scholarly journals Intersection of Data Science and Smart Destinations: A Systematic Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander Aguirre Montero ◽  
José Antonio López-Sánchez
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Open Data ◽  
Research Topic ◽  
Tourism Marketing ◽  
Research Areas ◽  
Current Trends ◽  
Smart Card Data ◽  
Challenges And Opportunities ◽  
Smart Data

This systematic review adopts a formal and structured approach to review the intersection of data science and smart tourism destinations in terms of components found in previous research. The study period corresponds to 1995–2021 focusing the analysis mainly on the last years (2015–2021), identifying and characterizing the current trends on this research topic. The review comprises documentary research based on bibliometric and conceptual analysis, using the VOSviewer and SciMAT software to analyze articles from the Web of Science database. There is growing interest in this research topic, with more than 300 articles published annually. Data science technologies on which current smart destinations research is based include big data, smart data, data analytics, social media, cloud computing, the internet of things (IoT), smart card data, geographic information system (GIS) technologies, open data, artificial intelligence, and machine learning. Critical research areas for data science techniques and technologies in smart destinations are public tourism marketing, mobility-accessibility, and sustainability. Data analysis techniques and technologies face unprecedented challenges and opportunities post-coronavirus disease-2019 (COVID-19) to build on the huge amount of data and a new tourism model that is more sustainable, smarter, and safer than those previously implemented.

scholarly journals Electric Vehicles: A Data Science Perspective Review

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1190 ◽  
Author(s):  
Dario Pevec ◽  
Jurica Babic ◽  
Vedran Podobnik
Keyword(s):  
Electric Vehicles ◽  
Data Science ◽  
Combustion Engine ◽  
Open Data ◽  
Business And Society ◽  
Transportation Industry ◽  
Main Research ◽  
Charging Station ◽  
Current Trends ◽  
Green Transportation

Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r)evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

scholarly journals Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

2021 ◽  
Author(s):  
Neal Robert Haddaway ◽  
Charles T. Gray ◽  
Matthew Grainger
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Open Data ◽  
Study Data ◽  
Data Reuse ◽  
Complex Data ◽  
Standard Practices ◽  
Machine Readable

One of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

scholarly journals Access to Credit and Women Entrepreneurs: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 12
Author(s):  
Ferdinando Giglio
Keyword(s):  
Systematic Review ◽  
Women Entrepreneurs ◽  
Supply And Demand ◽  
Research Topic ◽  
Financial Resources ◽  
Review Of The Literature ◽  
Cultural Traditions ◽  
Main Research ◽  
Access To Credit ◽  
Research Areas

This document aims to investigate some of the problems faced by women entrepreneurs when they request access to credit. Through the systematic review of the literature, documents relating to the research topic have been detected. A detailed analysis revealed four main research areas: supply and demand barriers, obstacles related to the characteristics of the entrepreneur and the enterprise, lack of financial resources and problems related to the country’s social and cultural traditions. The different studies have been conducted in non-European countries. Studies could be carried in Italy given the shortage. Other variables may be added to the model. It is a document that seeks to understand the situation in which women entrepreneurs find themselves, especially in the workplace.

scholarly journals Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Neal R. Haddaway ◽  
Charles T. Gray ◽  
Matthew Grainger
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Open Data ◽  
Study Data ◽  
Data Reuse ◽  
Complex Data ◽  
Standard Practices ◽  
Machine Readable

AbstractOne of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

2020 ◽  
pp. 2-6
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

scholarly journals Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

2021 ◽  
Author(s):  
Polona Caserman ◽  
Augusto Garcia-Agundez ◽  
Alvar Gámez Zerban ◽  
Stefan Göbel
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Meta Analysis ◽  
Subjective Evaluation ◽  
Future Research ◽  
Current Generation ◽  
Research Areas ◽  
Head Mounted Displays ◽  
Skin Response ◽  
Golden Standard

AbstractCybersickness (CS) is a term used to refer to symptoms, such as nausea, headache, and dizziness that users experience during or after virtual reality immersion. Initially discovered in flight simulators, commercial virtual reality (VR) head-mounted displays (HMD) of the current generation also seem to cause CS, albeit in a different manner and severity. The goal of this work is to summarize recent literature on CS with modern HMDs, to determine the specificities and profile of immersive VR-caused CS, and to provide an outlook for future research areas. A systematic review was performed on the databases IEEE Xplore, PubMed, ACM, and Scopus from 2013 to 2019 and 49 publications were selected. A summarized text states how different VR HMDs impact CS, how the nature of movement in VR HMDs contributes to CS, and how we can use biosensors to detect CS. The results of the meta-analysis show that although current-generation VR HMDs cause significantly less CS ($$p<0.001$$ p < 0.001 ), some symptoms remain as intense. Further results show that the nature of movement and, in particular, sensory mismatch as well as perceived motion have been the leading cause of CS. We suggest an outlook on future research, including the use of galvanic skin response to evaluate CS in combination with the golden standard (Simulator Sickness Questionnaire, SSQ) as well as an update on the subjective evaluation scores of the SSQ.

Data science skills: Building partnership for efficient school curriculum delivery in Africa

2020 ◽  
Vol 36 ◽  
pp. 49-62
Author(s):  
Nureni Olawale Adeboye ◽  
Peter Osuolale Popoola ◽  
Oluwatobi Nurudeen Ogunnusi
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Open Data ◽  
School Curriculum ◽  
Global Perspective ◽  
Special Focus ◽  
African Countries ◽  
Tertiary Institutions ◽  
Data Description ◽  
Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

scholarly journals On the relevance of data science for flight delay research: a systematic review

2020 ◽  
pp. 1-30
Author(s):  
Leonardo Carvalho ◽  
Alice Sternberg ◽  
Leandro Maia Gonçalves ◽  
Ana Beatriz Cruz ◽  
Jorge A. Soares ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Flight Delay
Sign in / Sign up

Export Citation Format

Share Document