Designing the Next Generation of Open Data Policy


Andrew Young and Stefaan Verhulst at the Open Data Charter Blog: “The international Open Data Charter has emerged from the global open data community as a galvanizing document to place open government data directly in the hands of citizens and organizations. To drive this process forward, and ensure that the outcomes are both systemic and transformational, new open data policy needs to be based on evidence of how and when open data works in practice. To support this work, the GovLab, in collaboration with Omidyar Network, has recently completed research which provides vital evidence of open data projects around the world, including an analysis of 19 in-depth, impact-focused case studies and a key findings paper. All of the research is now available in an eBook published by O’Reilly Media.

The research found that open data is making an impact in four core ways, including:…(More)”

Twitter, UN Global Pulse announce data partnership


PressRelease: “Twitter and UN Global Pulse today announced a partnership that will provide the United Nations with access to Twitter’s data tools to support efforts to achieve the Sustainable Development Goals, which were adopted by world leaders last year.

Every day, people around the world send hundreds of millions of Tweets in dozens of languages. This public data contains real-time information on many issues including the cost of food, availability of jobs, access to health care, quality of education, and reports of natural disasters. This partnership will allow the development and humanitarian agencies of the UN to turn these social conversations into actionable information to aid communities around the globe.

“The Sustainable Development Goals are first and foremost about people, and Twitter’s unique data stream can help us truly take a real-time pulse on priorities and concerns — particularly in regions where social media use is common — to strengthen decision-making. Strong public-private partnerships like this show the vast potential of big data to serve the public good,” said Robert Kirkpatrick, Director of UN Global Pulse.

“We are incredibly proud to partner with the UN in support of the Sustainable Development Goals,” said Chris Moody, Twitter’s VP of Data Services. “Twitter data provides a live window into the public conversations that communities around the world are having, and we believe that the increased potential for research and innovation through this partnership will further the UN’s efforts to reach the Sustainable Development Goals.”

Organizations and business around the world currently use Twitter data in many meaningful ways, and this unique data source enables them to leverage public information at scale to better inform their policies and decisions. These partnerships enable innovative uses of Twitter data, while protecting the privacy and safety of Twitter users.

UN Global Pulse’s new collaboration with Twitter builds on existing R&D that has shown the power of social media for social impact, like measuring the impact of public health campaigns, tracking reports of rising food prices, or prioritizing needs after natural disasters….(More)”

How Technology is Crowd-Sourcing the Fight Against Hunger


Beth Noveck at Media Planet: “There is more than enough food produced to feed everyone alive today. Yet access to nutritious food is a challenge everywhere and depends on getting every citizen involved, not just large organizations. Technology is helping to democratize and distribute the job of tackling the problem of hunger in America and around the world.

Real-time research

One of the hardest problems is the difficulty of gaining real-time insight into food prices and shortages. Enter technology. We no longer have to rely on professional inspectors slowly collecting information face-to-face. The UN World Food Programme, which provides food assistance to 80 million people each year, together with Nielsen is conducting mobile phone surveys in 15 countries (with plans to expand to 30), asking people by voice and text about what they are eating. Formerly blank maps are now filled in with information provided quickly and directly by the most affected people, making it easy to prioritize the allocation of resources.

Technology helps the information flow in both directions, enabling those in need to reach out, but also to become more effective at helping themselves. The Indian Ministry of Agriculture, in collaboration with Reuters Market Light, provides information services in nine Indian languages to 1.4 million registered farmers in 50,000 villages across 17 Indian states via text and voice messages.

“In the United States, 40 percent of the food produced here is wasted, and yet 1 in 4 American children (and 1 in 6 adults) remain food insecure…”

Data to the people

New open data laws and policies that encourage more transparent publication of public information complement data collection and dissemination technologies such as phones and tablets. About 70 countries and hundreds of regions and cities have adopted open data policies, which guarantee that the information these public institutions collect be available for free use by the public. As a result, there are millions of open datasets now online on websites such as the Humanitarian Data Exchange, which hosts 4,000 datasets such as country-by-country stats on food prices and undernourishment around the world.

Companies are compiling and sharing data to combat food insecurity, too. Anyone can dig into the data on the Global Open Data for Agriculture and Nutrition platform, a data collaborative where 300 private and public partners are sharing information.

Importantly, this vast quantity of open data is available to anyone, not only to governments. As a result, large and small entrepreneurs are able to create new apps and programs to combat food insecurity, such as Plantwise, which uses government data to offer a knowledge bank and run “plant clinics” that help farmers lose less of what they grow to pests. Google uses open government data to show people the location of farmers markets near their homes.

Students, too, can learn to play a role. For the second summer in a row, the Governance Lab at New York University, in partnership with the United States Department of Agriculture (USDA), mounted a two-week open data summer camp for 40 middle and high school students. The next generation of problem solvers is learning new data science skills by working on food safety and other projects using USDA open data.

Enhancing connection

Ultimately, technology enables greater communication and collaboration among the public, social service organizations, restaurants, farmers and other food producers who must work together to avoid food crises. The European Food Safety Authority in Italy has begun exploring how to use internet-based collaboration (often called citizen science or crowdsourcing) to get more people involved in food and feed risk assessment.

In the United States, 40 percent of the food produced here is wasted, and yet 1 in 4 American children (and 1 in 6 adults) remain food insecure, according to the Rockefeller Foundation. Copia, a San Francisco based smartphone app facilitates donations and deliveries of those with excess food in six cities in the Bay Area. Zero Percent in Chicago similarly attacks the distribution problem by connecting restaurants to charities to donate their excess food. Full Harvest is a tech platform that facilitates the selling of surplus produce that otherwise would not have a market.

Mobilizing the world

Prize-backed challenges create the incentives for more people to collaborate online and get involved in the fight against hunger….(More)”

Living in the World of Both/And


Essay by Adene Sacks & Heather McLeod Grant  in SSIR: “In 2011, New York Times data scientist Jake Porway wrote a blog post lamenting the fact that most data scientists spend their days creating apps to help users find restaurants, TV shows, or parking spots, rather than addressing complicated social issues like helping identify which teens are at risk of suicide or creating a poverty index of Africa using satellite data.

That post hit a nerve. Data scientists around the world began clamoring for opportunities to “do good with data.” Porway—at the center of this storm—began to convene these scientists and connect them to nonprofits via hackathon-style events called DataDives, designed to solve big social and environmental problems. There was so much interest, he eventually quit his day job at the Times and created the organization DataKind to steward this growing global network of data science do-gooders.

At the same time, in the same city, another movement was taking shape—#GivingTuesday, an annual global giving event fueled by social media. In just five years, #GivingTuesday has reshaped how nonprofits think about fundraising and how donors give. And yet, many don’t know that 92nd Street Y (92Y)—a 140-year-old Jewish community and cultural center in Manhattan, better known for its star-studded speaker series, summer camps, and water aerobics classes—launched it.

What do these two examples have in common? One started as a loose global network that engaged data scientists in solving problems, and then became an organization to help support the larger movement. The other started with a legacy organization, based at a single site, and catalyzed a global movement that has reshaped how we think about philanthropy. In both cases, the founding groups have incorporated the best of both organizations and networks.

Much has been written about the virtues of thinking and acting collectively to solve seemingly intractable challenges. Nonprofit leaders are being implored to put mission above brand, build networks not just programs, and prioritize collaboration over individual interests. And yet, these strategies are often in direct contradiction to the conventional wisdom of organization-building: differentiating your brand, developing unique expertise, and growing a loyal donor base.

A similar tension is emerging among network and movement leaders. These leaders spend their days steering the messy process required to connect, align, and channel the collective efforts of diverse stakeholders. It’s not always easy: Those searching to sustain movements often cite the lost momentum of the Occupy movement as a cautionary note. Increasingly, network leaders are looking at how to adapt the process, structure, and operational expertise more traditionally associated with organizations to their needs—but without co-opting or diminishing the energy and momentum of their self-organizing networks…

Welcome to the World of “Both/And”

Today’s social change leaders—be they from business, government, or nonprofits—must learn to straddle the leadership mindsets and practices of both networks and organizations, and know when to use which approach. Leaders like Porway, and Henry Timms and Asha Curran of 92Y can help show us the way.

How do these leaders work with the “both/and” mindset?

First, they understand and leverage the strengths of both organizations and networks—and anticipate their limitations. As Timms describes it, leaders need to be “bilingual” and embrace what he has called “new power.” Networks can be powerful generators of new talent or innovation around complex multi-sector challenges. It’s useful to take a network approach when innovating new ideas, mobilizing and engaging others in the work, or wanting to expand reach and scale quickly. However, networks can dissipate easily without specific “handrails,” or some structure to guide and support their work. This is where they need some help from the organizational mindset and approach.

On the flip side, organizations are good at creating centralized structures to deliver products or services, manage risk, oversee quality control, and coordinate concrete functions like communications or fundraising. However, often that efficiency and effectiveness can calcify over time, becoming a barrier to new ideas and growth opportunities. When organizational boundaries are too rigid, it is difficult to engage the outside world in ideating or mobilizing on an issue. This is when organizations need an infusion of the “network mindset.”

 

…(More)

Beware of the gaps in Big Data


Edd Gent at E&T: “When the municipal authority in charge of Boston, Massachusetts, was looking for a smarter way to find which roads it needed to repair, it hit on the idea of crowdsourcing the data. The authority released a mobile app called Street Bump in 2011 that employed an elegantly simple idea: use a smartphone’s accelerometer to detect jolts as cars go over potholes and look up the location using the Global Positioning System. But the approach ran into a pothole of its own.The system reported a disproportionate number of potholes in wealthier neighbourhoods. It turned out it was oversampling the younger, more affluent citizens who were digitally clued up enough to download and use the app in the first place. The city reacted quickly, but the incident shows how easy it is to develop a system that can handle large quantities of data but which, through its own design, is still unlikely to have enough data to work as planned.

As we entrust more of our lives to big data analytics, automation problems like this could become increasingly common, with their errors difficult to spot after the fact. Systems that ‘feel like they work’ are where the trouble starts.

Harvard University professor Gary King, who is also founder of social media analytics company Crimson Hexagon, recalls a project that used social media to predict unemployment. The model was built by correlating US unemployment figures with the frequency that people used words like ‘jobs’, ‘unemployment’ and ‘classifieds’. A sudden spike convinced researchers they had predicted a big rise in joblessness, but it turned out Steve Jobs had died and their model was simply picking up posts with his name. “This was an example of really bad analytics and it’s even worse because it’s the kind of thing that feels like it should work and does work a little bit,” says King.

Big data can shed light on areas with historic information deficits, and systems that seem to automatically highlight the best course of action can be seductive for executives and officials. “In the vacuum of no decision any decision is attractive,” says Jim Adler, head of data at Toyota Research Institute in Palo Alto. “Policymakers will say, ‘there’s a decision here let’s take it’, without really looking at what led to it. Was the data trustworthy, clean?”…(More)”

Data and Analytics Innovation


GAO report from the Data and Analytics Innovation Forum Convened by the Comptroller General of the United States: “….discussions considered the implications of new data-related technologies and developments that are revolutionizing the basic three-step innovation process in the figure below. As massive amounts of varied data become available in many fields, data generation (step 1 in the process) is transformed. Continuing technological advances are bringing more powerful analytics and changing analysis possibilities (step 2 in the process). And approaches to new decision making include intelligent machines that may, for example, guide human decision makers. Additionally, data may be automatically generated on actions taken in response to data analytic results, creating an evaluative feedback loop.
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Forum participants:

• saw the newly revolutionized and still-evolving process of data and analytics innovation (DAI) as generating far-reaching new economic opportunities, including a new Industrial Revolution based on combining data-transmitting cyber systems and physical systems, resulting in cyber-physical systems—which have alternatively been termed the Industrial Internet, also the Internet of Things;

• warned of an ongoing and potentially widening mismatch between the kinds of jobs that are or will be available and the skill levels of the U.S. labor force;

• identified beneficial DAI impacts that could help efforts to reach key societal goals—through defining DAI pathways to greater efficiency and effectiveness—in areas such as

• saw the newly revolutionized and still-evolving process of data and analytics innovation (DAI) as generating far-reaching new economic opportunities, including a new Industrial Revolution based on combining data-transmitting cyber systems and physical systems, resulting in cyber-physical systems—which have alternatively been termed the Industrial Internet, also the Internet of Things;

• warned of an ongoing and potentially widening mismatch between the kinds of jobs that are or will be available and the skill levels of the U.S. labor force; • identified beneficial DAI impacts that could help efforts to reach key societal goals—through defining DAI pathways to greater efficiency and effectiveness—in areas such as

• identified beneficial DAI impacts that could help efforts to reach key societal goals—through defining DAI pathways to greater efficiency and effectiveness—in areas such as health care, transportation, financial markets, and “smart cities,” among others; and

• outlined areas of data-privacy concern, including for example, possible threats to personal autonomy, which could occur as data on individual persons are collected and used without their knowledge or against their will.

The overall goal of the forum’s discussions and of this report is to help lay the groundwork for future efforts to maximize DAI benefits and minimize potential drawbacks. As such, the forum was not directed toward identifying a specific set of policies relevant to DAI. However, participants suggested that efforts to help realize the promise of DAI opportunities would be directed toward improving data access, assessing the validity of new data and models, creating a welcoming DAI ecosystem, and more generally, raising awareness of DAI’s potential among both policymakers and the general public. Participants also noted a likely need for higher U.S. educational achievement and a measured approach to privacy issues that recognizes both their import and their complexity….(More)”

Open Government Implementation Model


Open Government Implementation ModelKDZ: “The City of Vienna was the first public agency in a German speaking country to develop an Open Government Initative and to commit itself to the concept of Open Data – an open and transparent system that makes city data available to citizens for their further use. Vienna’s first Open Data catalogue has been presented to the public.

The KDZ – Centre for Public Administration Research was contracted by the Chief Executive Office of Vienna to contribute to the Open Government strategy of the City of Vienna. In order to bring the insights and propositions gained to the attention of a wider public, the Open Government Implementation Model  has been translated into English.

The KDZ Implementation Model is based on and significantly elaborates the “Open Government Implementation Model” by Lee/Kwak (2011). …(More)

See also:

Responsible Data in Agriculture


Report by Lindsay Ferris and Zara Rahman for GODAN: “The agriculture sector is creating increasing amounts of data, from many different sources. From tractors equipped with GPS tracking, to open data released by government ministries, data is becoming ever more valuable, as agricultural business development and global food policy decisions are being made based upon data. But the sector is also home to severe resource inequality. The largest agricultural companies make billions of dollars per year, in comparison with subsistence farmers growing just enough to feed themselves, or smallholder farmers who grow enough to sell on a year-by-year basis. When it comes to data and technology, these differences in resources translate to stark power imbalances in data access and use. The most well resourced actors are able to delve into new technologies and make the most of those insights, whereas others are unable to take any such risks or divert any of their limited resources. Access to and use of data has radically changed the business models and behaviour of some of those well resourced actors, but in contrast, those with fewer resources are receiving the same, limited access to information that they always have.

In this paper, we have approached these issues from a responsible data perspective, drawing upon the experience of the Responsible Data community1 who over the past three years have created tools, questions and resources to deal with the ethical, legal, privacy and security challenges that come from new uses of data in various sectors. This piece aims to provide a broad overview of some of the responsible data challenges facing these actors, with a focus on the power imbalance between actors, and looking into how that inequality affects behaviour when it comes to the agricultural data ecosystem. What are the concerns of those with limited resources, when it comes to this new and rapidly changing data environment? In addition, what are the ethical grey areas or uncertainties that we need to address in the future? As a first attempt to answer these questions, we spoke to 14 individuals with various perspectives on the sector to understand what the challenges are for them and for the people they work with. We also carried out desk research to dive deeper into these issues, and we provide here an analysis of our findings and responsible data challenges….(More)”

Infostorms. Why do we ‘like’? Explaining individual behavior on the social net.


Book by Hendricks, Vincent F. and  Hansen, Pelle G.: “With points of departure in philosophy, logic, social psychology, economics, and choice and game theory, Infostorms shows how information may be used to improve the quality of personal decision and group thinking but also warns against the informational pitfalls which modern information technology may amplify: From science to reality culture and what it really is, that makes you buy a book like this.

The information society is upon us. New technologies have given us back pocket libraries, online discussion forums, blogs, crowdbased opinion aggregators, social media and breaking news wherever, whenever. But are we more enlightened and rational because of it?

Infostorms provides the nuts and bolts of how irrational group behaviour may get amplified by social media and information technology. If we could be collectively dense before, now we can do it at light speed and with potentially global reach. That’s how things go viral, that is how cyberbullying, rude comments online, opinion bubbles, status bubbles, political polarisation and a host of other everyday unpleasantries start. Infostorms will give the story of the mechanics of these phenomena. This will help you to avoid them if you want or learn to start them if you must. It will allow you to stay sane in an insane world of information….(More)”

How to advance open data research: Towards an understanding of demand, users, and key data


Danny Lämmerhirt and Stefaan Verhulst at IODC blog: “…Lord Kelvin’s famous quote “If you can not measure it, you can not improve it” equally applies to open data. Without more evidence of how open data contributes to meeting users’ needs and addressing societal challenges, efforts and policies toward releasing and using more data may be misinformed and based upon untested assumptions.

When done well, assessments, metrics, and audits can guide both (local) data providers and users to understand, reflect upon, and change how open data is designed. What we measure and how we measure is therefore decisive to advance open data.

Back in 2014, the Web Foundation and the GovLab at NYU brought together open data assessment experts from Open Knowledge, Organisation for Economic Co-operation and Development, United Nations, Canada’s International Development Research Centre, and elsewhere to explore the development of common methods and frameworks for the study of open data. It resulted in a draft template or framework for measuring open data. Despite the increased awareness for more evidence-based open data approaches, since 2014 open data assessment methods have only advanced slowly. At the same time, governments publish more of their data openly, and more civil society groups, civil servants, and entrepreneurs employ open data to manifold ends: the broader public may detect environmental issues and advocate for policy changes, neighbourhood projects employ data to enable marginalized communities to participate in urban planning, public institutions may enhance their information exchange, and entrepreneurs embed open data in new business models.

In 2015, the International Open Data Conference roadmap made the following recommendations on how to improve the way we assess and measure open data.

  1. Reviewing and refining the Common Assessment Methods for Open Data framework. This framework lays out four areas of inquiry: context of open data, the data published, use practices and users, as well as the impact of opening data.
  2. Developing a catalogue of assessment methods to monitor progress against the International Open Data Charter (based on the Common Assessment Methods for Open Data).
  3. Networking researchers to exchange common methods and metrics. This helps to build methodologies that are reproducible and increase credibility and impact of research.
  4. Developing sectoral assessments.

In short, the IODC called for refining our assessment criteria and metrics by connecting researchers, and applying the assessments to specific areas. It is hard to tell how much progress has been made in answering these recommendations, but there is a sense among researchers and practitioners that the first two goals are yet to be fully addressed.

Instead we have seen various disparate, yet well meaning, efforts to enhance the understanding of the release and impact of open data. A working group was created to measure progress on the International Open Data Charter, which provides governments with principles for implementing open data policies. While this working group compiled a list of studies and their methodologies, it did not (yet) deepen the common framework of definitions and criteria to assess and measure the implementation of the Charter.

In addition, there is an increase of sector- and case-specific studies that are often more descriptive and context specific in nature, yet do contribute to the need for examples that illustrate the value proposition for open data.

As such, there seems to be a disconnect between top-level frameworks and on-the-ground research, preventing the sharing of common methods and distilling replicable experiences about what works and what does not….(More)”