scholarly journals Critical big data literacy tools—Engaging citizens and promoting empowered internet usage

Data & Policy ◽  
2020 ◽  
Vol 2 ◽  
Author(s):  
Ina Sander
Keyword(s):  
Big Data ◽  
Data Science ◽  
Personal Data ◽  
Internet Usage ◽  
Data Systems ◽  
Data Literacy ◽  
Internet Users ◽  
New Findings ◽  
And Behavior ◽  
Attitudes And Behavior

Abstract Datafied societies need informed public debate about the implications of data science technologies. At present, internet users are often unaware of the potential consequences of disclosing personal data online and few citizens have the knowledge to participate in such debates. This paper argues that critical big data literacy efforts are one way to address this lack of knowledge. It draws on findings from a small qualitative investigation and discusses the effectiveness of online critical big data literacy tools. Through pre and post use testing, the short- and longer-term influence of these tools on people’s privacy attitudes and behavior was investigated. The study’s findings suggested that the tools tested had a predominantly positive initial effect, leading to improved critical big data literacy among most participants, which resulted in more privacy-sensitive attitudes and internet usage. When analyzing the tools’ longer-term influence, results were more mixed, with evidence suggesting for some that literacy effects of the tools were short-lived, while for others they led to more persistent and growing literacy. The findings confirm previous research noting the complexity of privacy attitudes and also find that resignation toward privacy is multi-faceted. Overall, this study reaffirms the importance of critical big data literacy and produces new findings about the value of interactive data literacy tools. These tools have been under-researched to date. This research shows that these tools could provide a relevant means to work toward empowering internet users, promoting a critical internet usage and, ideally, enabling more citizens to engage in public debates about changing data systems.

scholarly journals LITERASI DATA DAN TANTANGAN INDUSTRIALISASI 4.0 BAGI MASYARAKAT PESISIR DI INDONESIA

2020 ◽  
Vol 3 (2) ◽  
pp. 188
Author(s):  
Dian Kristyanto
Keyword(s):  
Big Data ◽  
Personal Data ◽  
Coastal Communities ◽  
Internet Usage ◽  
Basic Knowledge ◽  
The Internet ◽  
Telephone Number ◽  
Data Literacy ◽  
Private Data ◽  
Internet Of Thing

Industrialization 4.0 brought many changes to the community, especially in terms of sustainable internet usage. Data literacy arises because of the large amount of data and information spread on the internet. This objective study to provide knowledge about data literacy in coastal society, another purpose is to explain the challenges that arise as a result of the birth industrialization 4.0 which certainly also felt by the coastal community. This methodology uses a literature review approach. Data collection techniques are carried out through studies of reference sources that are used as references. This study discussion is that data literacy in coastal society is devoted to basic knowledge in protecting personal data when conducting activities using the internet. Public-private data that needs to be kept secret include; identity number, account number, full name, telephone number, password, address, and others. While the challenges faced by coastal society in the industrialization 4.0 cover many aspects, three main challenges can be felt directly by coastal society such as big data, internet objects, and robotization.Keyword: coastal communities; data literacy; industrialization 4.0; information technologyABSTRAKIndustrialisasi 4.0 membawa banyak perubahan bagi masyarakat terutama dalam hal pemanfaatan internet secara terus-menerus. Literasi data muncul karena banyaknya data dan informasi yang tersebar di internet. Penelitian ini bertujuan untuk memberikan pengetahuan mengenai literasi data pada masyarakat pesisir, tujuan lain adalah untuk menjelaskan tantangan yang muncul akibat dari lahirnya era industrialisasi 4.0 yang pastinya dirasakan juga oleh masyarakat pesisir (nelayan). Metodologi menggunakan pendekatan riset pustaka. Proses pengumpulan data dilakukan dengan cara telaah terhadap sumber referensi yang digunakan sebagai rujukan. Kajian ini menghasilkan pembahasan bahwa literasi data pada masyarakat pesisir dikhususkan pada pengetahuan dasar dalam menjaga data pribadi pada saat melakukan aktifitas menggunakan internet. Data pribadi masyarakat yang perlu dijaga kerahasiaannya meliputi: nomor identitas, nomor rekening, nama lengkap, nomor telepon, password, alamat tinggal dan sebagainya. Sedangkan tantangan yang dihadapi masyarakat pesisir di era industrialisasi 4.0 meliputi banyak aspek, namun terdapat tiga tantangan utama yang dapat dirasakan secara langsung oleh masyarakat pesisir seperti big data, internet of thing dan robotization. 

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.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

scholarly journals The posthumous privacy paradox: Privacy preferences and behavior regarding digital remains

2020 ◽  
pp. 146144482097495
Author(s):  
Tal Morse ◽  
Michael Birnhack
Keyword(s):  
Representative Sample ◽  
Personal Data ◽  
Stated Preferences ◽  
Actual Behavior ◽  
Privacy Preferences ◽  
Internet Users ◽  
Privacy Paradox ◽  
And Behavior

Scholars have observed a gap between users’ stated preferences to protect their privacy and their actual behavior. This is the privacy paradox. This article queries the persistence of the privacy paradox after death. A survey of a representative sample of Israeli Internet users inquired of perceptions, preferences, and actions taken by users regarding their digital remains. The analysis yielded three distinct groups: (1) users interested in preserving privacy posthumously but do not act accordingly; for these users, the privacy paradox persists posthumously; (2) users who match their behavior to their preferences; for these users, the privacy paradox is resolved; and (3) users interested in sharing their personal data posthumously but do not make the appropriate provisions. This scenario is the inverted privacy paradox. This new category has yet to be addressed in the literature. We present some explanations for the persistence of the posthumous privacy paradox and for the inverted privacy paradox.

Big Data Analytics

2017 ◽  
Vol 11 (2) ◽  
pp. 41-56 ◽  
Author(s):  
Yingxu Wang ◽  
Jun Peng
Keyword(s):  
Big Data ◽  
Cognitive Processes ◽  
Data Analytics ◽  
Data Science ◽  
Computational Models ◽  
Big Data Analytics ◽  
Data Systems ◽  
Data Engineering ◽  
Big Data Systems ◽  
Cognitive Foundations

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

scholarly journals Security and Privacy Issues of Big Data

2019 ◽  
pp. 375-407
Author(s):  
José Moura ◽  
Carlos Serrão
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Personal Data ◽  
Security And Privacy ◽  
Data Systems ◽  
Computing Systems ◽  
Big Data Applications ◽  
Big Data Systems ◽  
Privacy Issues

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.

The Privacy Paradox in the Big Data Era? No Thanks, We Are the E-People

2019 ◽  
Vol 9 (3) ◽  
pp. 32-47 ◽  
Author(s):  
Marco Vassallo
Keyword(s):  
Big Data ◽  
Personal Data ◽  
Factor Analyses ◽  
Convenience Sample ◽  
Confirmatory Factor Analyses ◽  
Internet Users ◽  
Confirmatory Factor ◽  
Privacy Paradox ◽  
New Perspective ◽  
Definition Of

The objective of this work is to propose a new perspective in understanding the phenomenon of online behaviors, termed the privacy paradox, i.e., worry on preserving personal data and contents, but a little attention to disclose them, and thus introducing the new definition of e-people. The provocative hypothesis of this study regards the internet users who, in the Big Data era, are affected by a common covariation of being e-popular/e-visible, e-narcissist, e-(socially)-accepted, e-remembered. These e-behaviors will be conceptually gathered under the term of Achilles' paradigm. A structured web-questionnaire was submitted to a convenience sample of 198 internet users. First and second-order confirmatory factor analyses together with latent means models concretely supported the existence of the Achilles' paradigm and its impact on the privacy paradox concerns. As a result, the privacy paradox is not an effective paradox anymore: self-disclosing privacy online seems to be a well-accepted behavior.

Russian Internet Users' Privacy: A Study of Attitudes and Behavior

2014 ◽  
Author(s):  
Serhiy Hovyadinov
Keyword(s):  
Internet Users ◽  
And Behavior ◽  
Attitudes And Behavior

scholarly journals Reviews and syntheses: The promise of big soil data, moving current practices towards future potential

2021 ◽  
Author(s):  
Katherine E. O. Todd-Brown ◽  
Rose Z. Abramoff ◽  
Jeffrey Beem-Miller ◽  
Hava K. Blair ◽  
Stevan Earl ◽  
...  
Keyword(s):  
Big Data ◽  
Data Science ◽  
Study Data ◽  
Soil Survey ◽  
Data Reuse ◽  
Data Synthesis ◽  
New Findings ◽  
Future Potential ◽  
Large Soil ◽  
Current Practices

Abstract. In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources- remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

On the Cognitive and Theoretical Foundations of Big Data Science and Engineering

2017 ◽  
Vol 13 (02) ◽  
pp. 101-117 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Recursive Formula ◽  
Significant Finding ◽  
Data Systems ◽  
Basic Study ◽  
Wide Range ◽  
Big Data Systems ◽  
Cognitive Foundations ◽  
Theoretical Foundations

Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive [Formula: see text]-dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering.

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