A New Portal for the Decentralized Web and its Guiding Principles

Curated on Posted on by Stefaan Verhulst

Internet Archive: “For a long time, we’ve felt that the growing, diverse, global community interested in building the decentralized Web needed an entry point. A portal into the events, concepts, voices, and resources critical to moving the Decentralized Web forward.

This is why we created, getdweb.net, to serve as a portal, a welcoming entry point for people to learn and share strategies, analysis, and tools around how to build a decentralized Web.

Screenshot of https://getdweb.net/

It began at DWeb Camp 2019, when designer Iryna Nezhynska of Jolocom led a workshop to imagine what form that portal should take. Over the next 18 months, Iryna steered a dedicated group of DWeb volunteers through a process to create this new website. If you are new to the DWeb, it should help you learn about its core concepts. If you are a seasoned coder, it should point you to opportunities nearby. For our nine local nodes, it should be a clearinghouse and archive for past and future events.

Above all, the new website was designed to clearly state the principles we believe in as a community, the values we are trying to build right into the code.

At our February DWeb Meetup, our designer Iryna took us on a tour of the new website and the design concepts that support it.

Then John Ryan and I (Associate Producer of DWeb Projects) shared the first public version of the Principles of the DWeb and described the behind-the-scenes process that went into developing them. It was developed in consultation with dozens of community members, including technologists, organizers, academics, policy experts, and artists. These DWeb Principles are a starting point, not an end point — open for iteration.

As stewards, we felt that we needed to crystallize the shared vision of this community, to demonstrate how and why we are building a Decentralized Web. Our aim is to identify our guiding principles through discussion and distill them into a living document that we can point to. It is to create a set of practical guiding values as we design and build the Web of the future….(More)”.

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Curated on Posted on by Stefaan Verhulst

Paper by Nicole E. Kogan et al: “We propose that several digital data sources may provide earlier indication of epidemic spread than traditional COVID-19 metrics such as confirmed cases or deaths. Six such sources are examined here: (i) Google Trends patterns for a suite of COVID-19–related terms; (ii) COVID-19–related Twitter activity; (iii) COVID-19–related clinician searches from UpToDate; (iv) predictions by the global epidemic and mobility model (GLEAM), a state-of-the-art metapopulation mechanistic model; (v) anonymized and aggregated human mobility data from smartphones; and (vi) Kinsa smart thermometer measurements.

We first evaluate each of these “proxies” of COVID-19 activity for their lead or lag relative to traditional measures of COVID-19 activity: confirmed cases, deaths attributed, and ILI. We then propose the use of a metric combining these data sources into a multiproxy estimate of the probability of an impending COVID-19 outbreak. Last, we develop probabilistic estimates of when such a COVID-19 outbreak will occur on the basis of multiproxy variability. These outbreak-timing predictions are made for two separate time periods: the first, a “training” period, from 1 March to 31 May 2020, and the second, a “validation” period, from 1 June to 30 September 2020. Consistent predictive behavior among proxies in both of these subsequent and nonoverlapping time periods would increase the confidence that they may capture future changes in the trajectory of COVID-19 activity….(More)”.

How can we measure productivity in the public sector?

Curated on Posted on by Stefaan Verhulst

Ravi Somani at the World Bank: “In most economies, the public sector is a major purchaser of goods, services and labor. According to the Worldwide Bureaucracy Indicators, globally the public sector accounts for around 25% of GDP and 38% of formal employment. Generating efficiency gains in the public sector can, therefore, have important implications for a country’s overall economic performance.  

Public-sector productivity measures the rate with which inputs are converted into desirable outputs in the public sector. Measures can be developed at the level of the employee, organization, or overall public sector, and can be tracked over time. Such information allows policymakers to identify good and bad performers, understand what might be correlated with good performance, and measure the returns to different types of public expenditures. This knowledge can be used to improve the allocation of public resources in the future and maximize the impact of the public purse.

But how can we measure it?

However, measuring productivity in the public sector can be tricky because:

  • There are often no market transactions for public services, or they are distorted by subsidies and other market imperfections.
  • Many public services are complex, requiring (often immeasurable) inputs from multiple individuals and organizations.
  • There is often a substantial time lag between investments in inputs and the realization of outputs and outcomes.

This recent World Bank publication provides a summary of the different approaches to measuring productivity in the public sector, presented in the table below.  For simplicity, the approaches are separated into: ‘macro’ approaches, which provide aggregate information at the level of an organization, sector, or service as a whole; and ‘micro’ approaches, which can be applied to the individual employee, task, project, and process.   
 

Macro and Micro Approaches to measure public-sector productivity

There is no silver bullet for accurately measuring public-sector productivity – each approach has its own limitations.  For example, the cost-weighted-output approach requires activity-level data, necessitates different approaches for different sectors, and results in metrics with difficult-to-interpret absolute levels.  Project-completion rates require access to project-level data and may not fully account for differences in the quality and complexity of projects. The publication includes a list of the pros, cons, and implementation requirements for each approach….(More)”.

The Mathematics of How Connections Become Global

Curated on Posted on by Stefaan Verhulst

Kelsey Houston-Edwards at Scientific American: “When you hit “send” on a text message, it is easy to imagine that the note will travel directly from your phone to your friend’s. In fact, it typically goes on a long journey through a cellular network or the Internet, both of which rely on centralized infrastructure that can be damaged by natural disasters or shut down by repressive governments. For fear of state surveillance or interference, tech-savvy protesters in Hong Kong avoided the Internet by using software such as FireChat and Bridgefy to send messages directly between nearby phones.

These apps let a missive hop silently from one phone to the next, eventually connecting the sender to the receiver—the only users capable of viewing the message. The collections of linked phones, known as mesh networks or mobile ad hoc networks, enable a flexible and decentralized mode of communication. But for any two phones to communicate, they need to be linked via a chain of other phones. How many people scattered throughout Hong Kong need to be connected via the same mesh network before we can be confident that crosstown communication is possible?

Mesh network in action: when cell-phone ranges overlap, a linked chain of connections is established.
Credit: Jen Christiansen (graphic); Wee People font, ProPublica and Alberto Cairo (figure drawings)

A branch of mathematics called percolation theory offers a surprising answer: just a few people can make all the difference. As users join a new network, isolated pockets of connected phones slowly emerge. But full east-to-west or north-to-south communication appears all of a sudden as the density of users passes a critical and sharp threshold. Scientists describe such a rapid change in a network’s connectivity as a phase transition—the same concept used to explain abrupt changes in the state of a material such as the melting of ice or the boiling of water.

A phase transition in a mesh network: the density of users suddenly passes a critical threshold.
Credit: Jen Christiansen (graphic); Wee People font, ProPublica and Alberto Cairo (figure drawings)

Percolation theory examines the consequences of randomly creating or removing links in such networks, which mathematicians conceive of as a collection of nodes (represented by points) linked by “edges” (lines). Each node represents an object such as a phone or a person, and the edges represent a specific relation between two of them. The fundamental insight of percolation theory, which dates back to the 1950s, is that as the number of links in a network gradually increases, a global cluster of connected nodes will suddenly emerge….(More)”.

What the drive for open science data can learn from the evolving history of open government data

Curated on Posted on by Stefaan Verhulst

Stefaan Verhulst, Andrew Young, and Andrew Zahuranec at The Conversation: “Nineteen years ago, a group of international researchers met in Budapest to discuss a persistent problem. While experts published an enormous amount of scientific and scholarly material, few of these works were accessible. New research remained locked behind paywalls run by academic journals. The result was researchers struggled to learn from one another. They could not build on one another’s findings to achieve new insights. In response to these problems, the group developed the Budapest Open Access Initiative, a declaration calling for free and unrestricted access to scholarly journal literature in all academic fields.

In the years since, open access has become a priority for a growing number of universities, governments, and journals. But while access to scientific literature has increased, access to the scientific data underlying this research remains extremely limited. Researchers can increasingly see what their colleagues are doing but, in an era defined by the replication crisis, they cannot access the data to reproduce the findings or analyze it to produce new findings. In some cases there are good reasons to keep access to the data limited – such as confidentiality or sensitivity concerns – yet in many other cases data hoarding still reigns.

To make scientific research data open to citizens and scientists alike, open science data advocates can learn from open data efforts in other domains. By looking at the evolving history of the open government data movement, scientists can see both limitations to current approaches and identify ways to move forward from them….(More) (French version)”.

Wikipedia Is Finally Asking Big Tech to Pay Up

Curated on Posted on by Stefaan Verhulst

Noam Cohen at Wired: “From the start, Google and Wikipedia have been in a kind of unspoken partnership: Wikipedia produces the information Google serves up in response to user queries, and Google builds up Wikipedia’s reputation as a source of trustworthy information. Of course, there have been bumps, including Google’s bold attempt to replace Wikipedia with its own version of user-generated articles, under the clumsy name “Knol,” short for knowledge. Knol never did catch on, despite Google’s offer to pay the principal author of an article a share of advertising money. But after that failure, Google embraced Wikipedia even tighter—not only linking to its articles but reprinting key excerpts on its search result pages to quickly deliver Wikipedia’s knowledge to those seeking answers.

The two have grown in tandem over the past 20 years, each becoming its own household word. But whereas one mushroomed into a trillion-dollar company, the other has remained a midsize nonprofit, depending on the generosity of individual users, grant-giving foundations, and the Silicon Valley giants themselves to stay afloat. Now Wikipedia is seeking to rebalance its relationships with Google and other big tech firms like Amazon, Facebook, and Apple, whose platforms and virtual assistants lean on Wikipedia as a cost-free virtual crib sheet.

Today, the Wikimedia Foundation, which operates the Wikipedia project in more than 300 languages as well as other wiki-projects, is announcing the launch of a commercial product, Wikimedia Enterprise. The new service is designed for the sale and efficient delivery of Wikipedia’s content directly to these online behemoths (and eventually, to smaller companies too)….(More)”.

The Handbook: How to regulate?

Curated on Posted on by Stefaan Verhulst

Handbook edited by the Regulatory Institute: “…presents an inventory of regulatory techniques from over 40 jurisdictions and a basic universal method. The Handbook is based on the idea that officials with an inventory of regulatory techniques have more choices and can develop better regulations. The same goes for officials using methodological knowledge. The Handbook is made available free of charge because better regulations benefit us all….

The purpose of the Handbook is to assist officials involved in regulatory activities. Readers can draw inspiration from it, can learn how colleagues have tackled a certain regulatory challenge and can even develop a tailor-made systematic approach to improve their regulation. The Handbook can also be used as a basis for training courses or for self-training.

The Handbook is not intended to be read from A to Z. Instead, readers are invited to pick and choose the sections that are relevant to them. The Handbook was not developed to be the authoritative source of how to regulate, but to offer in the most neutral and objective way possibilities for improving regulation…

The Handbook explores the empty space between:

  • the constitution or similar documents setting the legal frame,
  • the sector-specific policies followed by the government, administration, or institution,
  • the impact assessment, better regulation, simplification, and other regulatory policies,
  • applicable drafting instructions or recommendations, and
  • the procedural settings of the respective jurisdiction….(More)”.

Thinking systems

Curated on Posted on by Stefaan Verhulst

Paper by Geoff Mulgan: “…describes methods for understanding how vital everyday systems work, and how they could work better, through improved shared cognition – observation, memory, creativity and judgement – organised as commons.

Much of our life we depend on systems: interconnected webs of activity that link many organisations, technologies and people. These bring us food and clothing; energy for warmth and light; mobility including rail, cars and global air travel; care, welfare and handling of waste. Arguably the biggest difference between the modern world and the world of a few centuries ago is the thickness and complexity of these systems. These have brought huge gains.

But one of their downsides is that they have made the world around us harder to understand or shape. A good example is the Internet: essential to much of daily life but largely obscure and opaque to its users. Its physical infrastructures, management, protocols and flows are almost unknown except to specialists, as are its governance structures and processes (if you are in any doubt, just ask a random sample of otherwise well-informed people). Other vital systems like those for food, energy or care are also hardly visible to those within them as well as those dependent on them. This makes it much harder to hold them to account, or to ensure they take account of more voices and needs. We often feel that the world is much more accessible thanks to powerful search engines and ubiquitous data. But try to get a picture of the systems around you and you quickly discover just how much is opaque and obscure.

If you think seriously about these systems it’s also hard not to be struck by another feature. Our systems generally use much more data and knowledge than their equivalents in the past. But this progress also highlights what’s missing in the data they use (often including the most important wants and needs). Moreover, huge amounts of potentially relevant data is lost immediately or never captured and how much that is captured is then neither organised nor shared. The result is a strangely lop-sided world: vast quantities of data are gathered and organised at great expense for some purposes (notably defense or click-through advertising)

So how could we recapture our systems and help them make the most of intelligence of all kinds? The paper shares methods and approaches that could make our everyday systems richer in intelligence and also easier to guide. It advocates:

· A cognitive approach to systems – focusing on how they think, and specifically how they observe, analyse, create and remember. It argues that this approach can help to bridge the often abstract language of systems thinking and practical action

· It advocates that much of this systems intelligence needs to be organised as a commons – which is very rarely the case now

· And it advocates new structures and roles within government and other organisations, and the growth of a practice of systems architects with skills straddling engineering, management, data and social science – who are adept at understanding, designing and improving intelligent systems that are transparent and self-aware.

The background to the paper is the great paradox of systems right now: there is a vast literature, a small industry of consultancies and labs, and no shortage of rhetorical commitment in many fields. Yet these have had at best uneven impact on how decisions are made or large organisations are run….(More)”.

The Ethics and Laws of Medical Big Data

Curated on Posted on by Stefaan Verhulst

Chapter by Hrefna Gunnarsdottir et al: “The COVID-19 pandemic has highlighted that leveraging medical big data can help to better predict and control outbreaks from the outset. However, there are still challenges to overcome in the 21st century to efficiently use medical big data, promote innovation and public health activities and, at the same time, adequately protect individuals’ privacy. The metaphor that property is a “bundle of sticks”, each representing a different right, applies equally to medical big data. Understanding medical big data in this way raises a number of questions, including: Who has the right to make money off its buying and selling, or is it inalienable? When does medical big data become sufficiently stripped of identifiers that the rights of an individual concerning the data disappear? How have different regimes such as the General Data Protection Regulation in Europe and the Health Insurance Portability and Accountability Act in the US answered these questions differently? In this chapter, we will discuss three topics: (1) privacy and data sharing, (2) informed consent, and (3) ownership. We will identify and examine ethical and legal challenges and make suggestions on how to address them. In our discussion of each of the topics, we will also give examples related to the use of medical big data during the COVID-19 pandemic, though the issues we raise extend far beyond it….(More)”.

The Third Wave of Open Data Toolkit

Curated on Posted on by Stefaan Verhulst

The GovLab: “Today, as part of Open Data Week 2021, the Open Data Policy Lab is launching  The Third Wave of Open Data Toolkit, which provides organizations with specific operational guidance on how to foster responsible, effective, and purpose-driven re-use. The toolkit—authored by Andrew Young, Andrew J. Zahuranec, Stefaan G. Verhulst, and Kateryna Gazaryan—supports the work of data stewards, responsible data leaders at public, private, and civil society organizations empowered to seek new ways to create public value through cross-sector data collaboration. The toolkit provides this support a few different ways. 

First, it offers a framework to make sense of the present and future open data ecosystem. Acknowledging that data re-use is the result of many stages, the toolkit separates each stage, identifying the ways the data lifecycle plays into data collaboration, the way data collaboration plays into the production of insights, the way insights play into conditions that enable further collaboration, and so on. By understanding the processes that data is created and used, data stewards can promote better and more impactful data management. 

Third Wave Framework

Second, the toolkit offers eight primers showing how data stewards can operationalize the actions previously identified as being part of the third wave. Each primer includes a brief explanation of what each action entails, offers some specific ways data stewards can implement these actions, and lists some supplementary pieces that might be useful in this work. The primers, which are available as part of the toolkit and as standalone two-pagers, are…(More)”.