Stefaan Verhulst

Open Access Button: “Hidden data is hindering research, and we’re tired of it. Next week we’ll release the Open Data Button beta as part of Open Data Day. The Open Data Button will help people find, release, and share the data behind papers. We need your support to share, test, and improve the Open Data Button. Today, we’re going to provide some in depth info about the tool.

You’ll be able to download the free Open Data Button on the 29th of February. Follow the launch conversation on Twitter and at #opendatabutton.

How the Open Data Button works

You will be able to download the Open Data Button on Chrome, and later on Firefox. When you need the data supporting a paper (even if it’s behind a paywall), push the Button. If the data has already been made available through the Open Data Button, we’ll give you a link. If it hasn’t, you’ll be able to start a request for the data. Eventually, we want to search a variety of other sources for it – but can’t yet (read on, we need your help with that).

The request will be sent to the author. We know sharing data can be hard and there’s sometimes good reasons not to. The author will be able to respond to it by saying how long it’ll take to share the data – or if they can’t. If the data is already available, the author can simply share a URL to the dataset. If it isn’t, they can attach files to a response for us to make available. Files shared with us will be deposited in the Open Science Framework for identification and archiving. The Open Science Framework supports data sharing for all disciplines. As much metadata as possible will be obtained from the paper, the rest we’ll ask the author for.

The progress of this request is tracked through our new “request” pages. On request pages others can support a request and be sent a copy of the data when it’s available. We’ll map requests, and stories will be searchable – both will now be embeddable objects.

Once available, we’ll send data to people who’ve requested it. You can award an Open Data Badge to the author if there’s enough supporting information to reproduce the data’s results.

At first we’ll only have a Chrome add-on, but support for Firefox will be available from Firefox 46. Support for a bookmarklet will also be provided, but we don’t have a release date yet….(More)”

Kevin Ebi at Smart Cities Council: “Most cities and utilities want to get smarter. They see the smart cities movement as delivering more than some incremental improvement. They see it as a meaningful transformation — one that delivers far more than just some cost savings.

Despite all that, the latest Black & Veatch Strategic Directions: U.S. Smart City/Smart Utility Report finds they plan to move slower — not faster — to become smarter. But understanding the obstacles can help you overcome them.

First, the good news
Cities don’t need to be sold on the idea of becoming smarter. More than 90% see the smart cities movement as being transformational with long-term lasting impacts.

Nearly 80% believe it should start with initiatives that have lasting benefits — even if that work is largely behind the scenes (and therefore less likely for the public to notice.) A similar number also believe that data analytics will significantly improve decision making. And nearly all believe it’s a comprehensive effort; it’s more than just buying some new technology.

The smart cities revolution is also inclusive. More than three-quarters say that energy, water and telecommunications providers should play a leadership role in smart cities initiatives — they shouldn’t be relegated to a supporting role.

And growing numbers see smart cities initiatives as something more than just a vehicle to cut costs. This year, more respondents — cities leaders and utilities alike — see the potential to become more sustainable, better manage community resources and to attract business investment.

But there’s also room for improvement
Despite clearly understanding the value of smart cities initiatives, the survey finds respondents are losing faith the transition can happen quickly. Last year, the study found that nearly 1 in 5 thought the smart cities model would be widespread in American cities within the next five years. This year, not even 1 in 10 believe that timeline is achievable.

Instead, more than a third now believe the implementation could take a decade. Nearly a quarter believe it could take 15 years. More than 80% believe the U.S. is lagging the world in the smart cities revolution.

What’s holding them back
Part of the problem may be a big knowledge gap. While people responding to the survey say they understand the potential, more than half say their city still doesn’t understand what it means to be a “smart city.”

And while half the cities and utilities are assessing their readiness — a third are even working on roadmaps — nearly two-thirds still don’t understand where the payoff point is. That may be adding to the money woes….(More)”

Ziad Al Achkar, Isaac L. Baker, Nathaniel A. Raymond at Harvard Humanitarian Initiative: “At present, accepted methodologies for wind disaster damage assessments rely almost exclusively on responders having ground access to the affected area to document damage to housing structures.  This approach can prove both time consuming and inefficient, and does not support the use of drones and satellites.

Geospatially-based damage assessments offer potential improvements to this process in terms of providing responding agencies with previously unavailable information about hard to reach, often non-permissive environments, at a scale and speed not possible through ground-based counts of damaged structures.

This guide provides the first standard method for conducting these types of damage assessments through the analysis of drone and satellite imagery. The “BAR Methodology” has been developed by the Signal Program on Human Security and Technology at HHI to address this critical gap in this evolving area humanitarian practice….(More)”

Wenjie Wu Jianghao Wang & Tianshi Dai  in Annals of the American Association of Geographers: “A largely unexplored big data application in urban contexts is how cultural ties affect human mobility patterns. This article explores China’s intercity human mobility patterns from social media data to contribute to our understanding of this question. Exposure to human mobility patterns is measured by big data computational strategy for identifying hundreds of millions of individuals’ space–time footprint trajectories. Linguistic data are coded as a proxy for cultural ties from a unique geographically coded atlas of dialect distributions. We find that cultural ties are associated with human mobility flows between city pairs, contingent on commuting costs and geographical distances. Such effects are not distributed evenly over time and space, however. These findings present useful insights in support of the cultural mechanism that can account for the rise, decline, and dynamics of human mobility between regions….(More)”

“Crowd2Map Tanzania is a new crowdsourcing initiative aimed at creating a comprehensive map ofTanzania, including detailed depictions of all of its villages, roads and public resources (such as schools, shops, offices etc.) in OpenStreetMap and/or Google Maps, both of which are sadly rather poor at the moment. (For a convincing example, see our post about a not-so-blank-as-map-suggests Zeze village here.)

…In February 2016, Crowd2Map Tanzania was one of the 7 projects selected in the Open Seventeenchallenge, which rallies the public to use open data as a means of achieving the 17 SustainableDevelopment Goals as proposed but the UN in September 2015! We are now excited to carry on with the helpof O17 partners – Citizen Cyberlab, The GovLab, ONE and SciFabric! We’re tackling Goal 11: creatingsustainable cities & communities and Goal 4: education through technology….(More)

Richard A. Berk, Susan B. Sorenson and Geoffrey Barnes in the The Journal of Empirical Legal Studies: “Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to public safety can be a key factor in that decision. Implicitly, a forecast of “future dangerousness” is required. In this article, we consider in particular whether usefully accurate forecasts of domestic violence can be obtained. We apply machine learning to data on over 28,000 arraignment cases from a major metropolitan area in which an offender faces domestic violence charges. One of three possible post-arraignment outcomes is forecasted within two years: (1) a domestic violence arrest associated with a physical injury, (2) a domestic violence arrest not associated with a physical injury, and (3) no arrests for domestic violence. We incorporate asymmetric costs for different kinds of forecasting errors so that very strong statistical evidence is required before an offender is forecasted to be a good risk. When an out-of-sample forecast of no post-arraignment domestic violence arrests within two years is made, it is correct about 90 percent of the time. Under current practice within the jurisdiction studied, approximately 20 percent of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. If magistrates used the methods we have developed and released only offenders forecasted not to be arrested for domestic violence within two years after an arraignment, as few as 10 percent might be arrested. The failure rate could be cut nearly in half. Over a typical 24-month period in the jurisdiction studied, well over 2,000 post-arraignment arrests for domestic violence perhaps could be averted….(More)”

Joshua Wilwohl in Forbes: “A new mobile and web application will help Cambodians better track complaints registered with local governments, but part of the app’s effectiveness hinges on whether the country’s leaders are receptive to the technology.

Known as Transmit, the app works by allowing selected government and grassroots leaders to enter in complaints made by citizens during routine community council meetings.

The app then sends the complaints to an online database. Once in the database, the government officials referenced by the issues can address them and indicate the status of the complaints.

The database is public and offers registered users the opportunity to comment on the complaints.

Currently, citizens register complaints with pen and paper or in a spreadsheet on an official’s computer….

Earlier this month, Pact began training officials in Pursat province to use the app and will expand training this week to local governments and community-based organizations in Kampong Cham, Battambang and Mondulkiri provinces, saidCenter.

But the app relies on government officials using the technology to keep the community informed about the progress of the complaints—a task that may be easier said than done in a country that is well-documented for its lack of transparency…(More)”

Tom Rosenstiel for the Brookings Center for Effective Public Management: “The path toward sustainable journalism, already challenged by a disrupted advertising business model, is also being undermined by something more unexpected—terrible data.

Analytics—another word for audience data or metrics—was supposed to offer the promise that journalists would be able to understand consumers at a deeper level. Journalism would be more connected and relevant as news people could see what audiences really wanted. Handled well, this should have helped journalists pursue what is at its core their fundamental challenge: learning how to make the significant interesting and the interesting more significant.

But a generation into the digital age, the problem associated with analytics isn’t the one that some feared—the discovery that audiences only care to be entertained and distracted. The bigger problem is that most web analytics are a mess. Designed for other purposes, the metrics used to understand publishing today offer too little information that is useful to journalists or to publishers on the business side. They mostly measure the wrong things. They also to a large extent measure things that are false or illusory.

As an example, the metric we have taken to call “unique visitors” is not what it sounds. Unique visitors are not different people. Instead, this metric measures devices; the same person who visits a publication on a phone, a tablet, and a computer is counted as three unique visitors. If they clean their cookies they are counted all over again. The traffic to most websites is probably over counted by more than double, perhaps more than triple

Time spent per article, in contrast, might offer a sense of depth of interest in a particular piece. But by itself it might also mean that someone stopped reading and walked away from the computer. Page views can tell a publisher how many times an individual piece of content was viewed. But views cannot tell the publisher why. Using conventional analytics, every story is an anecdote. Publishers may look at popular stories and say let’s do more like those. But they are largely inferring what “like those” means….(More)”

“The Digital Civil Society Lab at Stanford created digitalIMPACT.io to support civil society organizations in using digital data ethically, safely, and effectively. The content and tools on the site come from nonprofit and foundation partners.

digitalIMPACT.io is designed to help you learn from and share with others. The materials are provided as examples to inform your decision-making, organizational practice, and policy creation. We invite you to use and adapt what you find here, and hope you will share the practices and policies that you’ve developed. This website is only a start; real change will come as organizations integrate appropriate data management and governance throughout their work.

Digital data hold tremendous promise for civil society and they also raise new challenges. Think of digital data as both assets and liabilities. It’s time to start managing them to help you achieve your mission…. (More)”