scholarly journals New Methods in the History of Medicine: Streamlining Workflows to Enable Big-Data History Projects

2017 ◽  
Vol 61 (3) ◽  
pp. 477-480
Author(s):  
S. Wright Kennedy ◽  
Jessica C. Kuzmin ◽  
Benjamin Jones
2016 ◽  
Vol 61 (1) ◽  
pp. 176-176
Author(s):  
Frederick W. Gibbs ◽  
Jeffrey S. Reznick

2016 ◽  
Vol 63 (2) ◽  
pp. 67-88 ◽  
Author(s):  
Sandra Tuppen ◽  
Stephen Rose ◽  
Loukia Drosopoulou

2016 ◽  
Vol 60 (2) ◽  
pp. 294-296 ◽  
Author(s):  
Elizabeth Toon ◽  
Carsten Timmermann ◽  
Michael Worboys

Author(s):  
Dariusz Jemielniak

The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.


2020 ◽  
Vol 34 (5) ◽  
pp. 670-686
Author(s):  
Karl–Heinz Renner ◽  
Stephanie Klee ◽  
Timo von Oertzen

Behaviour and the individual person are important but widely neglected topics of personality psychology. We argue that new technologies to collect and new methods to analyse Big (Behavioural) Data have the potential to bring back both more behaviour and the individual person into personality science. The call for studying the individual person in the history of personality science, the related idiographic/nomothetic divide, as well as attempts to reconcile these two approaches are briefly reviewed. Furthermore, different meanings of the term idiographic and some unique selling points that emphasize the importance of idiographic research are highlighted. A nonexhaustive literature review shows that a wealth of behaviours are considered in extant personality studies using such Big Data but only in a nomothetic way. Against this background, we demonstrate the potential of Big Data collection and analysis with regard to four idiographic research topics: (i) unique manifestations of common traits and the resurgence of personal dispositions, (ii) idiographic prediction, (iii) intraindividual consistency versus variability of behaviour and (iv) intraindividual personality trait change through intervention. Methodological, ethical and legal pitfalls of doing Big Data research with individual persons as well as potential countermeasures are considered.


2017 ◽  
Vol 61 (4) ◽  
pp. 609-611
Author(s):  
Frederick W. Gibbs ◽  
Jeffrey S. Reznick

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1118-1131
Author(s):  
Raid Abd Alreda Shekan ◽  
Ahmed Mahdi Abdulkadium ◽  
Hiba Ameer Jabir

In past few decades, big data has evolved as a modern framework that offers huge amount of data and possibilities for applying and/or promoting analysis and decision-making technologies with unparalleled importance for digital processes in organization, engineering and science. Because of the new methods in these domains, the paper discusses history of big data mining under the cloud computing environment. In addition to the pursuit of exploration of knowledge, Big Data revolution gives companies many exciting possibilities (in relation to new vision, decision making and business growths strategies). The prospect of developing large-data processing, data analytics, and evaluation through a cloud computing model has been explored. The key component of this paper is the technical description of how to use cloud computing and the uses of data mining techniques and analytics methods in predictive and decision support systems.


Early Music ◽  
2015 ◽  
Vol 43 (4) ◽  
pp. 649-660 ◽  
Author(s):  
Stephen Rose ◽  
Sandra Tuppen ◽  
Loukia Drosopoulou
Keyword(s):  
Big Data ◽  

2001 ◽  
Vol 18 (3) ◽  
pp. 135-136
Author(s):  
David Pearson ◽  
Susan Gove ◽  
John Lancaster

Sign in / Sign up

Export Citation Format

Share Document