scholarly journals LA SCIENZA DEI DATI: UNA NUOVA SFIDA MULTIDISCIPLINARE

2019 ◽  
Author(s):  
Antonietta Mira
Keyword(s):  
Big Data ◽  
Data Science ◽  
Social Impact ◽  
Ethical Issues ◽  
Sensitive Data

The present manuscript aims to delineate and deepen the new multidisciplinary challenge of Data Science. In order to make the reader aware of the revolutionary scope of this new discipline, I will review various examples of success arising from the analysis of so called “Big Data”. My analysis will not fail to underline the economic and social impact of the Big Data revolution by also referring to the ethical issues that naturally arise in the presence of sensitive data and information.

scholarly journals Big Data, Method and the Ethics of Location: A Case Study of a Hookup App for Men Who Have Sex with Men

2018 ◽  
Vol 4 (2) ◽  
pp. 205630511876829
Author(s):  
Ben Light ◽  
Peta Mitchell ◽  
Patrik Wikström
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Ethical Issues ◽  
Sensitive Data ◽  
Public Sex ◽  
Sexual Cultures ◽  
Sex With Men ◽  
Data Context ◽  
Public Toilets

With the rise of geo-social media, location is emerging as a particularly sensitive data point for big data and digital media research. To explore this area, we reflect on our ethics for a study in which we analyze data generated via an app that facilitates public sex among men who have sex with men. The ethical sensitivities around location are further heightened in the context of research into such digital sexual cultures. Public sexual cultures involving men who have sex with men operate both in spaces “meant” for public sex (e.g., gay saunas and dark rooms) and spaces “not meant” for public sex (e.g., shopping centers and public toilets). The app in question facilitates this activity. We developed a web scraper that carefully collected selected data from the app and that data were then analyzed to help identify ethical issues. We used a mixture of content analysis using Python scripts, geovisualisation software and manual qualitative coding techniques. Our findings, which are methodological rather than theoretical in nature, center on the ethics associated with generating, processing, presenting, archiving and deleting big data in a context where harassment, imprisonment, physical harm and even death occur. We find a tension in normal standards of ethical conduct where humans are involved in research. We found that location came to the fore as a key—though not the only—actor requiring attention when considering ethics in a big data context.

Issues in security and privacy of big data

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.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

Produção internacional sobre ciência orientada a dados: análise dos termos data science e e-science na scopus e na web of science

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Leilah Santiago Bufrem ◽  
Fábio Mascarenhas Silva ◽  
Natanael Vitor Sobral ◽  
Anna Elizabeth Galvão Coutinho Correia
Keyword(s):  
Big Data ◽  
Open Access ◽  
Grid Computing ◽  
Digital Library ◽  
Data Science ◽  
Web Of Science ◽  
Computer Systems ◽  
Distributed Computer Systems

Introdução: A atual configuração da dinâmica relativa à produção e àcomunicação científicas revela o protagonismo da Ciência Orientada a Dados,em concepção abrangente, representada principalmente por termos como “e-Science” e “Data Science”. Objetivos: Apresentar a produção científica mundial relativa à Ciência Orientada a Dados a partir dos termos “e-Science” e “Data Science” na Scopus e na Web of Science, entre 2006 e 2016. Metodologia: A pesquisa está estruturada em cinco etapas: a) busca de informações nas bases Scopus e Web of Science; b) obtenção dos registros; bibliométricos; c) complementação das palavras-chave; d) correção e cruzamento dos dados; e) representação analítica dos dados. Resultados: Os termos de maior destaque na produção científica analisada foram Distributed computer systems (2006), Grid computing (2007 a 2013) e Big data (2014 a 2016). Na área de Biblioteconomia e Ciência de Informação, a ênfase é dada aos temas: Digital library e Open access, evidenciando a centralidade do campo nas discussões sobre dispositivos para dar acesso à informação científica em meio digital. Conclusões: Sob um olhar diacrônico, constata-se uma visível mudança de foco das temáticas voltadas às operações de compartilhamento de dados para a perspectiva analítica de busca de padrões em grandes volumes de dados.Palavras-chave: Data Science. E-Science. Ciência orientada a dados. Produção científica.Link:http://www.uel.br/revistas/uel/index.php/informacao/article/view/26543/20114

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals Record linkage of routine data with cohorts’ data of infants under European and Portuguese law

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Doetsch ◽  
I Lopes ◽  
R Redinha ◽  
H Barros
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Protection ◽  
Record Linkage ◽  
Data Science ◽  
Personal Data ◽  
Routine Data ◽  
Cohort Data ◽  
Education Data ◽  
Explicit Consent

Abstract The usage and exchange of “big data” is at the forefront of the data science agenda where Record Linkage plays a prominent role in biomedical research. In an era of ubiquitous data exchange and big data, Record Linkage is almost inevitable, but raises ethical and legal problems, namely personal data and privacy protection. Record Linkage refers to the general merging of data information to consolidate facts about an individual or an event that are not available in a separate record. This article provides an overview of ethical challenges and research opportunities in linking routine data on health and education with cohort data from very preterm (VPT) infants in Portugal. Portuguese, European and International law has been reviewed on data processing, protection and privacy. A three-stage analysis was carried out: i) interplay of threefold law-levelling for Record Linkage at different levels; ii) impact of data protection and privacy rights for data processing, iii) data linkage process' challenges and opportunities for research. A framework to discuss the process and its implications for data protection and privacy was created. The GDPR functions as utmost substantial legal basis for the protection of personal data in Record Linkage, and explicit written consent is considered the appropriate basis for the processing sensitive data. In Portugal, retrospective access to routine data is permitted if anonymised; for health data if it meets data processing requirements declared with an explicit consent; for education data if the data processing rules are complied. Routine health and education data can be linked to cohort data if rights of the data subject and requirements and duties of processors and controllers are respected. A strong ethical context through the application of the GDPR in all phases of research need to be established to achieve Record Linkage between cohort and routine collected records for health and education data of VPT infants in Portugal. Key messages GDPR is the most important legal framework for the protection of personal data, however, its uniform approach granting freedom to its Member states hampers Record Linkage processes among EU countries. The question remains whether the gap between data protection and privacy is adequately balanced at three legal levels to guarantee freedom for research and the improvement of health of data subjects.

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals Ethical issues in genomics research on neurodevelopmental disorders: a critical interpretive review

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
S. Mezinska ◽  
L. Gallagher ◽  
M. Verbrugge ◽  
E.M. Bunnik
Keyword(s):  
Informed Consent ◽  
Neurodevelopmental Disorders ◽  
Cognitive Abilities ◽  
Social Impact ◽  
Mental Health Problems ◽  
Ethical Issues ◽  
Family Members ◽  
Genetic Diagnosis ◽  
Research Participation ◽  
Genomic Research

Abstract Background Genomic research on neurodevelopmental disorders (NDDs), particularly involving minors, combines and amplifies existing research ethics issues for biomedical research. We performed a review of the literature on the ethical issues associated with genomic research involving children affected by NDDs as an aid to researchers to better anticipate and address ethical concerns. Results Qualitative thematic analysis of the included articles revealed themes in three main areas: research design and ethics review, inclusion of research participants, and communication of research results. Ethical issues known to be associated with genomic research in general, such as privacy risks and informed consent/assent, seem especially pressing for NDD participants because of their potentially decreased cognitive abilities, increased vulnerability, and stigma associated with mental health problems. Additionally, there are informational risks: learning genetic information about NDD may have psychological and social impact, not only for the research participant but also for family members. However, there are potential benefits associated with research participation, too: by enrolling in research, the participants may access genetic testing and thus increase their chances of receiving a (genetic) diagnosis for their neurodevelopmental symptoms, prognostic or predictive information about disease progression or the risk of concurrent future disorders. Based on the results of our review, we developed an ethics checklist for genomic research involving children affected by NDDs. Conclusions In setting up and designing genomic research efforts in NDD, researchers should partner with communities of persons with NDDs. Particular attention should be paid to preventing disproportional burdens of research participation of children with NDDs and their siblings, parents and other family members. Researchers should carefully tailor the information and informed consent procedures to avoid therapeutic and diagnostic misconception in NDD research. To better anticipate and address ethical issues in specific NDD studies, we suggest researchers to use the ethics checklist for genomic research involving children affected by NDDs presented in this paper.

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