The Creepy New Wave of the Internet

Curated on Posted on by Stefaan Verhulst

Review by Sue Halpern in the New York Review of Books from:

 
…So here comes the Internet’s Third Wave. In its wake jobs will disappear, work will morph, and a lot of money will be made by the companies, consultants, and investment banks that saw it coming. Privacy will disappear, too, and our intimate spaces will become advertising platforms—last December Google sent a letter to the SEC explaining how it might run ads on home appliances—and we may be too busy trying to get our toaster to communicate with our bathroom scale to notice. Technology, which allows us to augment and extend our native capabilities, tends to evolve haphazardly, and the future that is imagined for it—good or bad—is almost always historical, which is to say, naive.”

A World That Counts: Mobilising a Data Revolution for Sustainable Development

Curated on Posted on by Stefaan Verhulst

Executive Summary of the Report by the UN Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development (IEAG): “Data are the lifeblood of decision-making and the raw material for accountability. Without high-quality data providing the right information on the right things at the right time; designing, monitoring and evaluating effective policies becomes almost impossible.
New technologies are leading to an exponential increase in the volume and types of data available, creating unprecedented possibilities for informing and transforming society and protecting the environment. Governments, companies, researchers and citizen groups are in a ferment of experimentation, innovation and adaptation to the new world of data, a world in which data are bigger, faster and more detailed than ever before. This is the data revolution.
Some are already living in this new world. But too many people, organisations and governments are excluded because of lack of resources, knowledge, capacity or opportunity. There are huge and growing inequalities in access to data and information and in the ability to use it.
Data needs improving. Despite considerable progress in recent years, whole groups of people are not being counted and important aspects of people’s lives and environmental conditions are still not measured. For people, this can lead to the denial of basic rights, and for the planet, to continued environmental degradation. Too often, existing data remain unused because they are released too late or not at all, not well-documented and harmonized, or not available at the level of detail needed for decision-making.
As the world embarks on an ambitious project to meet new Sustainable Development Goals (SDGs), there is an urgent need to mobilise the data revolution for all people and the whole planet in order to monitor progress, hold governments accountable and foster sustainable development. More diverse, integrated, timely and trustworthy information can lead to better decision-making and real-time citizen feedback. This in turn enables individuals, public and private institutions, and companies to make choices that are good for them and for the world they live in.
This report sets out the main opportunities and risks presented by the data revolution for sustain-able development. Seizing these opportunities and mitigating these risks requires active choices, especially by governments and international institutions. Without immediate action, gaps between developed and developing countries, between information-rich and information-poor people, and between the private and public sectors will widen, and risks of harm and abuses of human rights will grow.

An urgent call for action: Key recommendations

The strong leadership of the United Nations (UN) is vital for the success of this process. The Independent Expert Advisory Group (IEAG), established in August 2014, offers the UN Secretary-General several key recommendations for actions to be taken in the near future, summarised below:

  1. Develop a global consensus on principles and standards: The disparate worlds of public, private and civil society data and statistics providers need to be urgently brought together to build trust and confidence among data users. We propose that the UN establish a process whereby key stakeholders create a “Global Consensus on Data”, to adopt principles concerning legal, technical, privacy, geospatial and statistical standards which, among other things, will facilitate openness and information exchange and promote and protect human rights.
  2. Share technology and innovations for the common good: To create mechanisms through which technology and innovation can be shared and used for the common good, we propose
    to create a global “Network of Data Innovation Networks”, to bring together the organisations and experts in the field. This would: contribute to the adoption of best practices for improving the monitoring of SDGs, identify areas where common data-related infrastructures could address capacity problems and improve efficiency, encourage collaborations, identify critical research gaps and create incentives to innovate.
  3. New resources for capacity development: Improving data is a development agenda in
    its own right, and can improve the targeting of existing resources and spur new economic opportunities. Existing gaps can only be overcome through new investments and the strengthening of capacities. A new funding stream to support the data revolution for sustainable development should be endorsed at the “Third International Conference on Financing for Development”, in Addis Ababa in July 2015. An assessment will be needed of the scale of investments, capacity development and technology transfer that is required, especially for low income countries; and proposals developed for mechanisms to leverage the creativity and resources of the private sector. Funding will also be needed to implement an education program aimed at improving people’s, infomediaries’ and public servants’ capacity and data literacy to break down barriers between people and data.
  4. Leadership for coordination and mobilisation: A UN-led “Global Partnership for Sustainable Development Data” is proposed, tomobiliseandcoordinate the actions and institutions required to make the data revolution serve sustainable development, promoting several initiatives, such as:
    • A “World Forum on Sustainable Development Data” to bring together the whole data ecosystem to share ideas and experiences for data improvements, innovation, advocacy and technology transfer. The first Forum should take place at the end of 2015, once the SDGs are agreed;
    • A “Global Users Forum for Data for SDGs”, to ensure feedback loops between data producers and users, help the international community to set priorities and assess results;
    • Brokering key global public-private partnerships for data sharing.
  5. Exploit some quick wins on SDG data: Establishing a “SDGs data lab” to support the development of a first wave of SDG indicators, developing an SDG analysis and visualisation platform using the most advanced tools and features for exploring data, and building a dashboard from diverse data sources on ”the state of the world”.

Never again should it be possible to say “we didn’t know”. No one should be invisible. This is the world we want – a world that counts.”

The Future of Cities

Curated on Posted on by Stefaan Verhulst
John Chambers and Wim Elfrink at Foreign Affairs: “As much as the Internet has already changed the world, it is the Web’s next phase that will bring the biggest opportunities, revolutionizing the way we live, work, play, and learn.

That next phase, which some call the Internet of Things and which we call the Internet of Everything, is the intelligent connection of people, processes, data, and things. Although it once seemed like a far-off idea, it is becoming a reality for businesses, governments, and academic institutions worldwide. Today, half the world’s population has access to the Internet; by 2020, two-thirds will be connected. Likewise, some 13.5 billion devices are connected to the Internet today; by 2020, we expect that number to climb to 50 billion. The things that are—and will be—connected aren’t just traditional devices, such as computers, tablets, and phones, but also parking spaces and alarm clocks, railroad tracks, street lights, garbage cans, and components of jet engines.
All of these connections are already generating massive amounts of digital data—and it doubles every two years. New tools will collect and share that data (some 15,000 applications are developed each week!) and, with analytics, that can be turned into information, intelligence, and even wisdom, enabling everyone to make better decisions, be more productive, and have more enriching experiences.
And the value that it will bring will be epic. In fact, the Internet of Everything has the potential to create $19 trillion in value over the next decade. For the global private sector, this equates to a 21 percent potential aggregate increase in corporate profits—or $14.4 trillion. The global public sector will benefit as well, using the Internet of Everything as a vehicle for the digitization of cities and countries. This will improve efficiency and cut costs, resulting in as much as $4.6 trillion of total value. Beyond that, it will help (and already is helping) address some of the world’s most vexing challenges: aging and growing populations rapidly moving to urban centers; growing demand for increasingly limited natural resources; and massive rebalancing in economic growth between briskly growing emerging market countries and slowing developed countries….”

OpenUp Corporate Data while Protecting Privacy

Curated on Posted on by Stefaan Verhulst
Article by Stefaan G. Verhulst and David Sangokoya, (The GovLab) for the OpenUp? Blog: “Consider a few numbers: By the end of 2014, the number of mobile phone subscriptions worldwide is expected to reach 7 billion, nearly equal to the world’s population. More than 1.82 billion people communicate on some form of social network, and almost 14 billion sensor-laden everyday objects (trucks, health monitors, GPS devices, refrigerators, etc.) are now connected and communicating over the Internet, creating a steady stream of real-time, machine-generated data.
Much of the data generated by these devices is today controlled by corporations. These companies are in effect “owners” of terabytes of data and metadata. Companies use this data to aggregate, analyze, and track individual preferences, provide more targeted consumer experiences, and add value to the corporate bottom line.
At the same time, even as we witness a rapid “datafication” of the global economy, access to data is emerging as an increasingly critical issue, essential to addressing many of our most important social, economic, and political challenges. While the rise of the Open Data movement has opened up over a million datasets around the world, much of this openness is limited to government (and, to a lesser extent, scientific) data. Access to corporate data remains extremely limited. This is a lost opportunity. If corporate data—in the form of Web clicks, tweets, online purchases, sensor data, call data records, etc.—were made available in a de-identified and aggregated manner, researchers, public interest organizations, and third parties would gain greater insights on patterns and trends that could help inform better policies and lead to greater public good (including combatting Ebola).
Corporate data sharing holds tremendous promise. But its potential—and limitations—are also poorly understood. In what follows, we share early findings of our efforts to map this emerging open data frontier, along with a set of reflections on how to safeguard privacy and other citizen and consumer rights while sharing. Understanding the practice of shared corporate data—and assessing the associated risks—is an essential step in increasing access to socially valuable data held by businesses today. This is a challenge certainly worth exploring during the forthcoming OpenUp conference!
Understanding and classifying current corporate data sharing practices
Corporate data sharing remains very much a fledgling field. There has been little rigorous analysis of different ways or impacts of sharing. Nonetheless, our initial mapping of the landscape suggests there have been six main categories of activity—i.e., ways of sharing—to date:…
Assessing risks of corporate data sharing
Although the shared corporate data offers several benefits for researchers, public interest organizations, and other companies, there do exist risks, especially regarding personally identifiable information (PII). When aggregated, PII can serve to help understand trends and broad demographic patterns. But if PII is inadequately scrubbed and aggregated data is linked to specific individuals, this can lead to identity theft, discrimination, profiling, and other violations of individual freedom. It can also lead to significant legal ramifications for corporate data providers….”

Could digital badges clarify the roles of co-authors?

Curated on Posted on by Stefaan Verhulst

Dalmeet Singh Chawla  at AAAS Science Magazine: “Ever look at a research paper and wonder how the half-dozen or more authors contributed to the work? After all, it’s usually only the first or last author who gets all the media attention or the scientific credit when people are considered for jobs, grants, awards, and more. Some journals try to address this issue with the “authors’ contributions” sections within a paper, but a collection of science, publishing, and software groups is now developing a more modern solution—digital “badges,” assigned on publication of a paper online, that detail what each author did for the work and that the authors can link to their profiles elsewhere on the Web.

Digital badges could clarify co-authors' roles

Those organizations include publishers BioMed Central and the Public Library of Science; The Wellcome Trust research charity; software development groups Mozilla Science Lab (a group of researchers, developers, librarians, and publishers) and Digital Science (a software and technology firm); and ORCID, an effort to assign researchers digital identifiers. The collaboration presented its progress on the project at the Mozilla Festival in London that ended last week. (Mozilla is the open software community behind the Firefox browser and other programs.)
The infrastructure of the badges is still being established, with early prototypes scheduled to launch early next year, according to Amye Kenall, the journal development manager of open data initiatives and journals at BioMed Central. She envisions the badge process in the following way: Once an article is published, the publisher would alert software maintained by Mozilla to automatically set up an online form, where authors fill out roles using a detailed contributor taxonomy. After the authors have completed this, the badges would then appear next to their names on the journal article, and double-clicking on a badge would lead to the ORCID site for that particular author, where the author’s badges, integrated with their publishing record, live….
The parties behind the digital badge effort are “looking to change behavior” of scientists in the competitive dog-eat-dog world of academia by acknowledging contributions, says Kaitlin Thaney, director of Mozilla Science Lab. Amy Brand, vice president of academic and research relations and VP of North America at Digital Science, says that the collaboration believes that the badges should be optional, to accommodate old-fashioned or less tech-savvy authors. She says that the digital credentials may improve lab culture, countering situations where junior scientists are caught up in lab politics and the “star,” who didn’t do much of the actual research apart from obtaining the funding, gets to be the first author of the paper and receive the most credit. “All of this calls out for more transparency,” Brand says….”

The collision between big data and privacy law

Curated on Posted on by Stefaan Verhulst
Paper by Stephen Wilson in the Australian Journal of Telecommunications and the Digital Economy : “We live in an age where billionaires are self-made on the back of the most intangible of assets – the information they have about us. The digital economy is awash with data. It’s a new and endlessly re-useable raw material, increasingly left behind by ordinary people going about their lives online. Many information businesses proceed on the basis that raw data is up for grabs; if an entrepreneur is clever enough to find a new vein of it, they can feel entitled to tap it in any way they like. However, some tacit assumptions underpinning today’s digital business models are naive. Conventional data protection laws, older than the Internet, limit how Personal Information is allowed to flow. These laws turn out to be surprisingly powerful in the face of ‘Big Data’ and the ‘Internet of Things’. On the other hand, orthodox privacy management was not framed for new Personal Information being synthesised tomorrow from raw data collected today. This paper seeks to bridge a conceptual gap between data analytics and privacy, and sets out extended Privacy Principles to better deal with Big Data.”

Urban Observatory Is Snapping 9,000 Images A Day Of New York City

Curated on Posted on by Stefaan Verhulst
FastCo-Exist: “Astronomers have long built observatories to capture the night sky and beyond. Now researchers at NYU are borrowing astronomy’s methods and turning their cameras towards Manhattan’s famous skyline.
NYU’s Center for Urban Science and Progress has been running what’s likely the world’s first “urban observatory” of its kind for about a year. From atop a tall building in downtown Brooklyn (NYU won’t say its address, due to security concerns), two cameras—one regular one and one that captures infrared wavelengths—take panoramic images of lower and midtown Manhattan. One photo is snapped every 10 seconds. That’s 8,640 images a day, or more than 3 million since the project began (or about 50 terabytes of data).

“The real power of the urban observatory is that you have this synoptic imaging. By synoptic imaging, I mean these large swaths of the city,” says the project’s chief scientist Gregory Dobler, a former astrophysicist at Harvard University and the University of California, Santa Barbara who now heads the 15-person observatory team at NYU.
Dobler’s team is collaborating with New York City officials on the project, which is now expanding to set up stations that study other parts of Manhattan and Brooklyn. Its major goal is to discover information about the urban landscape that can’t be seen at other scales. Such data could lead to applications like tracking which buildings are leaking energy (with the infrared camera), or measuring occupancy patterns of buildings at night, or perhaps detecting releases of toxic chemicals in an emergency.
The video above is an example. The top panel cycles through a one-minute slice of observatory images. The bottom panel is an analysis of the same images in which everything that remains static in each image is removed, such as buildings, trees, and roads. What’s left is an imprint of everything in flux within the scene—the clouds, the cars on the FDR Drive, the boat moving down the East River, and, importantly, a plume of smoke that puffs out of a building.
“Periodically, a building will burp,” says Dobler. “It’s hard to see the puffs of smoke . . . but we can isolate that plume and essentially identify it.” (As Dobler has done by highlighting it in red in the top panel).
To the natural privacy concerns about this kind of program, Dobler emphasizes that the pictures are only from an 8 megapixel camera (the same found in the iPhone 6) and aren’t clear enough to see inside a window or make out individuals. As a further privacy safeguard, the images are analyzed to only look at “aggregate” measures—such as the patterns of nighttime energy usage—rather than specific buildings. “We’re not really interested in looking at a given building, and saying, hey, these guys are particular offenders,” he says (He also says the team is not looking at uses for the data in security applications.) However, Dobler was not able to answer a question as to whether the project’s partners at city agencies are able to access data analysis for individual buildings….”

Finding Collaborators: Toward Interactive Discovery Tools for Research Network Systems

Curated on Posted on by Stefaan Verhulst

New paper by Charles D Borromeo, Titus K Schleyer, Michael J Becich, and Harry Hochheiser: “Background: Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs.
Objective: The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype.
Methods: Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS).
Results: Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified.
Conclusions: Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.”

How Wikipedia Data Is Revolutionizing Flu Forecasting

Curated on Posted on by Stefaan Verhulst
Emerging Technology From the arXiv: “This time last year, the Centers for Disease Control and Prevention in Atlanta launched a competition to find the best way to forecast the characteristics of the 2013-2014 influenza season using data gathered from the internet. Today, Kyle Hickmann from Los Alamos National Laboratories in New Mexico and a few pals reveal the results of their model which used real-time data from Wikipedia to forecast the ground truth data gathered by the CDC that surfaces about two weeks later.

They say their model has the potential to transform flu forecasting from a black art to a modern science as well-founded as weather forecasting.
Flu takes between 3,000 and 49,000 lives each year in the U.S. so an accurate forecast can have a significant impact on the way society prepares for the epidemic. The current method of monitoring flu outbreaks is somewhat antiquated. It relies on a voluntary system in which public health officials report the percentage of patients they see each week with influenza-like illnesses. This is defined as the percentage of people with a temperature higher than 100 degrees, a cough and no other explanation other than flu.
These numbers give a sense of the incidence of flu at any instant but the accuracy is clearly limited. They do not, for example, account for people with flu who do not seek treatment or people with flu-like symptoms who seek treatment but do not have flu.
There is another significant problem. The network that reports this data is relatively slow. It takes about two weeks for the numbers to filter through the system so the data is always weeks old.
That’s why the CDC is interested in finding new ways to monitor the spread of flu in real time. Google, in particular, has used the number of searches for flu and flu-like symptoms to forecast flu in various parts of the world. That approach has had considerable success but also some puzzling failures. One problem, however, is that Google does not make its data freely available and this lack of transparency is a potential source of trouble for this kind of research.
So Hickmann and co have turned to Wikipedia. Their idea is that the variation in numbers of people accessing articles about flu is an indicator of the spread of the disease. And since Wikipedia makes this data freely available to any interested party, it is an entirely transparent source that is likely to be available for the foreseeable future….
Ref: arxiv.org/abs/1410.7716 : Forecasting the 2013–2014 Influenza Season using Wikipedia”

The New Thing in Google Flu Trends Is Traditional Data

Curated on Posted on by Stefaan Verhulst

in the New York Times: “Google is giving its Flu Trends service an overhaul — “a brand new engine,” as it announced in a blog post on Friday.

The new thing is actually traditional data from the Centers for Disease Control and Prevention that is being integrated into the Google flu-tracking model. The goal is greater accuracy after the Google service had been criticized for consistently over-estimating flu outbreaks in recent years.

The main critique came in an analysis done by four quantitative social scientists, published earlier this year in an article in Science magazine, “The Parable of Google Flu: Traps in Big Data Analysis.” The researchers found that the most accurate flu predictor was a data mash-up that combined Google Flu Trends, which monitored flu-related search terms, with the official C.D.C. reports from doctors on influenza-like illness.

The Google Flu Trends team is heeding that advice. In the blog post, written by Christian Stefansen, a Google senior software engineer, wrote, “We’re launching a new Flu Trends model in the United States that — like many of the best performing methods in the literature — takes official CDC flu data into account as the flu season progresses.”

Google’s flu-tracking service has had its ups and downs. Its triumph came in 2009, when it gave an advance signal of the severity of the H1N1 outbreak, two weeks or so ahead of official statistics. In a 2009 article in Nature explaining how Google Flu Trends worked, the company’s researchers did, as the Friday post notes, say that the Google service was not intended to replace official flu surveillance methods and that it was susceptible to “false alerts” — anything that might prompt a surge in flu-related search queries.

Yet those caveats came a couple of pages into the Nature article. And Google Flu Trends became a symbol of the superiority of the new, big data approach — computer algorithms mining data trails for collective intelligence in real time. To enthusiasts, it seemed so superior to the antiquated method of collecting health data that involved doctors talking to patients, inspecting them and filing reports.

But Google’s flu service greatly overestimated the number of cases in the United States in the 2012-13 flu season — a well-known miss — and, according to the research published this year, has persistently overstated flu cases over the years. In the Science article, the social scientists called it “big data hubris.”