, or even lovers.
But while many researchers have studied the network of intentional links between individuals—using mobile-phone records, for example—little work has been on these unintentional links, which form a kind of hidden social network.
Today, that changes thanks to the work of Lijun Sun at the Future Cities Laboratory in Singapore and a few pals who have analysed the passive interactions between 3 million residents on Singapore’s bus network (about 55 per cent of the city’s population). ”This is the first time that such a large network of encounters has been identied and analyzed,” they say.
The results are a fascinating insight into this hidden network of familiar strangers and the effects it has on people….
Perhaps the most interesting result involves the way this hidden network knits society together. Lijun and co say that the data hints that the connections between familiar strangers grows stronger over time. So seeing each other more often increases the chances that familiar strangers will become socially connected.
That’s a fascinating insight into the hidden social network in which we are all embedded. It’s important because it has implications for our understanding of the way things like epidemics can spread through cities.
Perhaps a more interesting is the insight it gives into how links form within communities and how these can strengthened. With the widespread adoption of smart cards on transport systems throughout the world, this kind of study can easily be repeated in many cities, which may help to tease apart some of the factors that make them so different.”
Ref: arxiv.org/abs/1301.5979: Understanding Metropolitan Patterns of Daily Encounters
Social: Why Our Brains are Wired to Connect
Book by Matthew D. Lieberman : “Why are we influenced by the behaviour of complete strangers? Why does the brain register similar pleasure when I perceive something as ‘fair’ or when I eat chocolate? Why can we be so profoundly hurt by bereavement? What are the evolutionary benefits of these traits? The young discipline of ‘social cognitive neuroscience’ has been exploring this fascinating interface between brain science and human behaviour since the late 1990s. Now one of its founding pioneers, Matthew D. Lieberman, presents the discoveries that he and fellow researchers have made. Using fMRI scanning and a range of other techniques, they have been able to see that the brain responds to social pain and pleasure the same way as physical pain and pleasure; and that unbeknown to ourselves, we are constantly ‘mindreading’ other people so that we can fit in with them. It is clear that our brains are designed to respond to and be influenced by others. For good evolutionary reasons, he argues, we are wired to be social. The implications are numerous and profound. Do we have to rethink what we understand by identity, and free will? How can managers improve the way their teams relate and perform? Could we organize large social institutions in ways that would work far better? And could there be whole new methods of education?”