Global

Assessing the Evidence: The Effectiveness and Impact of Public Governance-Oriented Multi-Stakeholder Initiatives

Curated on November 25, 2015August 3, 2018 by Stefaan Verhulst

Paper by Brandon Brockmyer and Jonathan A. Fox: “Transnational multi-stakeholder initiatives (MSIs) – voluntary partnerships between governments, civil society, and the private sector – are an increasingly prevalent strategy for promoting government responsiveness and accountability to citizens. While most transnational MSIs involve using voluntary standards to encourage socially and environmentally responsible private sector behavior, a handful of these initiatives – the Extractive Industries Transparency Initiative (EITI), the Construction Sector Transparency Initiative (CoST), the Open Government Partnership (OGP), the Global Initiative on Fiscal Transparency (GIFT) and the Open Contracting Partnership (OCP) – focus on information disclosure and participation in the public sector. Unlike private sector MSIs, which attempt to supplement weak government capacity to enforce basic social and environmental standards through partnerships between businesses and civil society, public sector MSIs ultimately seek to bolster public governance. But how exactly are these MSIs supposed to work? And how much has actually been achieved?

The purpose of this study is to identify and consolidate the current state of the evidence for public governance-oriented MSI effectiveness and impact. Researchers collected over 300 documents and interviewed more than two-dozen MSI stakeholders about their experiences with five public governance oriented multi-stakeholder initiatives.

This report provides a ‘snapshot’ of the evidence related to these five MSIs, and suggests that the process of leveraging transparency and participation through these initiatives for broader accountability gains remains uncertain. The report highlights the ongoing process of defining MSI success and impact, and how these initiatives intersect with other accountability actors and processes in complex ways. The study closes with key recommendations for MSI stakeholders….(More)”

Do We Need to Educate Open Data Users?

Curated on November 24, 2015August 15, 2018 by Stefaan Verhulst

Tony Hirst at IODC: “Whilst promoting the publication of open data is a key, indeed necessary, ingredient in driving the global open data agenda, promoting initiatives that support the use of open data is perhaps an even more pressing need….

This, then, is the first issue we need to address: improving basic levels of literacy in interpreting – and manipulating (for example, sorting and grouping) – simple tables and charts. Sensemaking, in other words: what does the chart you’ve just produced actually say? What story does it tell? And there’s an added benefit that arises from learning to read and critique charts better – it makes you better at creating your own.

Associated with reading stories from data comes the reason for telling the story and putting the data to work. How does “data” help you make a decision, or track the impact of a particular intervention? (Your original question should also have informed the data you searched for in the first place). Here we have a need to develop basic skills in how to actually use data, from finding anomalies to hold publishers to account, to using the data as part of a positive advocacy campaign.

After a quick read, on site, of some of the stories the data might have to tell, there may be a need to do further analysis, or more elaborate visualization work. At this point, a range of technical craft skills often come into play, as well as statistical knowledge.

Many openly published datasets just aren’t that good – they’re “dirty”, full of misspellings, missing data, things in the wrong place or wrong format, even if the data they do contain is true. A significant amount of time that should be spent analyzing the data gets spent trying to clean the data set and get it into a form where it can be worked with. I would argue here that a data technician, with a wealth of craft knowledge about how to repair what is essentially a broken dataset, can play an important timesaving role here getting data into a state where an analyst can actually start to do their job analyzing the data.

But at the same time, there are a range of tools and techniques that can help the everyday user improve the quality of their data. Many of these tools require an element of programming knowledge, but less than you might at first think. In the Open University/FutureLean MOOC “Learn to Code for Data Analysis” we use an interactive notebook style of computing to show how you can use code literally one line at a time to perform powerful data cleaning, analysis, and visualization operations on a range of open datasets, including data from the World Bank and Comtrade.

Here, then, is yet another area where skills development may be required: statistical literacy. At its heart, statistics simply provide us with a range of tools for comparing sets of numbers. But knowing what comparisons to make, or the basis on which particular comparisons can be made, knowing what can be said about those comparisons or how they might be interpreted, in short, understanding what story the stats appear to be telling, can quickly become bewildering. Just as we need to improve sensemaking skills associated with reading charts, so to we need to develop skills in making sense of statistics, even if not actually producing those statistics ourselves.

As more data gets published, there are more opportunities for more people to make use of that data. In many cases, what’s likely to hold back that final data use is a skills gap: primary among these are the skills required to interpret simple datasets and the statistics associated with them associated with developing knowledge about how to make decisions or track progress based on that interpretation. However, the path to producing the statistics or visualizations used by the end-users from the originally published open data dataset may also be a windy one, requiring skills not only in analyzing data and uncovering – and then telling – the stories it contains, but also in more mundane technical operational concerns such as actually accessing, and cleaning, dirty datasets….(More)”

Will Open Data Policies Contribute to Solving Development Challenges?

Curated on November 18, 2015July 19, 2019 by Stefaan Verhulst

Fabrizio Scrollini at IODC: “As the international open data charter gains momentum in the context of the wider development agenda related to the sustainable development goals set by the United Nations, a pertinent question to ask is: will open data policies contribute to solve development challenges? In this post I try to answer this question grounded in recent Latin American experience to contribute to a global debate.

Latin America has been exploring open data since 2013, when the first open data unconference (Abrelatam)and conference took place in Montevideo. In September 2015 in Santiago de Chile a vibrant community of activists, public servants, and entrepreneurs gathered in the third edition of Abrelatam and Condatos. It is now a more mature community. The days where it was sufficient to just open a few datasets and set up a portal are now gone. The focus of this meeting was on collaboration and use of data to address several social challenges.

Take for instance the health sector. Transparency in this sector is key to deliver better development goals. One of the panels at Condatos showed three different ways to use data to promote transparency and citizen empowerment in this sector. A tu servicio, a joint venture of DATA and the Uruguayan Ministry of Health helped to standardize and open public datasets that allowed around 30,000 users to improve the way they choose health providers. Government-civil society collaboration was crucial in this process in terms pooling resources and skills. The first prototype was only possible because some data was already open.

This contrasts with Cuidados Intensivos, a Peruvian endeavour aiming to provide key information about the health sector. Peruvian activists had to fill right to information requests, transform, and standardize data to eventually release it. Both experiences demanded a great deal of technical, policy, and communication craft. And both show the attitudes the public sector can take: either engaging or at the very best ignoring the potential of open data.

In the same sector look at a recent study dealing with Dengue and open data developed by our research initiative. If international organizations and countries were persuaded to adopt common standards for Dengue outbreaks, they could be potentially predicted if the right public data is available and standardized. Open data in this sector not only delivers accountability but also efficiency and foresight to allocate scarce resources.

Latin American countries – gathered in the open data group of the Red Gealc – acknowledge the increasing public value of open data. This group engaged constructively in Condatos with the principles enshrined in the charter and will foster the formalization of open data policies in the region. A data revolution won’t yield results if data is closed. When you open data you allow for several initiatives to emerge and show its value.

Once a certain level of maturity is reached in a particular sector, more than data is needed. Standards are crucial to ensure comparability and ease the collection, processing, and use of open government data. To foster and engage with open data users is also needed, as several strategies deployed by some Latin American cities show.

Coming back to our question: will open data policies contribute to solve development challenges? The Latin American experience shows evidence that it will….(More)”

Tackling quality concerns around (volunteered) big data

Curated on November 18, 2015August 3, 2018 by Stefaan Verhulst

University of Twente: “… Improvements in online information communication and mobile location-aware technologies have led to a dramatic increase in the amount of volunteered geographic information (VGI) in recent years. The collection of volunteered data on geographic phenomena has a rich history worldwide. For example, the Christmas Bird Count has studied the impacts of climate change on spatial distribution and population trends of selected bird species in North America since 1900. Nowadays, several citizen observatories collect information about our environment. This information is complementary or, in some cases, essential to tackle a wide range of geographic problems.

Despite the wide applicability and acceptability of VGI in science, many studies argue that the quality of the observations remains a concern. Data collected by volunteers does not often follow scientific principles of sampling design, and levels of expertise vary among volunteers. This makes it hard for scientists to integrate VGI in their research.

Low quality, inconsistent, observations can bias analysis and modelling results because they are not representative for the variable studied, or because they decrease the ratio of signal to noise. Hence, the identification of inconsistent observations clearly benefits VGI-based applications and provide more robust datasets to the scientific community.

In their paper the researchers describe a novel automated workflow to identify inconsistencies in VGI. “Leveraging a digital control mechanism means we can give value to the millions of observations collected by volunteers” and “it allows a new kind of science where citizens can directly contribute to the analysis of global challenges like climate change” say Hamed Mehdipoor and Dr. Raul Zurita-Milla, who work at the Geo-Information Processing department of ITC….

While some inconsistent observations may reflect real, unusual events, the researchers demonstrated that these observations also bias the trends (advancement rates), in this case of the date of lilac flowering onset. This shows that identifying inconsistent observations is a pre-requisite for studying and interpreting the impact of climate change on the timing of life cycle events….(More)”

How Big Data is Helping to Tackle Climate Change

Curated on November 18, 2015October 10, 2018 by Stefaan Verhulst

Bernard Marr at DataInformed: “Climate scientists have been gathering a great deal of data for a long time, but analytics technology’s catching up is comparatively recent. Now that cloud, distributed storage, and massive amounts of processing power are affordable for almost everyone, those data sets are being put to use. On top of that, the growing number of Internet of Things devices we are carrying around are adding to the amount of data we are collecting. And the rise of social media means more and more people are reporting environmental data and uploading photos and videos of their environment, which also can be analyzed for clues.

Perhaps one of the most ambitious projects that employ big data to study the environment is Microsoft’s Madingley, which is being developed with the intention of creating a simulation of all life on Earth. The project already provides a working simulation of the global carbon cycle, and it is hoped that, eventually, everything from deforestation to animal migration, pollution, and overfishing will be modeled in a real-time “virtual biosphere.” Just a few years ago, the idea of a simulation of the entire planet’s ecosphere would have seemed like ridiculous, pie-in-the-sky thinking. But today it’s something into which one of the world’s biggest companies is pouring serious money. Microsoft is doing this because it believes that analytical technology has finally caught up with the ability to collect and store data.

Another data giant that is developing tools to facilitate analysis of climate and ecological data is EMC. Working with scientists at Acadia National Park in Maine, the company has developed platforms to pull in crowd-sourced data from citizen science portals such as eBird and iNaturalist. This allows park administrators to monitor the impact of climate change on wildlife populations as well as to plan and implement conservation strategies.

Last year, the United Nations, under its Global Pulse data analytics initiative, launched the Big Data Climate Challenge, a competition aimed to promote innovate data-driven climate change projects. Among the first to receive recognition under the program is Global Forest Watch, which combines satellite imagery, crowd-sourced witness accounts, and public datasets to track deforestation around the world, which is believed to be a leading man-made cause of climate change. The project has been promoted as a way for ethical businesses to ensure that their supply chain is not complicit in deforestation.

Other initiatives are targeted at a more personal level, for example by analyzing transit routes that could be used for individual journeys, using Google Maps, and making recommendations based on carbon emissions for each route.

The idea of “smart cities” is central to the concept of the Internet of Things – the idea that everyday objects and tools are becoming increasingly connected, interactive, and intelligent, and capable of communicating with each other independently of humans. Many of the ideas put forward by smart-city pioneers are grounded in climate awareness, such as reducing carbon dioxide emissions and energy waste across urban areas. Smart metering allows utility companies to increase or restrict the flow of electricity, gas, or water to reduce waste and ensure adequate supply at peak periods. Public transport can be efficiently planned to avoid wasted journeys and provide a reliable service that will encourage citizens to leave their cars at home.

These examples raise an important point: It’s apparent that data – big or small – can tell us if, how, and why climate change is happening. But, of course, this is only really valuable to us if it also can tell us what we can do about it. Some projects, such as Weathersafe, which helps coffee growers adapt to changing weather patterns and soil conditions, are designed to help humans deal with climate change. Others are designed to tackle the problem at the root, by highlighting the factors that cause it in the first place and showing us how we can change our behavior to minimize damage….(More)”

Build digital democracy

Curated on November 12, 2015August 3, 2018 by Stefaan Verhulst

Dirk Helbing & Evangelos Pournaras in Nature: “Fridges, coffee machines, toothbrushes, phones and smart devices are all now equipped with communicating sensors. In ten years, 150 billion ‘things’ will connect with each other and with billions of people. The ‘Internet of Things’ will generate data volumes that double every 12 hours rather than every 12 months, as is the case now.

Blinded by information, we need ‘digital sunglasses’. Whoever builds the filters to monetize this information determines what we see — Google and Facebook, for example. Many choices that people consider their own are already determined by algorithms. Such remote control weakens responsible, self-determined decision-making and thus society too.

The European Court of Justice’s ruling on 6 October that countries and companies must comply with European data-protection laws when transferring data outside the European Union demonstrates that a new digital paradigm is overdue. To ensure that no government, company or person with sole control of digital filters can manipulate our decisions, we need information systems that are transparent, trustworthy and user-controlled. Each of us must be able to choose, modify and build our own tools for winnowing information.

With this in mind, our research team at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), alongside international partners, has started to create a distributed, privacy-preserving ‘digital nervous system’ called Nervousnet. Nervousnet uses the sensor networks that make up the Internet of Things, including those in smartphones, to measure the world around us and to build a collective ‘data commons’. The many challenges ahead will be best solved using an open, participatory platform, an approach that has proved successful for projects such as Wikipedia and the open-source operating system Linux.

A wise king?

The science of human decision-making is far from understood. Yet our habits, routines and social interactions are surprisingly predictable. Our behaviour is increasingly steered by personalized advertisements and search results, recommendation systems and emotion-tracking technologies. Thousands of pieces of metadata have been collected about every one of us (seego.nature.com/stoqsu). Companies and governments can increasingly manipulate our decisions, behaviour and feelings¹.

Many policymakers believe that personal data may be used to ‘nudge’ people to make healthier and environmentally friendly decisions. Yet the same technology may also promote nationalism, fuel hate against minorities or skew election outcomes² if ethical scrutiny, transparency and democratic control are lacking — as they are in most private companies and institutions that use ‘big data’. The combination of nudging with big data about everyone’s behaviour, feelings and interests (‘big nudging’, if you will) could eventually create close to totalitarian power.

Countries have long experimented with using data to run their societies. In the 1970s, Chilean President Salvador Allende created computer networks to optimize industrial productivity³. Today, Singapore considers itself a data-driven ‘social laboratory’⁴ and other countries seem keen to copy this model.

The Chinese government has begun rating the behaviour of its citizens⁵. Loans, jobs and travel visas will depend on an individual’s ‘citizen score’, their web history and political opinion. Meanwhile, Baidu — the Chinese equivalent of Google — is joining forces with the military for the ‘China brain project’, using ‘deep learning’ artificial-intelligence algorithms to predict the behaviour of people on the basis of their Internet activity⁶.

The intentions may be good: it is hoped that big data can improve governance by overcoming irrationality and partisan interests. But the situation also evokes the warning of the eighteenth-century philosopher Immanuel Kant, that the “sovereign acting … to make the people happy according to his notions … becomes a despot”. It is for this reason that the US Declaration of Independence emphasizes the pursuit of happiness of individuals.

Ruling like a ‘benevolent dictator’ or ‘wise king’ cannot work because there is no way to determine a single metric or goal that a leader should maximize. Should it be gross domestic product per capita or sustainability, power or peace, average life span or happiness, or something else?

Better is pluralism. It hedges risks, promotes innovation, collective intelligence and well-being. Approaching complex problems from varied perspectives also helps people to cope with rare and extreme events that are costly for society — such as natural disasters, blackouts or financial meltdowns.

Centralized, top-down control of data has various flaws. First, it will inevitably become corrupted or hacked by extremists or criminals. Second, owing to limitations in data-transmission rates and processing power, top-down solutions often fail to address local needs. Third, manipulating the search for information and intervening in individual choices undermines ‘collective intelligence’⁷. Fourth, personalized information creates ‘filter bubbles’⁸. People are exposed less to other opinions, which can increase polarization and conflict⁹.

Fifth, reducing pluralism is as bad as losing biodiversity, because our economies and societies are like ecosystems with millions of interdependencies. Historically, a reduction in diversity has often led to political instability, collapse or war. Finally, by altering the cultural cues that guide peoples’ decisions, everyday decision-making is disrupted, which undermines rather than bolsters social stability and order.

Big data should be used to solve the world’s problems, not for illegitimate manipulation. But the assumption that ‘more data equals more knowledge, power and success’ does not hold. Although we have never had so much information, we face ever more global threats, including climate change, unstable peace and socio-economic fragility, and political satisfaction is low worldwide. About 50% of today’s jobs will be lost in the next two decades as computers and robots take over tasks. But will we see the macroeconomic benefits that would justify such large-scale ‘creative destruction’? And how can we reinvent half of our economy?

The digital revolution will mainly benefit countries that achieve a ‘win–win–win’ situation for business, politics and citizens alike¹⁰. To mobilize the ideas, skills and resources of all, we must build information systems capable of bringing diverse knowledge and ideas together. Online deliberation platforms and reconfigurable networks of smart human minds and artificially intelligent systems can now be used to produce collective intelligence that can cope with the diverse and complex challenges surrounding us….(More)” See Nervousnet project

Peer review in 2015: A global view

Curated on November 10, 2015August 15, 2018 by Stefaan Verhulst

A white paper by Taylor & Francis: “Within the academic community, peer review is widely recognized as being at the heart of scholarly research. However, faith in peer review’s integrity is of ongoing and increasing concern to many. It is imperative that publishers (and academic editors) of peer-reviewed scholarly research learn from each other, working together to improve practices in areas such as ethical issues, training, and data transparency….Key findings:

Authors, editors and reviewers all agreed that the most important motivation to publish in peer reviewed journals is making a contribution to the field and sharing research with others.
Playing a part in the academic process and improving papers are the most important motivations for reviewers. Similarly, 90% of SAS study respondents said that playing a role in the academic community was a motivation to review.
Most researchers, across the humanities and social sciences (HSS) and science, technology and medicine (STM), rate the benefit of the peer review process towards improving their article as 8 or above out of 10. This was found to be the most important aspect of peer review in both the ideal and the real world, echoing the earlier large-scale peer review studies.
In an ideal world, there is agreement that peer review should detect plagiarism (with mean ratings of 7.1 for HSS and 7.5 for STM out of 10), but agreement that peer review is currently achieving this in the real world is only 5.7 HSS / 6.3 STM out of 10.
Researchers thought there was a low prevalence of gender bias but higher prevalence of regional and seniority bias – and suggest that double blind peer review is most capable of preventing reviewer discrimination where it is based on an author’s identity.
Most researchers wait between one and six months for an article they’ve written to undergo peer review, yet authors (not reviewers / editors) think up to two months is reasonable .
HSS authors say they are kept less well informed than STM authors about the progress of their article through peer review….(More)”