MegaPixels

Curated on Posted on by Stefaan Verhulst

About: “…MegaPixels is an art and research project first launched in 2017 for an installation at Tactical Technology Collective’s GlassRoom about face recognition datasets. In 2018 MegaPixels was extended to cover pedestrian analysis datasets for a commission by Elevate Arts festival in Austria. Since then MegaPixels has evolved into a large-scale interrogation of hundreds of publicly-available face and person analysis datasets, the first of which launched on this site in April 2019.

MegaPixels aims to provide a critical perspective on machine learning image datasets, one that might otherwise escape academia and industry funded artificial intelligence think tanks that are often supported by the several of the same technology companies who have created datasets presented on this site.

MegaPixels is an independent project, designed as a public resource for educators, students, journalists, and researchers. Each dataset presented on this site undergoes a thorough review of its images, intent, and funding sources. Though the goals are similar to publishing an academic paper, MegaPixels is a website-first research project, with an academic publication to follow.

One of the main focuses of the dataset investigations presented on this site is to uncover where funding originated. Because of our emphasis on other researcher’s funding sources, it is important that we are transparent about our own….(More)”.

Does Aid Effectiveness Differ per Political Ideologies?

Curated on Posted on by Stefaan Verhulst

Paper by Vincent Tawiah, Barnes Evans and Abdulrasheed Zakari: “Despite the extensive empirical literature on aid effectiveness, existing studies have not addressed directly how political ideology affects the use of foreign aid in the recipient country. This study, therefore, uses a unique dataset of 12 democratic countries in Africa to investigate the impact of political ideologies on aid effectiveness. Our results indicate that each political party uses aid differently in peruse of their political, ideological orientation. Further analyses suggest that rightist capitalist parties are likely to use aid to improve the private sector environment. Leftist socialist on the other hand, use aid effectively on pro-poor projects such as short-term poverty reduction, mass education and health services. Our additional analysis on the lines of colonialisation shows that the difference in the use of aid by political parties is much stronger in French colonies than Britain colonies. The study provides insight on how the recipient government are likely to use foreign aid….(More)”.

Principles and Policies for “Data Free Flow With Trust”

Curated on Posted on by Stefaan Verhulst

Paper by Nigel Cory, Robert D. Atkinson, and Daniel Castro: “Just as there was a set of institutions, agreements, and principles that emerged out of Bretton Woods in the aftermath of World War II to manage global economic issues, the countries that value the role of an open, competitive, and rules-based global digital economy need to come together to enact new global rules and norms to manage a key driver of today’s global economy: data. Japanese Prime Minister Abe’s new initiative for “data free flow with trust,” combined with Japan’s hosting of the G20 and leading role in e-commerce negotiations at the World Trade Organization (WTO), provides a valuable opportunity for many of the world’s leading digital economies (Australia, the United States, and European Union, among others) to rectify the gradual drift toward a fragmented and less-productive global digital economy. Prime Minister Abe is right in proclaiming, “We have yet to catch up with the new reality, in which data drives everything, where the D.F.F.T., the Data Free Flow with Trust, should top the agenda in our new economy,” and right in his call “to rebuild trust toward the system for international trade. That should be a system that is fair, transparent, and effective in protecting IP and also in such areas as e-commerce.”

The central premise of this effort should be a recognition that data and data-driven innovation are a force for good. Across society, data innovation—the use of data to create value—is creating more productive and innovative economies, transparent and responsive governments, better social outcomes (improved health care, safer and smarter cities, etc.).3But to maximize the innovative and productivity benefits of data, countries that support an open, rules-based global trading system need to agree on core principles and enact common rules. The benefits of a rules-based and competitive global digital economy are at risk as a diverse range of countries in various stages of political and economic development have policy regimes that undermine core processes, especially the flow of data and its associated legal responsibilities; the use of encryption to protect data and digital activities and technologies; and the blocking of data constituting illegal, pirated content….(More)”.

Citizen, Science, and Citizen Science

Curated on Posted on by Stefaan Verhulst

Introduction by Shun-Ling and Chen Fa-ti Fan to special issue on citizen science: “The term citizen science has become very popular among scholars as well as the general public, and, given its growing presence in East Asia, it is perhaps not a moment too soon to have a special issue of EASTS on the topic. However, the quick expansion of citizen science, as a notion and a practice, has also spawned a mass of blurred meanings. The term is ill-defined and has been used in diverse ways. To avoid confusion, it is necessary to categorize the various and often ambiguous usages of the term and clarify their meanings.

As in any taxonomy, there are as many typologies as the particular perspectives, parameters, and criteria adopted for classification. There have been helpful attempts at classifying different modes of citizen science (Cooper and Lewenstein 2016; Wiggins and Crowston 2012; Haklay 2012). However, they focused primarily on the different approaches or methods in citizen science. Ottinger’s two categories of citizen science—“scientific authority driven” and “social movement based”—foreground the criteria of action and justification, but they unnecessarily juxtapose science and society; in any case, they may be too general and leaving out too much at the same time.1

In contrast, our classification will emphasize the different conceptions of citizen and citizenship in how we think about citizen science. We believe that this move can help us contextualize the ideas and practices of citizen science in the diverse socio-political conditions found in East Asia and beyond (Leach, Scoones, and Wynne 2005). To explain that point, we’ll begin with a few observations. First, the current discourse on citizen science tends to glide over such concepts as state, citizen, and the public and to assume that the reader will understand what they mean. This confidence originates in part from the fact that the default political framework of the discourse is usually Western (particularly Anglo-American). As a result, one often easily accepts a commonsense notion of participatory liberal democracy as the reference framework. However, one cannot assume that that is the de facto political framework for discussion of citizen science….(More)”.

A Symphony, Not a Solo: How Collective Management Organisations Can Embrace Innovation and Drive Data Sharing in the Music Industry

Curated on Posted on by Stefaan Verhulst

Paper by David Osimo, Laia Pujol Priego, Turo Pekari and Ano Sirppiniemi: “…data is becoming a fundamental source of competitive advantage in music, just as in other sectors, and streaming services in particular are generating large volume of new data offering unique insight around customer taste and behavior. (As Financial Times recently put it, the music
industry is having its “moneyball” moment) But how are the different players getting ready for this change?

This policy brief aims to look at the question from the perspective of CMOs, the organisations charged with redistributing royalties from music users to music rightsholders (such as musical authors and publishers).

The paper is divided in three sections. Part I will look at the current positioning of CMOs in this new data-intensive ecosystem. Part II will discuss how greater data sharing and reuse can maximize innovation, comparing the music industries with other industries. Part III will make policy and business-model reform recommendations for CMOs to stimulate data-driven innovation, internally and in the industry as a whole….(More)”

Data Stewardship on the map: A study of tasks and roles in Dutch research institutes

Curated on Posted on by Stefaan Verhulst

Report by Verheul, Ingeborg et al: “Good research requires good data stewardship. Data stewardship encompasses all the different tasks and responsibilities that relate to caring for data during the various phases of the whole research life cycle. The basic assumption is that the researcher himself/herself is primarily responsible for all data.

However, the researcher does need professional support to achieve this. To that end, diverse supportive data stewardship roles and functions have evolved in recent years. Often they have developed over the course of time.

Their functional implementation depends largely on their place in the organization. This comes as no surprise when one considers that data stewardship consists of many facets that are traditionally assigned to different departments. Researchers regularly take on data stewardship tasks as well, not only for themselves but also in a wider context for a research group. This data stewardship work often remains unnoticed….(More)”.

Data to the rescue

Curated on Posted on by Stefaan Verhulst

Podcast by Kenneth Cukier: “Access to the right data can be as valuable in humanitarian crises as water or medical care, but it can also be dangerous. Misused or in the wrong hands, the same information can put already vulnerable people at further risk. Kenneth Cukier hosts this special edition of Babbage examining how humanitarian organisations use data and what they can learn from the profit-making tech industry. This episode was recorded live from Wilton Park, in collaboration with the United Nations OCHA Centre for Humanitarian Data…(More)”.

Facebook releases a trio of maps to aid with fighting disease outbreaks

Curated on Posted on by Stefaan Verhulst

Sarah Perez at Techcrunch: “Facebook… announced a new initiative focused on using its data and technologies to help nonprofit organizations and universities working in public health better map the spread of infectious diseases around the world. Specifically, the company is introducing three new maps: population density maps with demographic estimates, movement maps and network coverage maps. These, says Facebook, will help the health partners to understand where people live, how they’re moving and if they have connectivity — all factors that can aid in determining how to respond to outbreaks, and where supplies should be delivered.

As Facebook explained, health organizations rely on information like this when planning public health campaigns. But much of the information they rely on is outdated, like older census data. In addition, information from more remote communities can be scarce.

By combining the new maps with other public health data, Facebook believes organizations will be better equipped to address epidemics.

The new high-resolution population density maps will estimate the number of people living within 30-meter grid tiles, and provide insights on demographics, including the number of children under five, the number of women of reproductive age, as well as young and elderly populations. These maps aren’t built using Facebook data, but are instead built by using Facebook’s AI capabilities with satellite imagery and census information.

Movement maps, meanwhile, track aggregate data about Facebook users’ movements via their mobile phones (when location services are enabled). At scale, health partners can combine this with other data to predict where other outbreaks may occur next….(More)”.

Data Protection and Digital Agency for Refugees

Curated on Posted on by Stefaan Verhulst

Paper by Dragana Kaurin: “For the millions of refugees fleeing conflict and persecution every year, access to information about their rights and control over their personal data are crucial for their ability to assess risk and navigate the asylum process. While asylum seekers are required to provide significant amounts of personal information on their journey to safety, they are rarely fully informed of their data rights by UN agencies or local border control and law enforcement staff tasked with obtaining and processing their personal information. Despite recent improvements in data protection mechanisms in the European Union, refugees’ informed consent for the collection and use of their personal data is rarely sought. Using examples drawn from interviews with refugees who have arrived in Europe since 2013, and an analysis of the impacts of the 2016 EU-Turkey deal on migration, this paper analyzes how the vast amount of data collected from refugees is gathered, stored and shared today, and considers the additional risks this collection process poses to an already vulnerable population navigating a perilous information-decision gap….(More)”.

Humans and Big Data: New Hope? Harnessing the Power of Person-Centred Data Analytics

Curated on Posted on by Stefaan Verhulst

Paper by Carmel Martin, Keith Stockman and Joachim P. Sturmberg: “Big data provide the hope of major health innovation and improvement. However, there is a risk of precision medicine based on predictive biometrics and service metrics overwhelming anticipatory human centered sense-making, in the fuzzy emergence of personalized (big data) medicine. This is a pressing issue, given the paucity of individual sense-making data approaches. A human-centric model is described to address the gap in personal particulars and experiences in individual health journeys. The Patient Journey Record System (PaJR) was developed to improve human-centric healthcare by harnessing the power of person-centred data analytics using complexity theory, iterative health services and information systems applications over a 10 year period. PaJR is a web-based service supporting usually bi-weekly telephone calls by care guides to individuals at risk of readmissions.

This chapter describes a case study of the timing and context of readmissions using human (biopsychosocial) particular data which is based on individual experiences and perceptions with differing patterns of instability. This Australian study, called MonashWatch, is a service pilot using the PaJR system in the Dandenong Hospital urban catchment area of the Monash Health network. State public hospital big data – the Victorian HealthLinks Chronic Care algorithm provides case finding for high risk of readmission based on disease and service metrics. Monash Watch was actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing) at the time of the study.

Three randomly selected intervention cases describe a dynamic interplay of self-reported change in health and health care, medication, drug and alcohol use, social support structure. While the three cases were at similar predicted risk initially, their cases represented different statistically different time series configurations and admission patterns. Fluctuations in admission were associated with (mal)alignment of bodily health with psychosocial and environmental influences. However human interpretation was required to make sense of the patterns as presented by the multiple levels of data.

A human-centric model and framework for health journey monitoring illustrates the potential for ‘small’ personal experience data to inform clinical care in the era of big data predominantly based on biometrics and medical industrial process. ….(More)”.