scholarly journals Data Science and Big Data in Energy Forecasting

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3224 ◽  
Author(s):  
Francisco Martínez-Álvarez ◽  
Alicia Troncoso ◽  
José Riquelme

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.

2020 ◽  
Vol 13 (2) ◽  
pp. 131-131
Author(s):  
Stefania Tomasiello ◽  
Feng Feng ◽  
Sotiris Kotsiantis ◽  
Alireza Khastan

2017 ◽  
Vol 24 (1) ◽  
pp. 1 ◽  
Author(s):  
Philip J. Scott ◽  
Ronald Cornet ◽  
Colin McCowan ◽  
Niels Peek ◽  
Paolo Fraccaro ◽  
...  

Introduction: The Informatics for Health congress, 24-26 April 2017, in Manchester, UK, brought together the Medical Informatics Europe (MIE) conference and the Farr Institute International Conference. This special issue of the Journal of Innovation in Health Informatics contains 113 presentation abstracts and 149 poster abstracts from the congress.Discussion: The twin programmes of “Big Data” and “Digital Health” are not always joined up by coherent policy and investment priorities. Substantial global investment in health IT and data science has led to sound progress but highly variable outcomes. Society needs an approach that brings together the science and the practice of health informatics. The goal is multi-level Learning Health Systems that consume and intelligently act upon both patient data and organizational intervention outcomes.Conclusions: Informatics for Health demonstrated the art of the possible, seen in the breadth and depth of our contributions. We call upon policy makers, research funders and programme leaders to learn from this joined-up approach.


2018 ◽  
Vol 4 (3) ◽  
pp. 205630511878450 ◽  
Author(s):  
Annette N Markham ◽  
Katrin Tiidenberg ◽  
Andrew Herman

This is an introduction to the special issue of “Ethics as Methods: Doing Ethics in the Era of Big Data Research.” Building on a variety of theoretical paradigms (i.e., critical theory, [new] materialism, feminist ethics, theory of cultural techniques) and frameworks (i.e., contextual integrity, deflationary perspective, ethics of care), the Special Issue contributes specific cases and fine-grained conceptual distinctions to ongoing discussions about the ethics in data-driven research. In the second decade of the 21st century, a grand narrative is emerging that posits knowledge derived from data analytics as true, because of the objective qualities of data, their means of collection and analysis, and the sheer size of the data set. The by-product of this grand narrative is that the qualitative aspects of behavior and experience that form the data are diminished, and the human is removed from the process of analysis. This situates data science as a process of analysis performed by the tool, which obscures human decisions in the process. The scholars involved in this Special Issue problematize the assumptions and trends in big data research and point out the crisis in accountability that emerges from using such data to make societal interventions. Our collaborators offer a range of answers to the question of how to configure ethics through a methodological framework in the context of the prevalence of big data, neural networks, and automated, algorithmic governance of much of human socia(bi)lity


2020 ◽  
Vol 34 (1) ◽  
pp. 19-42
Author(s):  
David Moats

It is often claimed that the rise of so called ‘big data’ and computationally advanced methods may exacerbate tensions between disciplines like data science and anthropology. This paper is an attempt to reflect on these possible tensions and their resolution, empirically. It contributes to a growing body of literature which observes interdisciplinary collabrations around new methods and digital infrastructures in practice but argues that many existing arrangements for interdisciplinary collaboration enforce a separation between disciplines in which identities are not really put at risk. In order to disrupt these standard roles and routines we put on a series of workshops in which mainly self-identified qualitative or non-technical researchers were encouraged to use digital tools (scrapers, automated text analysis and data visualisations). The paper focuses on three empirical examples from the workshops in which tensions, both between disciplines and methods, flared up and how they were ultimately managed or settled. In order to characterise both these tensions and negotiating strategies I draw on Woolgar and Stengers’ use of the humour and irony to describe how disciplines relate to each others truth claims. I conclude that while there is great potential in more open-ended collaborative settings, qualitative social scientists may need to confront some of their own disciplinary baggage in order for better dialogue and more radical mixings between disciplines to occur.


2020 ◽  
Vol 12 (2) ◽  
pp. 301
Author(s):  
Francisco Martínez-Álvarez ◽  
Dieu Tien Bui

This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI’s Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance.


Author(s):  
Shaveta Bhatia

 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.


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