scholarly journals The dynamics of big data and human rights: the case of scientific research

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160129 ◽  
Author(s):  
Effy Vayena ◽  
John Tasioulas
Keyword(s):  
Human Rights ◽  
Big Data ◽  
Data Science ◽  
Scientific Research ◽  
Dynamic Interaction ◽  
Digital Environment ◽  
Big Data Applications ◽  
Health Related Research ◽  
Health Related ◽  
Over Time

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities—the new capabilities and risks—of the rapidly evolving digital environment. This article is part of the themed issue ‘The ethical impact of data science’.

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Big Data, Data Science, and Career Pathways

2020 ◽  
pp. 239-254
Author(s):  
David W. Dorsey
Keyword(s):  
Big Data ◽  
Data Science ◽  
Career Pathways ◽  
Unstructured Data ◽  
Future Application ◽  
The Internet ◽  
The Future ◽  
Skill Requirements ◽  
Enormous Number ◽  
Over Time

With the rise of the internet and the related explosion in the amount of data that are available, the field of data science has expanded rapidly, and analytic techniques designed for use in “big data” contexts have become popular. These include techniques for analyzing both structured and unstructured data. This chapter explores the application of these techniques to the development and evaluation of career pathways. For example, data scientists can analyze online job listings and resumes to examine changes in skill requirements and careers over time and to examine job progressions across an enormous number of people. Similarly, analysts can evaluate whether information on career pathways accurately captures realistic job progressions. Within organizations, the increasing amount of data make it possible to pinpoint the specific skills, behaviors, and attributes that maximize performance in specific roles. The chapter concludes with ideas for the future application of big data to career pathways.

scholarly journals Openness in Big Data and Data Repositories

2019 ◽  
Vol 11 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Vicki Xafis ◽  
Markus K. Labude
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Open Science ◽  
Research Data ◽  
Data Repositories ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health Related Research ◽  
Health Related ◽  
Privacy And Confidentiality

Abstract There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

scholarly journals Visualização de dados: passado, presente e futuro | Data vizualization: past, present and future

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fabiano Couto Corrêa da Silva
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Science ◽  
Multivariate Data ◽  
Scientific Data ◽  
Cutting Edge ◽  
Cognitive Analysis ◽  
Analytical Thinking ◽  
Over Time

RESUMO São expostos os princípios fundamentais da ciência de dados e as generalidades de uma de suas áreas de estudo: a Visualização de dados. O artigo aborda como os dados multivariados tem sido representados por meio de imagens e gráficos ilustrados que relacionam os elementos de sintaxe e semântica que podem contemplar o pensamento analítico nas margens visuais. Analisa como a Visualização de Dados foi desenvolvida ao longo do tempo, utilizando exemplos reconhecidos como de vanguarda neste campo, validando a pesquisa com análise cognitivas básicas em princípios de apresentação de evidências nos displays de informação.Palavras-chave: Visualização de Dados; Infografias; Dados Científicos; Storytelling, Big Data.ABSTRACT The fundamental principles of data science and the generalities of one of its areas of study are exposed: Data Visualization. The article discusses how multivariate data has been represented through illustrated images and graphs that relate the elements of syntax and semantics that can include analytical thinking in visual margins. It analyzes how Data Visualization has been developed over time, using examples recognized as cutting edge in this field, validating research with basic cognitive analysis on principles of evidence presentation in information displays.Keywords: Data Visualization; Infographics; Scientific Data; Storytelling, Big Data.

scholarly journals Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yiming Li
Keyword(s):  
Big Data ◽  
Scientific Research ◽  
Research Management ◽  
Support Vector ◽  
Optimization Strategy ◽  
Web Database ◽  
Computing Platform ◽  
Research And Innovation ◽  
Software Configuration ◽  
Big Data Applications

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

scholarly journals Big Data Applications in Vaccinology

2021 ◽  
Vol 6 (2) ◽  
pp. 59-80
Author(s):  
Joseph E. Kasten
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Human Race ◽  
Big Data Applications ◽  
The World ◽  
Big Data Technologies ◽  
History Of ◽  
Structured Literature Review ◽  
Over Time

The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

scholarly journals Research Biobanks and Health Databases: The WMA Declaration of Taipei, Added Value to European Legislation (Soft and Hard Law)

2018 ◽  
Vol 25 (5) ◽  
pp. 501-516
Author(s):  
Gauthier Chassang ◽  
Emmanuelle Rial-Sebbag
Keyword(s):  
Scientific Research ◽  
Added Value ◽  
General Assembly ◽  
General Data Protection Regulation ◽  
European Guidelines ◽  
Health Related Research ◽  
Health Related ◽  
General Data ◽  
Research Biobanks ◽  
Essential Contribution

AbstractBiobanks and health databases make an essential contribution to health-related research (‘5P medicine’: predictive/preventive/personalised/participatory/provable). Since 1947, the World Medical Association (WMA) has addressed important issues in medical practice and scientific research, adopting guidelines that are recognised as global ethical standards. In October 2016, the WMA’s 67th General Assembly, held in Taipei, Taiwan, adopted a new Declaration on the Ethical Considerations regarding Health Databases and Biobanks, revising the Declaration adopted by the 53rd WMA General Assembly in 2002. Considering the way health databases and biobanks are currently used in research, the new recommendations are designed to facilitate the responsible collection and storage of human samples and/or associated data, and the provision of these bioresources for scientific research aimed at benefitting patients and populations. We analyse the Declaration of Taipei’s scope and content, highlighting its innovative features compared with other recent European guidelines and the General Data Protection Regulation (GDPR).

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
Vol 1 (5) ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

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