Paper by Kevin J. Boudreau and Karim R. Lakhani: “Most of society’s innovation systems – academic science, the patent system, open source, etc. – are “open” in the sense that they are designed to facilitate knowledge disclosure among innovators. An essential difference across innovation systems is whether disclosure is of intermediate progress and solutions or of completed innovations. We theorize and present experimental evidence linking intermediate versus final disclosure to an ‘incentives-versus-reuse’ tradeoff and to a transformation of the innovation search process. We find intermediate disclosure has the advantage of efficiently steering development towards improving existing solution approaches, but also has the effect of limiting experimentation and narrowing technological search. We discuss the comparative advantages of intermediate versus final disclosure policies in fostering innovation.”
Quantifying the Livable City
Brian Libby at City Lab: “By the time Constantine Kontokosta got involved with New York City’s Hudson Yards development, it was already on track to be historically big and ambitious.
Over the course of the next decade, developers from New York’s Related Companies and Canada-based Oxford Properties Group are building the largest real-estate development in United States history: a 28-acre neighborhood on Manhattan’s far West Side over a Long Island Rail Road yard, with some 17 million square feet of new commercial, residential, and retail space.
Hudson Yards is also being planned as an innovative model of efficiency. Its waste management systems, for example, will utilize a vast vacuum-tube system to collect garbage from each building into a central terminal, meaning no loud garbage trucks traversing the streets by night. Onsite power generation will prevent blackouts like those during Hurricane Sandy, and buildings will be connected through a micro-grid that allows them to share power with each other.
Yet it was Kontokosta, the deputy director of academics at New York University’s Center for Urban Science and Progress (CUSP), who conceived of Hudson Yards as what is now being called the nation’s first “quantified community.” This entails an unprecedentedly wide array of data being collected—not just on energy and water consumption, but real-time greenhouse gas emissions and airborne pollutants, measured with tools like hyper-spectral imagery.
New York has led the way in recent years with its urban data collection. In 2009, Mayor Michael Bloomberg signed Local Law 84, which requires privately owned buildings over 50,000 square feet in size to provide annual benchmark reports on their energy and water use. Unlike a LEED rating or similar, which declares a building green when it opens, the city benchmarking is a continuous assessment of its operations…”
Open Access Button
About the Open Access Button: “The key functions of the Open Access Button are finding free research, making more research available and also advocacy. Here’s how each works.
Finding free papers
Research published in journals that require you to pay to read can sometimes be accessed free in other places. These other copies are often very similar to the published version, but may lack nice formatting or be a version prior to peer review. These copies can be found in research repositories, on authors websites and many other places because they’re archived. To find these versions we identify the paper a user needs and effectively search on Google Scholar and CORE to find these copies and link them to the users.
Making more research, or information about papers available
If a free copy isn’t available we aim to make one. This is not a simple task and so we have to use a few different innovative strategies. First, we email the author of the research and ask them to make a copy of the research available – once they do this we’ll send it to everyone who needs it. Second, we create pages for each paper needed which, if shared, viewed, and linked to an author could see and provide their paper on. Third, we’re building ways to find associated information about a paper such as the facts contained, comments from people who’ve read it, related information and lay summaries.
Advocacy
Unfortunately the Open Access Button can only do so much, and isn’t a perfect or long term solution to this problem. The data and stories collected by the Button are used to help make the changes required to really solve this issue. We also support campaigns and grassroots advocates with this at openaccessbutton.org/action..”
The government wants to study ‘social pollution’ on Twitter
in the Washington Post: “If you take to Twitter to express your views on a hot-button issue, does the government have an interest in deciding whether you are spreading “misinformation’’? If you tweet your support for a candidate in the November elections, should taxpayer money be used to monitor your speech and evaluate your “partisanship’’?
My guess is that most Americans would answer those questions with a resounding no. But the federal government seems to disagree. The National Science Foundation , a federal agency whose mission is to “promote the progress of science; to advance the national health, prosperity and welfare; and to secure the national defense,” is funding a project to collect and analyze your Twitter data.
The project is being developed by researchers at Indiana University, and its purported aim is to detect what they deem “social pollution” and to study what they call “social epidemics,” including how memes — ideas that spread throughout pop culture — propagate. What types of social pollution are they targeting? “Political smears,” so-called “astroturfing” and other forms of “misinformation.”
Named “Truthy,” after a term coined by TV host Stephen Colbert, the project claims to use a “sophisticated combination of text and data mining, social network analysis, and complex network models” to distinguish between memes that arise in an “organic manner” and those that are manipulated into being.
But there’s much more to the story. Focusing in particular on political speech, Truthy keeps track of which Twitter accounts are using hashtags such as #teaparty and #dems. It estimates users’ “partisanship.” It invites feedback on whether specific Twitter users, such as the Drudge Report, are “truthy” or “spamming.” And it evaluates whether accounts are expressing “positive” or “negative” sentiments toward other users or memes…”
Tackling Wicked Government Problems
Book by Jackson Nickerson and Ronald Sanders: “How can government leaders build, sustain, and leverage the cross-organizational collaborative networks needed to tackle the complex interagency and intergovernmental challenges they increasingly face? Tackling Wicked Government Problems: A Practical Guide for Developing Enterprise Leaders draws on the experiences of high-level government leaders to describe and comprehensively articulate the complicated, ill-structured difficulties they face—often referred to as “wicked problems”—in leading across organizational boundaries and offers the best strategies for addressing them.
Tackling Wicked Government Problems explores how enterprise leaders use networks of trusted, collaborative relationships to respond and lead solutions to problems that span agencies. It also offers several approaches for translating social network theory into practical approaches for these leaders to build and leverage boundary-spanning collaborative networks and achieve real mission results.
Finally, past and present government executives offer strategies for systematically developing enterprise leaders. Taken together, these essays provide a way forward for a new cadre of officials better equipped to tackle government’s twenty-first-century wicked challenges”
Training Students to Extract Value from Big Data
New report by the National Research Council: “As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human’s ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.
The nation’s ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.
Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council’s Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula…”
The nation’s ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.
Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council’s Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula…”
Putting Government Data to Work
U.S. Department of Commerce Press Release: “The Governance Lab (GovLab) at New York University today released “Realizing The Potential of Open Government Data: A Roundtable with the U.S. Department of Commerce,” a report on findings and recommendations for ways the U.S. Commerce Department can improve its data management, dissemination and use. The report summarizes a June 2014 Open Data Roundtable, co-hosted by The GovLab and the White House Office of Science and Technology Policy with the Commerce Department, which brought together Commerce data providers and 25 representatives from the private sector and nonprofit organizations for an action-oriented dialogue on data issues and potential solutions. The GovLab is convening a series of other Open Data Roundtables in its mission to help make government more effective and connected to the public through technology.
“We were honored to work with the White House and the Department of Commerce to convene this event,” said Joel Gurin, senior advisor at The GovLab and project director of the Open Data 500 and the Roundtable Series. “The Department’s commitment to engaging with its data customers opens up great opportunities for public-private collaboration.”
Under Secretary of Commerce for Economic Affairs Mark Doms said, “At the Commerce Department, we are only at the beginning of our open data effort. We share the goals and objectives embodied by the call of the Open Data 500: to deliver data that is valuable to industry and that provides greater economic opportunity for millions of Americans.” …”
Under Secretary of Commerce for Economic Affairs Mark Doms said, “At the Commerce Department, we are only at the beginning of our open data effort. We share the goals and objectives embodied by the call of the Open Data 500: to deliver data that is valuable to industry and that provides greater economic opportunity for millions of Americans.” …”
CC Science → Sensored City
Citizen Sourced Data: “We routinely submit data to others and then worry about liberating the data from the silos. What if we could invert the model? What if collected data were first put into a completely free and open repository accessible to everyone so anyone could build applications with the data? What if the data itself were free so everyone could have an equal opportunity to create and even monetize their creativity? Funded by a generous grant from Robert Wood Johnson Foundation, we intend to do just that.
Partnering with Manylabs, a San Francisco-based sensor tools and education nonprofit, and Urban Matter, Inc., a Brooklyn-based design studio, and in collaboration with the City of Louisville, Kentucky, and Propeller Health, maker of a mobile platform for respiratory health management, we will design, develop and install a network of sensor-based hardware that will collect environmental information at high temporal and spatial scales and store it in a software platform designed explicitly for storing and retrieving such data.
Further, we will design, create and install a public data art installation that will be powered by the data we collect thereby communicating back to the public what has been collected about them.”
The Problem-solving Capacity of the Modern State
New book edited by Martin Lodge and Kai Wegrich: “The early 21st century has presented considerable challenges to the problem-solving capacity of the contemporary state in the industrialised world. Among the many uncertainties, anxieties and tensions, it is, however, the cumulative challenge of fiscal austerity, demographic developments, and climate change that presents the key test for contemporary states. Debates abound regarding the state’s ability to address these and other problems given increasingly dispersed forms of governing and institutional vulnerabilities created by politico-administrative and economic decision-making structures. This volume advances these debates, first, by moving towards a cross-sectoral perspective that takes into account the cumulative nature of the contemporary challenge to governance focusing on the key governance areas of infrastructure, sustainability, social welfare, and social integration; second, by considering innovations that have sought to add problem-solving capacity; and third, by exploring the kind of administrative capacities (delivery, regulatory, coordination, and analytical) required to encourage and sustain innovative problem-solving. This edition introduces a framework for understanding the four administrative capacities that are central to any attempt at problem-solving and how they enable the policy instruments of the state to have their intended effect. It also features chapters that focus on the way in which these capacities have become stretched and how they have been adjusted, given the changing conditions; the way in which different states have addressed particular governance challenges, with particular attention paid to innovation at the level of policy instrument and the required administrative capacities; and, finally, types of governance capacities that lie outside the boundaries of the state.”
A taxonomy of crowdsourcing based on task complexity
Paper by Robbie T. Nakatsu et al at the Journal of Information Science: “Although a great many different crowdsourcing approaches are available to those seeking to accomplish individual or organizational tasks, little research attention has yet been given to characterizing how those approaches might be based on task characteristics. To that end, we conducted an extensive review of the crowdsourcing landscape, including a look at what types of taxonomies are currently available. Our review found that no taxonomy explored the multidimensional nature of task complexity. This paper develops a taxonomy whose specific intent is the classification of approaches in terms of the types of tasks for which they are best suited. To develop this task-based taxonomy, we followed an iterative approach that considered over 100 well-known examples of crowdsourcing. The taxonomy considers three dimensions of task complexity: (a) task structure – is the task well-defined, or does it require a more open-ended solution; (2) task interdependence – can the task be solved by an individual, or does it require a community of problem solvers; and (3) task commitment – what level of commitment is expected from crowd members? Based on this taxonomy, we identify seven categories of crowdsourcing and discuss prototypical examples of each approach. Furnished with such an understanding, one should be able to determine which crowdsourcing approach is most suitable for a particular task situation.”