scholarly journals Qualitative Research Ethics in the Big Data Era

2018 ◽  
Vol 63 (5) ◽  
pp. 560-583 ◽  
Author(s):  
Arielle Hesse ◽  
Leland Glenna ◽  
Clare Hinrichs ◽  
Robert Chiles ◽  
Carolyn Sachs
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Research Ethics ◽  
Data Storage ◽  
National Science Foundation ◽  
Open Data ◽  
The Social ◽  
Challenges And Opportunities ◽  
New Research ◽  
Broad Overview

This article examines the developments that have motivated this special issue on Qualitative Research Ethics in the Big Data Era. The article offers a broad overview of many pressing challenges and opportunities that the Big Data era raises particularly for qualitative research. Big Data has introduced to the social sciences new data sources, new research methods, new researchers, and new forms of data storage that have immediate and potential effects on the ethics and practice of qualitative research. Drawing from a literature review and insights gathered at a National Science Foundation-funded workshop in 2016, we present five principles for qualitative researchers and their institutions to consider in navigating these emerging research landscapes. These principles include (a) valuing methodological diversity; (b) encouraging research that accounts for and retains context, specificity, and marginalized and overlooked populations; (c) pushing beyond legal concerns to address often messy ethical dilemmas; (d) attending to regional and disciplinary differences; and (e) considering the entire lifecycle of research, including the data afterlife in archives or in open-data facilities.

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Challenges and Access to Production and Distribution of News in the Era of Big Data

2020 ◽  
Vol 3 (3) ◽  
Author(s):  
Sirui Zhu
Keyword(s):  
Big Data ◽  
Media Industry ◽  
Propagation Effect ◽  
Social Functions ◽  
News Production ◽  
The Social ◽  
Challenges And Opportunities ◽  
Production And Distribution ◽  
The Media ◽  
Media Integration

With the strategy of media integration, transformation and upgrading of media has become an important issue. In the era of big data, due to the dual impact of data and technology, the media brings both challenges and opportunities. The paper traces the characteristics of the era of big data, focuses on analyzing the challenges and opportunities in the media industry, and analyzes the transformation and upgrading of the media from the dimensions of news production and distribution to better realize the social functions of media in the era of big data. Some strategic suggestions are put forward to improve the propagation effect.

scholarly journals Big data y open data en la administración turística: acceso y reutilización de información

2017 ◽  
pp. 19-41
Author(s):  
Felio José BAUZÁ MARTORELL
Keyword(s):  
Big Data ◽  
Technological Innovation ◽  
Data Protection ◽  
Open Data ◽  
Social Reality ◽  
New Paradigm ◽  
Legal Regime ◽  
Legal Relationship ◽  
The Social ◽  
Datos Abiertos

LABURPENA: Turismoaren arloan, harreman juridikoaren paradigma berri baten aurrean gaude, zeina berrikuntza teknologikoan eta datuak irekitzean oinarritzen den. Makro-datuak eta datu irekiak izateko aukerak datu-kategoria berri bat ekarri dute, ohiko datuekin alderatuta ezberdina. Legegileak ez zuen datu horietan pentsatu datuak babesteko arloan indarrean dagoen araubide juridikoa egiteko orduan. Indarrean dauden tresna arautzaileak zaharkituta geratu dira, eta, zalantzarik gabe, erregulatu behar duten gizarte-errealitatetik oso urrun. Artikulu honetan, indarrean dagoen araudia aztertuko da. Aldi berean, ordea, araudi hori aldatzeko modua iradokiko da, errealitate berrira egokitu dadin. RESUMEN: Asistimos a un nuevo paradigma de la relación jurídica en materia de turismo basado en la innovación tecnológica y la apertura de datos. Las posibilidades de los macro datos y los datos abiertos dan lugar a una nueva categoría de datos muy distintos a los convencionales y para los que no pensaba el legislador a la hora de elaborar el régimen jurídico vigente de protección de datos. Los instrumentos normativos vigentes han quedado obsoletos y desde luego muy alejados de la realidad social que deben regular. En este artículo se aborda un análisis de la normativa vigente, al tiempo que se sugiere su reforma y adaptación a la nueva realidad. ABSTRACT: A new paradigm for the legal relationship regarding tourism based in technological innovation and opening of data is noticed. The possibilities of macro data and open data give rise to a new data category which are very different from conventional data and that were not kept in mind by the legislator when elaborating the current legal regime for data protection. The normative tools in force have become obsolete and they are certainly rather far away from the social reality they are supposed to regulate.

scholarly journals Towards Big Data Analytics in the e-Learning Space

2019 ◽  
Vol 19 (3) ◽  
pp. 16-24 ◽  
Author(s):  
Ivan P. Popchev ◽  
Daniela A. Orozova
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Students At Risk ◽  
Learning Space ◽  
E Learning ◽  
New Research ◽  
Big Data Storage

Abstract The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

Qualitative Research Ethics in the Big Data Era: Part II

2019 ◽  
Vol 63 (6) ◽  
pp. 667-668
Author(s):  
Leland Glenna ◽  
Arielle Hesse ◽  
Clare Hinrichs ◽  
Robert Chiles ◽  
Carolyn Sachs
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Research Ethics ◽  
Special Issue

This serves as a brief introduction to Part II of the articles presented in the special issue, Qualitative Research Ethics in the Big-Data Era.

scholarly journals From Big Data to Knowledge in the Social Sciences

2015 ◽  
Vol 659 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Bradford W. Hesse ◽  
Richard P. Moser ◽  
William T. Riley
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Open Data ◽  
High Volume ◽  
National Institutes Of Health ◽  
Scientific Enterprise ◽  
Data Intensive ◽  
Scientific Disciplines ◽  
The Social ◽  
Systemic Problems

One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.

scholarly journals The Big Data toolkit for Psychologists: Data Sources and Methodologies

2021 ◽  
Author(s):  
Heinrich Peters ◽  
Zachariah Marrero ◽  
Samuel D. Gosling
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Computation Time ◽  
Psychological Research ◽  
Data Sources ◽  
Machine Learning Techniques ◽  
Challenges And Opportunities ◽  
Huge Impact ◽  
Analytic Strategy ◽  
The Right

As human interactions have shifted to virtual spaces and as sensing systems have become more affordable, an increasing share of peoples’ everyday lives can be captured in real time. The availability of such fine-grained behavioral data from billions of people has the potential to enable great leaps in our understanding of human behavior. However, such data also pose challenges to engineers and behavioral scientists alike, requiring a specialized set of tools and methodologies to generate psychologically relevant insights.In particular, researchers may need to utilize machine learning techniques to extract information from unstructured or semi-structured data, reduce high-dimensional data to a smaller number of variables, and efficiently deal with extremely large sample sizes. Such procedures can be computationally expensive, requiring researchers to balance computation time with processing power and memory capacity. Whereas modelling procedures on small datasets will usually take mere moments to execute, applying modeling procedures to big data can take much longer with typical execution times spanning hours, days, or even weeks depending on the complexity of the problem and the resources available. Seemingly subtle decisions regarding preprocessing and analytic strategy can end up having a huge impact on the viability of executing analyses within a reasonable timeframe. Consequently, researchers must anticipate potential pitfalls regarding the interplay of their analytic strategy with memory and computational constraints.Many researchers who are interested in using “big data” report having problems learning about new analytic methods or software, finding collaborators with the right skills and knowledge, and getting access to commercial or proprietary data for their research (Metzler et al. 2016). This chapter aims to serve as a practical introduction for psychologists who want to use large datasets and datasets from non-traditional data sources in their research (i.e., data not generated in the lab or through conventional surveys). First, we discuss the concept of big data and review some of the theoretical challenges and opportunities that arise with the availability of ever larger amounts of data. Second, we discuss practical implications and best practices with respect to data collection, data storage, data processing, and data modelling for psychological research in the age of big data.

scholarly journals Onlinization Tafsir: Studi Alquran di Era Disrupsi

TAJDID ◽  
2021 ◽  
Vol 28 (1) ◽  
pp. 73
Author(s):  
Helmi Maulana
Keyword(s):  
Qualitative Research ◽  
Information Technology ◽  
Big Data ◽  
Research Ethics ◽  
Primary Data ◽  
The Internet ◽  
Research Model ◽  
Policy Makers ◽  
Communication And Information Technology

The emergence and popularity of the internet also gave birth to the phenomenon of online interpretation of interpretation. The era of disruption forces every individual to change and leave old patterns to new patterns that are considered in accordance with the development of communication and information technology. Departing from this reality, this article examines how the presence of online interpretation has the opportunity to develop patterns of interpretation study and research as well as its challenges. This article uses a qualitative research model by analyzing primary data about the concept of online interpretation, its opportunities and hands in the study of interpretation, and its implications. The result is that the interpretation website as a form of online interpretation is big data that can be used as a research source. Multidisciplinary opportunities, easy and cheap, as well as the popularity of interpretation. The challenges are around data understanding, research ethics and methodology. It is necessary to follow up with policy makers so that the prospect of an interpretation study finds its footing in the era of disruption.

scholarly journals Relevance of Big Data Technologies to and users in Context of Social Networking Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 4182-4186
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Storage ◽  
Vital Role ◽  
Research Article ◽  
The Social ◽  
Big Data Technologies ◽  
Social Media Platforms ◽  
Big Data Storage

Unremitting generation of data by various data analytics platforms, ubiquitous ,edge nodes and social networks in the concurrent scenario has shaped the exceptional amount of data in volume, velocity, veracity, variety and value. Exceptional data have made traditional information technology and method unfeasible to cope up amid. This exceptional data has been termed as Big Data. Social media is one of the most important sources of Big Data. social media is a constituent of Big Data. Besides Big Data plays a vital role in moving forward the Social Networking Applications to innovate and enhance the experience of users. Various technologies are factored for Big Data storage, processing and analysis in the context of social networking. This paper investigates these technologies which are being used by social networking applications with their relevance to the end users. The research article provides a relevance computation of various social media platforms. It further summarizes a visualization of the use of the platforms in their contribution to the big data.

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