Stefaan Verhulst

Joe McKendrick at ZDNet: “Open data is one of those refreshing trends that flows in the opposite direction of the culture of fear that has developed around data security. Instead of putting data under lock and key, surrounded by firewalls and sandboxes, some organizations see value in making data available to all comers — especially developers.

The GovLab.org, a nonprofit advocacy group, published an overview of the benefits governments and organizations are realizing from open data, as well as some of the challenges. The group defines open data as “publicly available data that can be universally and readily accessed, used and redistributed free of charge. It is structured for usability and computability.”…

For enterprises, an open-data stance may be the fuel to build a vibrant ecosystem of developers and business partners. Scott Feinberg, API architect for The New York Times, is one of the people helping to lead the charge to open-data ecosystems. In a recent CXOTalk interview with ZDNet colleague Michael Krigsman, he explains how through the NYT APIs program, developers can sign up for access to 165 years worth of content.

But it requires a lot more than simply throwing some APIs out into the market. Establishing such a comprehensive effort across APIs requires a change in mindset that many organizations may not be ready for, Feinberg cautions. “You can’t be stingy,” he says. “You have to just give it out. When we launched our developer portal there’s a lot of questions like, are people going to be stealing our data, questions like that. Just give it away. You don’t have to give it all but don’t be stingy, and you will find that first off not that many people are going to use it at first. you’re going to find that out, but the people who do, you’re going to find those passionate people who are really interested in using your data in new ways.”

Feinberg clarifies that the NYT’s APIs are not giving out articles for free. Rather, he explains, “we give is everything but article content. You can search for articles. You can find out what’s trending. You can almost do anything you want with our data through our APIs with the exception of actually reading all of the content. It’s really about giving people the opportunity to really interact with your content in ways that you’ve never thought of, and empowering your community to figure out what they want. You know while we don’t give our actual article text away, we give pretty much everything else and people build a lot of really cool stuff on top of that.”

Open data sets, of course, have to worthy of the APIs that offer them. In his post, Borne outlines the seven qualities open data needs to have to be of value to developers and consumers. (Yes, they’re also “Vs” like big data.)

1. Validity: It’s “critical to pay attention to these data validity concerns when your organization’s data are exposed to scrutiny and inspection by others,” Borne states.
2. Value: The data needs to be the font of new ideas, new businesses, and innovations.
3. Variety: Exposing the wide variety of data available can be “a scary proposition for any data scientist,” Borne observes, but nonetheless is essential.
4. Voice: Remember that “your open data becomes the voice of your organization to your stakeholders.”
5. Vocabulary: “The semantics and schema (data models) that describe your data are more critical than ever when you provide the data for others to use,” says Borne. “Search, discovery, and proper reuse of data all require good metadata, descriptions, and data modeling.”
6. Vulnerability: Accept that open data, because it is so open, will be subjected to “misuse, abuse, manipulation, or alteration.”
7. proVenance: This is the governance requirement behind open data offerings. “Provenance includes ownership, origin, chain of custody, transformations that been made to it, processing that has been applied to it (including which versions of processing software were used), the data’s uses and their context, and more,” says Borne….(More)”

By Prianka Srinivasan and Stefaan G. Verhulst *

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data and humanitarian response was originally published in 2016.

Data, when used well in a trusted manner, allows humanitarian organizations to innovate how to respond to emergency events, including better coordination of post-disaster relief efforts, the ability to harness local knowledge to create more targeted relief strategies, and tools to predict and monitor disasters in real time. Consequently, in recent years both multinational groups and community-based advocates have begun to integrate data collection and evaluation strategies into their humanitarian operations, to better and more quickly respond to emergencies. However, this movement poses a number of challenges. Compared to the private sector, humanitarian organizations are often less equipped to successfully analyze and manage big data, which pose a number of risks related to the security of victims’ data. Furthermore, complex power dynamics which exist within humanitarian spaces may be further exacerbated through the introduction of new technologies and big data collection mechanisms. In the below we share:

• Selected Reading List (summaries and hyperlinks)
• Annotated Selected Reading List
• Additional Readings

Selected Reading List  (summaries in alphabetical order)

Data and Humanitarian Response

• John Karlsrud – Peacekeeping 4.0: Harnessing the Potential of Big Data, Social Media, and Cyber Technologies – Recommends that UN peacekeeping initiatives should better integrate big data and new technologies into their operations, adopting a “Peacekeeping 4.0” for the modern world.
• Fancesco Mancini, International Peace Institute – New Technology and the prevention of Violence and Conflict – Explores the ways in which new tools available in communications technology can assist humanitarian workers in preventing violence and conflict.
• Patrick Meier – Digital Humanitarians- How Big Data is changing the face of humanitarian response – Profiles the emergence of ‘Digital Humanitarians’—humanitarian workers who are using big data, crowdsourcing and new technologies to transform the way societies respond to humanitarian disasters.
• Andrew Robertson and Steve Olson (USIP) – Using Data Sharing to Improve Coordination in Peacebuilding – Summarises the findings of a United States Institute of Peace workshop which investigated the use of data-sharing systems between government and non-government actors in conflict zones. It identifies some of the challenges and benefits of data-sharing in peacebuilding efforts.
• United Nations Independent Expert Advisory Group on a Data Revolution for Sustainable Development – A World That Counts, Mobilizing the Data Revolution – Compiled by a group of 20 international experts, this report proposes ways to improve data management and monitoring, whilst mitigating some of the risks data poses.
• Katie Whipkey and Andrej Verity – Guidance for Incorporating Big Data into Humanitarian Operations – Created as part of the Digital Humanitarian Network with the support of UN-OCHA, this is a manual for humanitarian organizations looking to strategically incorporate Big Data into their work.

Risks of Using Big Data in Humanitarian Context

• Kate Crawford and Megan Finn – The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters – Analyzes the use of big data techniques following a crisis event, arguing that a reliance of social and mobile data can lead to significant oversights and ethical concerns in the wake of humanitarian disasters.
• Katja Lindskov Jacobsen – Making design safe for citizens: A hidden history of humanitarian experimentation – Argues that the UNHCR’s use of iris recognition technology in 2002 and 2007 during the repatriation of Afghan refugees from Pakistan constitutes a case of “humanitarian experimentation.” It questions this sort of experimentation which compromises the security of refugees in the pursuit of safer technologies for the rest of the world.
• Responsible Data Forum – Responsible Data Reflection Stories: an Overview – compiles various stories sourced by the Responsible Data Forum blog relating to data challenges faced by advocacy organizations, and draws recommendations based on these cases.
• Kristin Bergtora Sandvik – The humanitarian cyberspace: shrinking space or an expanding frontier? – Provides a detailed account of the development of a “humanitarian cyberspace” and how information and communication technologies have been further integrated into humanitarian operations since the mid-1990s.

Annotated Selected Reading List (in alphabetical order)

Karlsrud, John. “Peacekeeping 4.0: Harnessing the Potential of Big Data, Social Media, and Cyber Technologies.” Cyberspace and International Relations, 2013. http://bit.ly/235Qb3e

• This chapter from the book “Cyberspace and International Relations” suggests that advances in big data give humanitarian organizations unprecedented opportunities to prevent and mitigate natural disasters and humanitarian crises. However, the sheer amount of unstructured data necessitates effective “data mining” strategies for multinational organizations to make the most use of this data.
• By profiling some civil-society organizations who use big data in their peacekeeping efforts, Karlsrud suggests that these community-focused initiatives are leading the movement toward analyzing and using big data in countries vulnerable to crisis.
• The chapter concludes by offering ten recommendations to UN peacekeeping forces to best realize the potential of big data and new technology in supporting their operations.

Mancini, Fancesco. “New Technology and the prevention of Violence and Conflict.” International Peace Institute, 2013. http://bit.ly/1ltLfNV

• This report from the International Peace Institute looks at five case studies to assess how information and communications technologies (ICTs) can help prevent humanitarian conflicts and violence. Their findings suggest that context has a significant impact on the ability for these ICTs for conflict prevention, and any strategies must take into account the specific contingencies of the region to be successful.
• The report suggests seven lessons gleaned from the five case studies:
  • New technologies are just one in a variety of tools to combat violence. Consequently, organizations must investigate a variety of complementary strategies to prevent conflicts, and not simply rely on ICTs.
  • Not every community or social group will have the same relationship to technology, and their ability to adopt new technologies are similarly influenced by their context. Therefore, a detailed needs assessment must take place before any new technologies are implemented.
  • New technologies may be co-opted by violent groups seeking to maintain conflict in the region. Consequently, humanitarian groups must be sensitive to existing political actors and be aware of possible negative consequences these new technologies may spark.
  • Local input is integral to support conflict prevention measures, and there exists need for collaboration and awareness-raising with communities to ensure new technologies are sustainable and effective.
  • Information shared between civil-society has more potential to develop early-warning systems. This horizontal distribution of information can also allow communities to maintain the accountability of local leaders.

Meier, Patrick. “Digital humanitarians: how big data is changing the face of humanitarian response.” Crc Press, 2015. http://amzn.to/1RQ4ozc

• This book traces the emergence of “Digital Humanitarians”—people who harness new digital tools and technologies to support humanitarian action. Meier suggests that this has created a “nervous system” to connect people from disparate parts of the world, revolutionizing the way we respond to humanitarian crises.
• Meier argues that such technology is reconfiguring the structure of the humanitarian space, where victims are not simply passive recipients of aid but can contribute with other global citizens. This in turn makes us more humane and engaged people.

Robertson, Andrew and Olson, Steve. “Using Data Sharing to Improve Coordination in Peacebuilding.” United States Institute for Peace, 2012. http://bit.ly/235QuLm

• This report functions as an overview of a roundtable workshop on Technology, Science and Peace Building held at the United States Institute of Peace. The workshop aimed to investigate how data-sharing techniques can be developed for use in peace building or conflict management.
• Four main themes emerged from discussions during the workshop:
  • “Data sharing requires working across a technology-culture divide”—Data sharing needs the foundation of a strong relationship, which can depend on sociocultural, rather than technological, factors.
  • “Information sharing requires building and maintaining trust”—These relationships are often built on trust, which can include both technological and social perspectives.
  • “Information sharing requires linking civilian-military policy discussions to technology”—Even when sophisticated data-sharing technologies exist, continuous engagement between different stakeholders is necessary. Therefore, procedures used to maintain civil-military engagement should be broadened to include technology.
  • “Collaboration software needs to be aligned with user needs”—technology providers need to keep in mind the needs of its users, in this case peacebuilders, in order to ensure sustainability.

United Nations Independent Expert Advisory Group on a Data Revolution for Sustainable Development. “A World That Counts, Mobilizing the Data Revolution.” 2014. https://bit.ly/2Cb3lXq

• This report focuses on the potential benefits and risks data holds for sustainable development. Included in this is a strategic framework for using and managing data for humanitarian purposes. It describes a need for a multinational consensus to be developed to ensure data is shared effectively and efficiently.
• It suggests that “people who are counted”—i.e., those who are included in data collection processes—have better development outcomes and a better chance for humanitarian response in emergency or conflict situations.

Katie Whipkey and Andrej Verity. “Guidance for Incorporating Big Data into Humanitarian Operations.” Digital Humanitarian Network, 2015. http://bit.ly/1Y2BMkQ

• This report produced by the Digital Humanitarian Network provides an overview of big data, and how humanitarian organizations can integrate this technology into their humanitarian response. It primarily functions as a guide for organizations, and provides concise, brief outlines of what big data is, and how it can benefit humanitarian groups.
• The report puts forward four main benefits acquired through the use of big data by humanitarian organizations: 1) the ability to leverage real-time information; 2) the ability to make more informed decisions; 3) the ability to learn new insights; 4) the ability for organizations to be more prepared.
• It goes on to assess seven challenges big data poses for humanitarian organizations: 1) geography, and the unequal access to technology across regions; 2) the potential for user error when processing data; 3) limited technology; 4) questionable validity of data; 5) underdeveloped policies and ethics relating to data management; 6) limitations relating to staff knowledge.

Risks of Using Big Data in Humanitarian Context
Crawford, Kate, and Megan Finn. “The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters.” GeoJournal 80.4, 2015. http://bit.ly/1X0F7AI

• Crawford & Finn present a critical analysis of the use of big data in disaster management, taking a more skeptical tone to the data revolution facing humanitarian response.
• They argue that though social and mobile data analysis can yield important insights and tools in crisis events, it also presents a number of limitations which can lead to oversights being made by researchers or humanitarian response teams.
• Crawford & Finn explore the ethical concerns the use of big data in disaster events introduces, including issues of power, privacy, and consent.
• The paper concludes by recommending that critical data studies, such as those presented in the paper, be integrated into crisis event research in order to analyze some of the assumptions which underlie mobile and social data.

Jacobsen, Katja Lindskov (2010) Making design safe for citizens: A hidden history of humanitarian experimentation. Citizenship Studies 14.1: 89-103. http://bit.ly/1YaRTwG

• This paper explores the phenomenon of “humanitarian experimentation,” where victims of disaster or conflict are the subjects of experiments to test the application of technologies before they are administered in greater civilian populations.
• By analyzing the particular use of iris recognition technology during the repatriation of Afghan refugees to Pakistan in 2002 to 2007, Jacobsen suggests that this “humanitarian experimentation” compromises the security of already vulnerable refugees in order to better deliver biometric product to the rest of the world.

Responsible Data Forum. “Responsible Data Reflection Stories: An Overview.” http://bit.ly/1Rszrz1

• This piece from the Responsible Data forum is primarily a compilation of “war stories” which follow some of the challenges in using big data for social good. By drawing on these crowdsourced cases, the Forum also presents an overview which makes key recommendations to overcome some of the challenges associated with big data in humanitarian organizations.
• It finds that most of these challenges occur when organizations are ill-equipped to manage data and new technologies, or are unaware about how different groups interact in digital spaces in different ways.

Sandvik, Kristin Bergtora. “The humanitarian cyberspace: shrinking space or an expanding frontier?” Third World Quarterly 37:1, 17-32, 2016. http://bit.ly/1PIiACK

• This paper analyzes the shift toward more technology-driven humanitarian work, where humanitarian work increasingly takes place online in cyberspace, reshaping the definition and application of aid. This has occurred along with what many suggest is a shrinking of the humanitarian space.
• Sandvik provides three interpretations of this phenomena:
  • First, traditional threats remain in the humanitarian space, which are both modified and reinforced by technology.
  • Second, new threats are introduced by the increasing use of technology in humanitarianism, and consequently the humanitarian space may be broadening, not shrinking.
  • Finally, if the shrinking humanitarian space theory holds, cyberspace offers one example of this, where the increasing use of digital technology to manage disasters leads to a contraction of space through the proliferation of remote services.

Additional Readings on Data and Humanitarian Response

• Kristin Bergtora Sandvik, et al. – Humanitarian technology: a critical research agenda. – Takes a critical look at the field of humanitarian technology, analyzing what challenges this poses to post-disaster and conflict environment.
• Kristin Bergtora Sandvik – “The Risks of Technological Innovation.” – Suggests that despite the evident benefits such technology presents, it can also undermine humanitarian action and lead to “catastrophic events” themselves needing a new type of humanitarian response.
• Ryan Burns – Rethinking big data in digital humanitarianism: practices, epistemologies, and social relations – Takes a critical look at the use of big data in humanitarian spaces, arguing that the advent of digital humanitarianism has profound political and social implications, and can in fact limit information available following a humanitarian crisis.
• Kate Crawford – Is Data a Danger to the Developing World? – Argues that it is not simply risks to privacy that data poses to developing countries, but suggests that “data discrimination” can affect even the basic human rights of individuals, and introduce problematic power hierarchies between those who can access data and those who cannot.
• Paul Currion – Eyes Wide Shut: The challenge of humanitarian biometrics – Examines the use of biometrics by humanitarian organizations and national governments, and suggests stronger accountability is needed to ensure data from marginalized groups remain protected.
• Yves-Alexandre de Montjoye, Jake Kendall and Cameron F. Kerry – Enabling Humanitarian Use of Mobile Phone Data – Analyzes how data from mobile communication can provide insights into the spread of infectious disease, and how such data can also compromise individual privacy.
• Michael F. Goodchild and Alan Glennon – Crowdsourcing geographic information for disaster response: a research frontier – Explores how though volunteered geographic data may be messy and unreliable, it can provide many benefits in emergency situations.
• Raphael Horler – Crowdsourcing in the Humanitarian Network – An Analysis of the Literature – A Bachelor thesis which explores the increasing use of crowdsourced data by organizations involved in disaster response, investigating some of the challenges such use of crowdsourcing poses.
• Gus Hosein and Carly Nyst – Aiding Surveillance – Suggests that the unregulated use of technologies and surveillance systems by humanitarian organizations create systems which pose serious threats to individuals’ rights, particularly their right to privacy.
• L. Jacobsen – The Politics of Humanitarian Technology: Good Intentions, Unintended Consequences and Insecurity – Raises concerns about the rise of data collection and digital technology in humanitarian aid organizations, arguing that its unquestioned prominence creates new structures of power and control, which remain hidden under the rubric of liberal humanitarianism.
• Mirca Madianou – Digital Inequality and Second-Order Disasters: Social Media in the Typhoon Haiyan Recovery – Taking the effects of Typhoon Haiyan as a key case study, this paper investigates how digital inequalities and an unequal access to data can exacerbate existing social inequalities in a post-disaster environment.
• Sean Martin McDonald – Ebola: A Big Data Disaster. Privacy, Property, and the Law of Disaster Experimentation – Analyzes the challenges and privacy risks of using unregulated data in public health coordination by taking the use of Call Detail Record (CDR) data during the Ebola crisis as a key case study.
• National Academy of Engineering – Sensing and Shaping Emerging Conflicts: Report of a Joint Workshop of the National Academy of Engineering and the United States Institute of Peace: Roundtable on Technology, Science, and Peacebuilding – Building on the overview report of the United States Institute of Peace workshop examines what opportunities new technologies and data sharing provides for humanitarian groups.
• Mary K.Pratt – Big Data’s role in humanitarian aid – A Computer World article which provides an overview of Big Data, and how it is improving the efficiency and efficacy of humanitarian response, especially in conflict zones.
• Bertrand Taithe Róisínand and Roger Mac Ginty – Data hubris? Humanitarian information systems and the mirage of technology – Specifically looks at visual technology and crisis mapping, and big data, and suggests that there exists an over-enthusiasm in these claims made on behalf of technologically advanced humanitarian information systems.
• Linnet Taylor – No place to hide? The ethics and analytics of tracking mobility using mobile phone data – Examines the ethical problems associated with the tracking of mobile phone data, especially in low or middle-income countries.
• UN Office for the Coordination of Humanitarian Affairs (UN-OCHA) – Big data and humanitarianism: 5 things you need to know – Briefly outlines five issues that face humanitarian organizations as they integrate big data into their operations.
• United Nations Global Pulse – Mapping the Risk-Utility Landscape of Mobile Data for Sustainable Development and Humanitarian Action – Reports on a Global Pulse project (done in partnership with Massachusetts Institute of Technology) which aimed to find how aggregated mobile data can be maximized to protect privacy and provide effective support to crisis response.
• The Wilson Center – Connecting Grassroots to Government for Disaster Management: Workshop Summary – Summarizes the key points drawn from a two day Wilson Center workshop, which investigated how new technologies could engage whole communities in disaster management.

* Thanks to: Kristen B. Sandvik; Zara Rahman; Jennifer Schulte; Sean McDonald; Paul Currion; Dinorah Cantú-Pedraza and the Responsible Data Listserve for valuable input.

Book edited by Georg Aichholzer, Herbert Kubicek and Lourdes Torres: “There is a widely acknowledged evaluation gap in the field of e-participation practice and research, a lack of systematic evaluation with regard to process organization, outcome and impacts. This book addresses the state of the art of e-participation research and the existing evaluation gap by reviewing various evaluation approaches and providing a multidisciplinary concept for evaluating the output, outcome and impact of citizen participation via the Internet as well as via traditional media. It offers new knowledge based on empirical results of its application (tailored to different forms and levels of e-participation) in an international comparative perspective. The book will advance the academic study and practical application of e-participation through fresh insights, largely drawing on theoretical arguments and empirical research results gained in the European collaborative project “e2democracy”. It applies the same research instruments to a set of similar citizen participation processes in seven local communities in three countries (Austria, Germany and Spain). The generic evaluation framework has been tailored to a tested toolset, and the presentation and discussion of related evaluation results aims at clarifying to what extent these tools can be applied to other consultation and collaboration processes, making the book of interest to policymakers and scholars alike….(More)”

“The Berkman Center is pleased to announce the publication of a new paper from the Privacy Tools for Sharing Research Data project team. In this paper, Effy Vayena, Urs Gasser, Alexandra Wood, and David O’Brien from the Berkman Center, with Micah Altman from MIT Libraries, outline elements of a new ethical framework for big data research.

Emerging large-scale data sources hold tremendous potential for new scientific research into human biology, behaviors, and relationships. At the same time, big data research presents privacy and ethical challenges that the current regulatory framework is ill-suited to address. In light of the immense value of large-scale research data, the central question moving forward is not whether such data should be made available for research, but rather how the benefits can be captured in a way that respects fundamental principles of ethics and privacy.

The authors argue that a framework with the following elements would support big data utilization and help harness the value of big data in a sustainable and trust-building manner:

• Oversight should aim to provide universal coverage of human subjects research, regardless of funding source, across all stages of the information lifecycle.

• New definitions and standards should be developed based on a modern understanding of privacy science and the expectations of research subjects.

• Researchers and review boards should be encouraged to incorporate systematic risk-benefit assessments and new procedural and technological solutions from the wide range of interventions that are available.

• Oversight mechanisms and the safeguards implemented should be tailored to the intended uses, benefits, threats, harms, and vulnerabilities associated with a specific research activity.

Development of a new ethical framework with these elements should be the product of a dynamic multistakeholder process that is designed to capture the latest scientific understanding of privacy, analytical methods, available safeguards, community and social norms, and best practices for research ethics as they evolve over time.

The full paper is available for download through the Washington and Lee Law Review Online as part of a collection of papers featured at the Future of Privacy Forum workshop Beyond IRBs: Designing Ethical Review Processes for Big Data Research held on December 10, 2015, in Washington, DC….(More)”

New book by The Tow Center: “This is a book about the principles behind data journalism. Not what visualization software to use and how to scrape a website, but the fundamental ideas that underlie the human use of data. This isn’t “how to use data” but “how data works.”

This gets into some of the mathy parts of statistics, but also the difficulty of taking a census of race and the cognitive psychology of probabilities. It traces where data comes from, what journalists do with it, and where it goes after—and tries to understand the possibilities and limitations. Data journalism is as interdisciplinary as it gets, which can make it difficult to assemble all the pieces you need. This is one attempt. This is a technical book, and uses standard technical language, but all mathematical concepts are explained through pictures and examples rather than formulas.

The life of data has three parts: quantification, analysis, and communication. Quantification is the process that creates data. Analysis involves rearranging the data or combining it with other information to produce new knowledge. And none of this is useful without communicating the result.

Quantification is a problem without a home. Although physicists study measurement extensively, physical theory doesn’t say much about how to quantify things like “educational attainment” or even “unemployment.” There are deep philosophical issues here, but the most useful question to a journalist is simply, how was this data created? Data is useful because it represents the world, but we can only understand data if we correctly understand how it came to be. Representation through data is never perfect: all data has error. Randomly sampled surveys are both a powerful quantification technique and the prototype for all measurement error, so this report explains where the margin of error comes from and what it means – from first principles, using pictures.

All data analysis is really data interpretation, which requires much more than math. Data needs context to mean anything at all: Imagine if someone gave you a spreadsheet with no column names. Each data set could be the source of many different stories, and there is no objective theory that tells us which true stories are the best. But the stories still have to be true, which is where data journalism relies on established statistical principles. The theory of statistics solves several problems: accounting for the possibility that the pattern you see in the data was purely a fluke, reasoning from incomplete and conflicting information, and attempting to isolate causes. Stats has been taught as something mysterious, but it’s not. The analysis chapter centers on a single problem – asking if an earlier bar closing time really did reduce assaults in a downtown neighborhood – and traces through the entire process of analysis by explaining the statistical principles invoked at each step, building up to the state-of-the-art methods of Bayesian inference and causal graphs.

A story isn’t isn’t finished until you’ve communicated your results. Data visualization works because it relies on the biology of human visual perception, just as all data communication relies on human cognitive processing. People tend to overestimate small risks and underestimate large risks; examples leave a much stronger impression than statistics; and data about some will, unconsciously, come to represent all, no matter how well you warn that your sample doesn’t generalize. If you’re not aware of these issues you can leave people with skewed impressions or reinforce harmful stereotypes. The journalist isn’t only responsible for what they put in the story, but what ends up in the mind of the audience.

This report brings together many fields to explore where data comes from, how to analyze it, and how to communicate your results. It uses examples from journalism to explain everything from Bayesian statistics to the neurobiology of data visualization, all in plain language with lots of illustrations. Some of these ideas are thousands of years old, some were developed only a decade ago, and all of them have come together to create the 21st century practice of data journalism….(More)”

Payal Arora at the International Journal of Communication: “To date, little attention has been given to the impact of big data in the Global South, about 60% of whose residents are below the poverty line. Big data manifests in novel and unprecedented ways in these neglected contexts. For instance, India has created biometric national identities for her 1.2 billion people, linking them to welfare schemes, and social entrepreneurial initiatives like the Ushahidi project that leveraged crowdsourcing to provide real-time crisis maps for humanitarian relief.

While these projects are indeed inspirational, this article argues that in the context of the Global South there is a bias in the framing of big data as an instrument of empowerment. Here, the poor, or the “bottom of the pyramid” populace are the new consumer base, agents of social change instead of passive beneficiaries. This neoliberal outlook of big data facilitating inclusive capitalism for the common good sidelines critical perspectives urgently needed if we are to channel big data as a positive social force in emerging economies. This article proposes to assess these new technological developments through the lens of databased democracies, databased identities, and databased geographies to make evident normative assumptions and perspectives in this under-examined context….(More)”.

Karen E.C. Levy and David Merritt Johns in Big Data and Society: “Openness and transparency are becoming hallmarks of responsible data practice in science and governance. Concerns about data falsification, erroneous analysis, and misleading presentation of research results have recently strengthened the call for new procedures that ensure public accountability for data-driven decisions. Though we generally count ourselves in favor of increased transparency in data practice, this Commentary highlights a caveat. We suggest that legislative efforts that invoke the language of data transparency can sometimes function as “Trojan Horses” through which other political goals are pursued. Framing these maneuvers in the language of transparency can be strategic, because approaches that emphasize open access to data carry tremendous appeal, particularly in current political and technological contexts. We illustrate our argument through two examples of pro-transparency policy efforts, one historical and one current: industry-backed “sound science” initiatives in the 1990s, and contemporary legislative efforts to open environmental data to public inspection. Rules that exist mainly to impede science-based policy processes weaponize the concept of data transparency. The discussion illustrates that, much as Big Data itself requires critical assessment, the processes and principles that attend it—like transparency—also carry political valence, and, as such, warrant careful analysis….(More)”

10 Lessons : “…The GovLab and its network of 25 world-class coaches and over 100 mentors helped 446 participants in more thana dozen US cities and thirty foreign countries to take a public interest technology project from idea to implementation. In the process, we ‘ve learned a lot about the need for new ways of training the next generation of leaders and problem solvers.

Our aim has been to aid public entrepreneurs — passionate and innovative people who wish to take advantage of new technology to do good in the world. That’s why we measure success, not by the number of participants in a class, but by the project’s participants create and the impact those projects have on communities….

Lesson 1: There is growing, and unmet, demand for training a new kind of public servant: the public entrepreneur…

Lesson 2: Tap the distributed supply of talent and expertise to accelerate learning…

Lesson 3:  Create new methods for training public entrepreneurs to solve problems…

Lesson 4:  Develop tools to help public interest innovators “cross the chasm” from idea to implementation…

Lesson 5:  Teach collaboration and partnering for change…

Lesson 6:  In order to be successful, public entrepreneurs must be able to define the problem — a skill widely lacking…

Lesson 7:  Connecting innovators and alumni with one another generates a lasting public infrastructure that can help solve problems more effectively…

Lesson 8:  Pedagogical priorities include making problem solving more data driven and evidence based….

Lesson 9:  The demand and supply are global — which requires a global mindset and platform in order to learn what has worked elsewhere and why…

Lesson 10:  Collaboration and coordination among anchor organizations is key to meeting the demand and coordinating the supply….(More)

Rebecca Lipman at Economist Intelligence Unit Perspectives on “One city tweets to stay dry: From drones to old-fashioned phone calls, data come from many unlikely sources. In a disaster, such as a flood or earthquake, responders will take whatever information they can get to visualise the crisis and best direct their resources. Increasingly, cities prone to natural disasters are learning to better aid their citizens by empowering their local agencies and responders with sophisticated tools to cut through the large volume and velocity of disaster-related data and synthesise actionable information.

Consider the plight of the metro area of Jakarta, Indonesia, home to some 28m people, 13 rivers and 1,100 km of canals. With 40% of the city below sea level (and sinking), and regularly subject to extreme weather events including torrential downpours in monsoon season, Jakarta’s residents face far-too-frequent, life-threatening floods. Despite the unpredictability of flooding conditions, citizens have long taken a passive approach that depended on government entities to manage the response. But the information Jakarta’s responders had on the flooding conditions was patchy at best. So in the last few years, the government began to turn to the local population for help. It helped.

Today, Jakarta’s municipal government is relying on the web-based PetaJakarta.org project and a handful of other crowdsourcing mobile apps such as Qlue and CROP to collect data and respond to floods and other disasters. Through these programmes, crowdsourced, time-sensitive data derived from citizens’ social-media inputs have made it possible for city agencies to more precisely map the locations of rising floods and help the residents at risk. In January 2015, for example, the web-based Peta Jakarta received 5,209 reports on floods via tweets with detailed text and photos. Anytime there’s a flood, Peta Jakarta’s data from the tweets are mapped and updated every minute, and often cross-checked by Jakarta Disaster Management Agency (BPBD) officials through calls with community leaders to assess the information and guide responders.

But in any city Twitter is only one piece of a very large puzzle. …

Even with such life-and-death examples, government agencies remain deeply protective of data because of issues of security, data ownership and citizen privacy. They are also concerned about liability issues if incorrect data lead to an activity that has unsuccessful outcomes. These concerns encumber the combination of crowdsourced data with operational systems of record, and impede the fast progress needed in disaster situations….Download the case study here.”