New paper by JH Lee, MG Hancock, MC Hu in Technological Forecasting and Social Change: “This study aims to shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature. We developed a framework for conducting case studies examining how smart cities were being implemented in San Francisco and Seoul Metropolitan City. The study’s empirical results suggest that effective, sustainable smart cities emerge as a result of dynamic processes in which public and private sector actors coordinate their activities and resources on an open innovation platform. The different yet complementary linkages formed by these actors must further be aligned with respect to their developmental stage and embedded cultural and social capabilities. Our findings point to eight ‘stylized facts’, based on both quantitative and qualitative empirical results that underlie the facilitation of an effective smart city. In elaborating these facts, the paper offers useful insights to managers seeking to improve the delivery of smart city developmental projects.”
Global Open Data Initiative moving forward
“The Global Open Data Initiative will serve as a guiding voice internationally on open data issues. Civil society groups who focus on open data have often been isolated to single national contexts, despite the similar challenges and opportunities repeating themselves in countries across the globe. The Global Open Data Initiative aims to help share valuable resources, guidance and judgment, and to clarify the potential for government open data across the world.
Provide a leading vision for how governments approach open data. Open data commitments are among the most popular commitments for countries participating in the Open Government Partnership. The Global Open Data Initiative recommendations and resources will help guide open data initiatives and others as they seek to design and implement strong, effective open data initiatives and policies. Global Open Data Initiative resources will also help civil society actors who will be evaluating government initiatives.
Increase awareness of open data. Global Open Data Initiative will work to advance the understanding of open data issues, challenges, and resources by promoting best practices, engaging in online and offline dialogue, and supporting networking between organizations both new and familiar to the open data arena.
Support the development of the global open data community especially in civil society. Civil society organizations (CSOs) have a key role to play as suppliers, intermediaries, and users of open data, though at present, relatively few organizations are engaging with open data and the opportunities it presents. Most CSOs lack the awareness, skills and support needed to be active users and providers of open data in ways that can help them meet their goals. The Global Open Data Initiative aims to help CSOs, to engage with and use open data whether whatever area they work on – be it climate change, democratic rights, land governance or financial reform.
Our immediate focus is on two activities:
Provide a leading vision for how governments approach open data. Open data commitments are among the most popular commitments for countries participating in the Open Government Partnership. The Global Open Data Initiative recommendations and resources will help guide open data initiatives and others as they seek to design and implement strong, effective open data initiatives and policies. Global Open Data Initiative resources will also help civil society actors who will be evaluating government initiatives.
Increase awareness of open data. Global Open Data Initiative will work to advance the understanding of open data issues, challenges, and resources by promoting best practices, engaging in online and offline dialogue, and supporting networking between organizations both new and familiar to the open data arena.
Support the development of the global open data community especially in civil society. Civil society organizations (CSOs) have a key role to play as suppliers, intermediaries, and users of open data, though at present, relatively few organizations are engaging with open data and the opportunities it presents. Most CSOs lack the awareness, skills and support needed to be active users and providers of open data in ways that can help them meet their goals. The Global Open Data Initiative aims to help CSOs, to engage with and use open data whether whatever area they work on – be it climate change, democratic rights, land governance or financial reform.
Our immediate focus is on two activities:
- To consult with members of the CSO community around the world about what they think is important in this area
- Develop a set of principles in collaboration with the CSO community to guide open government data policies and approaches and to help initiate, strengthen and further elevate conversations between governments and civil society.”
The Value of Personal Data
The Digital Enlightenment Yearbook 2013 is dedicated this year to Personal Data: “The value of personal data has traditionally been understood in ethical terms as a safeguard for personality rights such as human dignity and privacy. However, we have entered an era where personal data are mined, traded and monetized in the process of creating added value – often in terms of free services including efficient search, support for social networking and personalized communications. This volume investigates whether the economic value of personal data can be realized without compromising privacy, fairness and contextual integrity. It brings scholars and scientists from the disciplines of computer science, law and social science together with policymakers, engineers and entrepreneurs with practical experience of implementing personal data management.
The resulting collection will be of interest to anyone concerned about privacy in our digital age, especially those working in the field of personal information management, whether academics, policymakers, or those working in the private sector.”
Using Big Data to Ask Big Questions
Chase Davis in the SOURCE: “First, let’s dispense with the buzzwords. Big Data isn’t what you think it is: Every federal campaign contribution over the last 30-plus years amounts to several tens of millions of records. That’s not Big. Neither is a dataset of 50 million Medicare records. Or even 260 gigabytes of files related to offshore tax havens—at least not when Google counts its data in exabytes. No, the stuff we analyze in pursuit of journalism and app-building is downright tiny by comparison.
But you know what? That’s ok. Because while super-smart Silicon Valley PhDs are busy helping Facebook crunch through petabytes of user data, they’re also throwing off intellectual exhaust that we can benefit from in the journalism and civic data communities. Most notably: the ability to ask Big Questions.
Most of us who analyze public data for fun and profit are familiar with small questions. They’re focused, incisive, and often have the kind of black-and-white, definitive answers that end up in news stories: How much money did Barack Obama raise in 2012? Is the murder rate in my town going up or down?
Big Questions, on the other hand, are speculative, exploratory, and systemic. As the name implies, they are also answered at scale: Rather than distilling a small slice of a dataset into a concrete answer, Big Questions look at entire datasets and reveal small questions you wouldn’t have thought to ask.
Can we track individual campaign donor behavior over decades, and what does that tell us about their influence in politics? Which neighborhoods in my city are experiencing spikes in crime this week, and are police changing patrols accordingly?
Or, by way of example, how often do interest groups propose cookie-cutter bills in state legislatures?
Looking at Legislation
Even if you don’t follow politics, you probably won’t be shocked to learn that lawmakers don’t always write their own bills. In fact, interest groups sometimes write them word-for-word.
Sometimes those groups even try to push their bills in multiple states. The conservative American Legislative Exchange Council has gotten some press, but liberal groups, social and business interests, and even sororities and fraternities have done it too.
On its face, something about elected officials signing their names to cookie-cutter bills runs head-first against people’s ideal of deliberative Democracy—hence, it tends to make news. Those can be great stories, but they’re often limited in scope to a particular bill, politician, or interest group. They’re based on small questions.
Data science lets us expand our scope. Rather than focusing on one bill, or one interest group, or one state, why not ask: How many model bills were introduced in all 50 states, period, by anyone, during the last legislative session? No matter what they’re about. No matter who introduced them. No matter where they were introduced.
Now that’s a Big Question. And with some basic data science, it’s not particularly hard to answer—at least at a superficial level.
Analyze All the Things!
Just for kicks, I tried building a system to answer this question earlier this year. It was intended as an example, so I tried to choose methods that would make intuitive sense. But it also makes liberal use of techniques applied often to Big Data analysis: k-means clustering, matrices, graphs, and the like.
If you want to follow along, the code is here….
To make exploration a little easier, my code represents similar bills in graph space, shown at the top of this article. Each dot (known as a node) represents a bill. And a line connecting two bills (known as an edge) means they were sufficiently similar, according to my criteria (a cosine similarity of 0.75 or above). Thrown into a visualization software like Gephi, it’s easy to click around the clusters and see what pops out. So what do we find?
There are 375 clusters in total. Because of the limitations of our data, many of them represent vague, subject-specific bills that just happen to have similar titles even though the legislation itself is probably very different (think things like “Budget Bill” and “Campaign Finance Reform”). This is where having full bill text would come handy.
But mixed in with those bills are a handful of interesting nuggets. Several bills that appear to be modeled after legislation by the National Conference of Insurance Legislators appear in multiple states, among them: a bill related to limited lines travel insurance; another related to unclaimed insurance benefits; and one related to certificates of insurance.”
Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo
New paper by Michael J. Madison: “The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, some times known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.”
Are Some Tweets More Interesting Than Others? #HardQuestion
New paper by Microsoft Research (Omar Alonso, Catherine C. Marshall, and Marc Najork): “Twitter has evolved into a significant communication nexus, coupling personal and highly contextual utterances with local news, memes, celebrity gossip, headlines, and other microblogging subgenres. If we take Twitter as a large and varied dynamic collection, how can we predict which tweets will be interesting to a broad audience in advance of lagging social indicators of interest such as retweets? The telegraphic form of tweets, coupled with the subjective notion of interestingness, makes it difficult for human judges to agree on which tweets are indeed interesting.
In this paper, we address two questions: Can we develop a reliable strategy that results in high-quality labels for a collection of tweets, and can we use this labeled collection to predict a tweet’s interestingness?
To answer the first question, we performed a series of studies using crowdsourcing to reach a diverse set of workers who served as a proxy for an audience with variable interests and perspectives. This method allowed us to explore different labeling strategies, including varying the judges, the labels they applied, the datasets, and other aspects of the task.
To address the second question, we used crowdsourcing to assemble a set of tweets rated as interesting or not; we scored these tweets using textual and contextual features; and we used these scores as inputs to a binary classifier. We were able to achieve moderate agreement (kappa = 0.52) between the best classifier and the human assessments, a figure which reflects the challenges of the judgment task.”
How to Make All Apps More Civic
Nick Grossman in Idea Lab: “The big idea in all of this is that through open data and standards and API-based interoperability, it’s possible not just to build more “civic apps,” but to make all apps more civic:
So in a perfect world, I’d not only be able to get my transit information from anywhere (say, Citymapper), I’d be able to read restaurant inspection data from anywhere (say, Foursquare), be able to submit a 311 request from anywhere (say, Twitter), etc.
These examples only scratch the surface of how apps can “become more civic” (i.e., integrate with government/civic information and services). And that’s only really describing one direction: apps tapping into government information and services.
Another, even more powerful direction is the reverse: helping governments tap into the people-power in web networks. In fact, I heard an amazing stat earlier this year:
It’s incredible to think about how web-enabled networks can extend the reach and increase the leverage of public-interest programs and government services, even when (perhaps especially when) that is not their primary function — i.e., Waze is a traffic avoidance app, not a “civic” app. Other examples include the Airbnb community coming together to provide emergency housing after Sandy, and the Etsy community helping to “craft a comeback” in Rockford, Ill.
In other words, helping all apps “be more civic,” rather than just building more civic apps. I think there is a ton of leverage there, and it’s a direction that has just barely begun to be explored.”
Defining Open Data
Open Knowledge Foundation Blog: “Open data is data that can be freely used, shared and built-on by anyone, anywhere, for any purpose. This is the summary of the full Open Definition which the Open Knowledge Foundation created in 2005 to provide both a succinct explanation and a detailed definition of open data.
As the open data movement grows, and even more governments and organisations sign up to open data, it becomes ever more important that there is a clear and agreed definition for what “open data” means if we are to realise the full benefits of openness, and avoid the risks of creating incompatibility between projects and splintering the community.
Open can apply to information from any source and about any topic. Anyone can release their data under an open licence for free use by and benefit to the public. Although we may think mostly about government and public sector bodies releasing public information such as budgets or maps, or researchers sharing their results data and publications, any organisation can open information (corporations, universities, NGOs, startups, charities, community groups and individuals).
Read more about different kinds of data in our one page introduction to open data
There is open information in transport, science, products, education, sustainability, maps, legislation, libraries, economics, culture, development, business, design, finance …. So the explanation of what open means applies to all of these information sources and types. Open may also apply both to data – big data and small data – or to content, like images, text and music!
So here we set out clearly what open means, and why this agreed definition is vital for us to collaborate, share and scale as open data and open content grow and reach new communities.
As the open data movement grows, and even more governments and organisations sign up to open data, it becomes ever more important that there is a clear and agreed definition for what “open data” means if we are to realise the full benefits of openness, and avoid the risks of creating incompatibility between projects and splintering the community.
Open can apply to information from any source and about any topic. Anyone can release their data under an open licence for free use by and benefit to the public. Although we may think mostly about government and public sector bodies releasing public information such as budgets or maps, or researchers sharing their results data and publications, any organisation can open information (corporations, universities, NGOs, startups, charities, community groups and individuals).
Read more about different kinds of data in our one page introduction to open data
There is open information in transport, science, products, education, sustainability, maps, legislation, libraries, economics, culture, development, business, design, finance …. So the explanation of what open means applies to all of these information sources and types. Open may also apply both to data – big data and small data – or to content, like images, text and music!
So here we set out clearly what open means, and why this agreed definition is vital for us to collaborate, share and scale as open data and open content grow and reach new communities.
What is Open?
The full Open Definition provides a precise definition of what open data is. There are 2 important elements to openness:
- Legal openness: you must be allowed to get the data legally, to build on it, and to share it. Legal openness is usually provided by applying an appropriate (open) license which allows for free access to and reuse of the data, or by placing data into the public domain.
- Technical openness: there should be no technical barriers to using that data. For example, providing data as printouts on paper (or as tables in PDF documents) makes the information extremely difficult to work with. So the Open Definition has various requirements for “technical openness,” such as requiring that data be machine readable and available in bulk.”…
The Science Behind Using Online Communities To Change Behavior
Sean D. Young in TechCrunch: “Although social media and online communities might have been developed for people to connect and share information, recent research shows that these technologies are really helpful in changing behaviors. My colleagues and I in the medical school, for instance, created online communities designed to improve health by getting people to do things, such as test for HIV, stop using methamphetamines, and just de-stress and relax. We don’t handpick people to join because we think they’ll love the technology; that’s not how science works. We invite them because the technology is relevant to them — they’re engaging in drugs, sex and other behaviors that might put themselves and others at risk. It’s our job to create the communities in a way that engages them enough to want to stay and participate. Yes, we do offer to pay them $30 to complete an hour-long survey, but then they are free to collect their money and never talk to us again. But for some reason, they stay in the group and decide to be actively engaged with strangers.
So how do we create online communities that keep people engaged and change their behaviors? Our starting point is to understand and address their psychological needs….
Throughout our research, we find that newly created online communities can change people’s behaviors by addressing the following psychological needs:
The Need to Trust. Sharing our thoughts, experiences, and difficulties with others makes us feel closer to others and increases our trust. When we trust people, we’re more open-minded, more willing to learn, and more willing to change our behavior. In our studies, we found that sharing personal information (even something as small as describing what you did today) can help increase trust and change behavior.
The Need to Fit In. Most of us inherently strive to fit in. Social norms, or other people’s attitudes and behaviors, heavily influence our own attitudes and behaviors. Each time a new online community or group forms, it creates its own set of social norms and expectations for how people should behave. Most people are willing to change their attitudes and/or behavior to fit these group norms and fit in with the community.
The Need for Self-Worth. When people feel good about themselves, they are more open to change and feel empowered to be able to change their behavior. When an online community is designed to have people support and care for each other, they can help to increase self-esteem.
The Need to Be Rewarded for Good Behavior. Anyone who has trained a puppy knows that you can get him to keep sitting as long as you keep the treats flowing to reward him, but if you want to wean him off the treats and really train him then you’ll need to begin spacing out the treats to make them less predictable. Well, people aren’t that different from animals in that way and can be trained with reinforcements too. For example, “liking” people’s communications when they immediately join a network, and then progressively spacing out the time that their posts are liked (psychologists call this variable reinforcement) can be incorporated onto social network platforms to encourage them to keep posting content. Eventually, these behaviors become habits.
The Need to Feel Empowered. While increasing self-esteem makes people feel good about themselves, increasing empowerment helps them know they have the ability to change. Creating a sense of empowerment is one of the most powerful predictors of whether people will change their behavior. Belonging to a network of people who are changing their own behaviors, support our needs, and are confident in our changing our behavior empowers us and gives us the ability to change our behavior.”
Imagining Data Without Division
Thomas Lin in Quanta Magazine: “As science dives into an ocean of data, the demands of large-scale interdisciplinary collaborations are growing increasingly acute…Seven years ago, when David Schimel was asked to design an ambitious data project called the National Ecological Observatory Network, it was little more than a National Science Foundation grant. There was no formal organization, no employees, no detailed science plan. Emboldened by advances in remote sensing, data storage and computing power, NEON sought answers to the biggest question in ecology: How do global climate change, land use and biodiversity influence natural and managed ecosystems and the biosphere as a whole?…
For projects like NEON, interpreting the data is a complicated business. Early on, the team realized that its data, while mid-size compared with the largest physics and biology projects, would be big in complexity. “NEON’s contribution to big data is not in its volume,” said Steve Berukoff, the project’s assistant director for data products. “It’s in the heterogeneity and spatial and temporal distribution of data.”
Unlike the roughly 20 critical measurements in climate science or the vast but relatively structured data in particle physics, NEON will have more than 500 quantities to keep track of, from temperature, soil and water measurements to insect, bird, mammal and microbial samples to remote sensing and aerial imaging. Much of the data is highly unstructured and difficult to parse — for example, taxonomic names and behavioral observations, which are sometimes subject to debate and revision.
And, as daunting as the looming data crush appears from a technical perspective, some of the greatest challenges are wholly nontechnical. Many researchers say the big science projects and analytical tools of the future can succeed only with the right mix of science, statistics, computer science, pure mathematics and deft leadership. In the big data age of distributed computing — in which enormously complex tasks are divided across a network of computers — the question remains: How should distributed science be conducted across a network of researchers?
Part of the adjustment involves embracing “open science” practices, including open-source platforms and data analysis tools, data sharing and open access to scientific publications, said Chris Mattmann, 32, who helped develop a precursor to Hadoop, a popular open-source data analysis framework that is used by tech giants like Yahoo, Amazon and Apple and that NEON is exploring. Without developing shared tools to analyze big, messy data sets, Mattmann said, each new project or lab will squander precious time and resources reinventing the same tools. Likewise, sharing data and published results will obviate redundant research.
To this end, international representatives from the newly formed Research Data Alliance met this month in Washington to map out their plans for a global open data infrastructure.”