The ethics of big data as a public good: which public? Whose good?

International development and humanitarian organizations are increasingly calling for digital data to be treated as a public good because of its value in supplementing scarce national statistics and informing interventions, including in emergencies. In response to this claim, a ‘responsible data’ movement has evolved to discuss guidelines and frameworks that will establish ethical principles for data sharing. However, this movement is not gaining traction with those who hold the highest-value data, particularly mobile network operators who are proving reluctant to make data collected in low- and middle-income countries accessible through intermediaries. This paper evaluates how the argument for ‘data as a public good’ fits with the corporate reality of big data, exploring existing models for data sharing. I draw on the idea of corporate data as an ecosystem involving often conflicting rights, duties and claims, in comparison to the utilitarian claim that data's humanitarian value makes it imperative to share them. I assess the power dynamics implied by the idea of data as a public good, and how differing incentives lead actors to adopt particular ethical positions with regard to the use of data.

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Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis

Advances in Statistics ◽

10.1155/2014/502678 ◽

2014 ◽

Vol 2014 ◽

pp. 1-19 ◽

Cited By ~ 6

Author(s):

Mark J. van der Laan ◽

Richard J. C. M. Starmans

Keyword(s):

Big Data ◽

Data Science ◽

State Of The Art ◽

Accurate Estimation ◽

Learning Tools ◽

New Developments ◽

Data Movement ◽

Computational Data ◽

Adaptive Estimators ◽

Data Adaptive

This outlook paper reviews the research of van der Laan’s group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming at only relying on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of uncertainty in order to make sound statistical conclusions. We also provide a philosophical historical perspective on Targeted Learning, also relating it to the new developments in Big Data. We conclude with some remarks explaining the immediate relevance of Targeted Learning to the current Big Data movement.

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Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

10.31235/osf.io/zm6xy ◽

2018 ◽

Author(s):

Jen Schradie

Keyword(s):

Big Data ◽

Data Science ◽

Digital Data ◽

The Internet ◽

Sociological Research ◽

Marginalized Populations ◽

Online Data ◽

Persistent Problem ◽

Current State ◽

Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

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Perspectives on Policy and the Value of Nursing Science in a Big Data Era

Nursing Science Quarterly ◽

10.1177/0894318417741122 ◽

2017 ◽

Vol 31 (1) ◽

pp. 78-81 ◽

Cited By ~ 3

Author(s):

Sheila M. Gephart ◽

Mary Davis ◽

Kimberly Shea

Keyword(s):

Big Data ◽

Data Science ◽

Epistemic Values ◽

Data Movement ◽

Nursing Science ◽

Body Of Knowledge ◽

Data Volume ◽

Domain Expertise

As data volume explodes, nurse scientists grapple with ways to adapt to the big data movement without jeopardizing its epistemic values and theoretical focus that celebrate while acknowledging the authority and unity of its body of knowledge. In this article, the authors describe big data and emphasize ways that nursing science brings value to its study. Collective nursing voices that call for more nursing engagement in the big data era are answered with ways to adapt and integrate theoretical and domain expertise from nursing into data science.

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Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo

10.31228/osf.io/ps9de ◽

2017 ◽

Author(s):

Michael J Madison

Keyword(s):

Big Data ◽

Citizen Science ◽

Large Scale ◽

Data Science ◽

Digital Data ◽

Peer Production ◽

Data Intensive ◽

Knowledge Commons ◽

Large Numbers ◽

Citizen Science Project

The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, sometimes known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.

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Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

Applications of Security, Mobile, Analytic, and Cloud (SMAC) Technologies for Effective Information Processing and Management - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-4044-1.ch011 ◽

2018 ◽

pp. 212-232

Author(s):

Stephen Dass ◽

Prabhu J.

Keyword(s):

Big Data ◽

Data Analysis ◽

Large Volume ◽

Data Science ◽

Big Data Analytics ◽

Data Capture ◽

Digital Data ◽

Step Process ◽

Science Domain ◽

Tools And Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.

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Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

10.1101/2021.04.22.436044 ◽

2021 ◽

Author(s):

Michael C. Schatz ◽

Anthony A. Philippakis ◽

Enis Afgan ◽

Eric Banks ◽

Vincent J. Carey ◽

...

Keyword(s):

Data Sharing ◽

Data Storage ◽

Data Science ◽

Genomic Data ◽

Threat Detection ◽

Computing Environment ◽

Cloud Computing Environment ◽

Data Movement ◽

Genomic Data Analysis ◽

Genomic Data Science

AbstractThe traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

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Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Ethnicity & Disease ◽

10.18865/ed.27.2.95 ◽

2017 ◽

Vol 27 (2) ◽

pp. 95 ◽

Cited By ~ 44

Author(s):

Xinzhi Zhang ◽

Eliseo J. Pérez-Stable ◽

Philip E. Bourne ◽

Emmanuel Peprah ◽

O. Kenrik Duru ◽

...

Keyword(s):

Big Data ◽

Health Disparities ◽

Social Determinants ◽

Minority Health ◽

Data Privacy ◽

Data Science ◽

Health Care Disparities ◽

Small Sample ◽

Digital Data ◽

Digital Divides

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.Ethn.Dis;2017;27(2):95-106; doi:10.18865/ed.27.2.95.

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Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

10.4018/978-1-6684-3662-2.ch074 ◽

2022 ◽

pp. 1527-1548

Author(s):

Stephen Dass ◽

Prabhu J.

Keyword(s):

Big Data ◽

Data Analysis ◽

Large Volume ◽

Data Science ◽

Big Data Analytics ◽

Data Capture ◽

Digital Data ◽

Step Process ◽

Science Domain ◽

Tools And Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.

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Issues in security and privacy of big data

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i12.482 ◽

2018 ◽

Vol 7 (12) ◽

pp. 1

Author(s):

Shaveta Bhatia

Keyword(s):

Cloud Computing ◽

Big Data ◽

Approximate Method ◽

Biomedical Research ◽

Cyber Security ◽

Data Science ◽

Third Party ◽

Security And Privacy ◽

Security Threats ◽

The Third

The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity. It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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