scholarly journals Challenges and Opportunities in Statistics and Data Science: Ten Research Areas

2020 ◽  
Author(s):  
Xuming He ◽  
Xihong Lin
Keyword(s):  
Data Science ◽  
Research Areas ◽  
Challenges And Opportunities

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.

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 Research challenges and opportunities in the Caribbean area: first bibliometric study in the French West Indies, from 1989 to 2018

2021 ◽  
Vol 45 ◽  
pp. 1
Author(s):  
Cédric Contaret ◽  
Raymond Cesaire ◽  
Jacqueline Deloumeaux ◽  
Rémi Neviere ◽  
Dabor Resiere ◽  
...  
Keyword(s):  
Scientific Output ◽  
University Teaching ◽  
University Hospital ◽  
Teaching Hospitals ◽  
Clinical Neurology ◽  
Collaborative Partnerships ◽  
Main Research ◽  
Research Areas ◽  
Challenges And Opportunities ◽  
The University

Objective. To analyze, describe, and quantify the collaborations and scientific output of the two university teaching hospitals of Martinique and Guadeloupe, at the regional, national, and international level. Methods. A bibliometrics analysis was performed from the international databases Web of Science and PubMed, for the period from 1989 to 2018, inclusive (30 years). Three types of bibliometric indicators were used, namely quantitative indicators, performance indicators, and organization-specific indicators. Affiliations of the first and last authors were identified from PubMed. Results. Between 1989 and 2018, a total of 1 522 indexed articles were published with at least one author affiliated to either the University Hospital of Martinique (n = 827) or the University Hospital of Guadeloupe (n = 685). The majority of articles were in category Q1 (35.8% for Martinique and 35.2% for Guadeloupe). In Martinique, over the last 30 years, the three main research areas have been clinical neurology, ophthalmology, and surgery, together representing 28.7% of all research areas, with the highest number of articles published in the field of clinical neurology (n = 81). In the University Hospital of Guadeloupe, the area of hematology was largely represented, with 79 articles published. For both hospitals, the first and last authors of the article published were mainly from mainland France Conclusions. This quantitative analysis shows the development of medical and scientific research in Martinique and Guadeloupe over the last three decades, as well as the extent of their collaborative partnerships at the national and international levels.

scholarly journals Biomedical informatics meets data science: current state and future directions for interaction

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Philip R O Payne ◽  
Elmer V Bernstam ◽  
Justin B Starren
Keyword(s):  
Big Data ◽  
Data Science ◽  
Clinical Decision Making ◽  
Social Issues ◽  
Biomedical Informatics ◽  
Scientific Discovery ◽  
Topic Area ◽  
Future Directions ◽  
Current State ◽  
Challenges And Opportunities

Abstract There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.

scholarly journals Infectious Disease Research in the Era of Big Data

2020 ◽  
Vol 3 (1) ◽  
pp. 43-59
Author(s):  
Peter M. Kasson
Keyword(s):  
Infectious Disease ◽  
Large Scale ◽  
Data Science ◽  
Research Progress ◽  
Biomedical Data ◽  
Disease Research ◽  
Research Areas ◽  
Large Scale Data ◽  
Infectious Disease Research ◽  
Unique Nature

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

scholarly journals Data science ethics in government

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160119 ◽  
Author(s):  
Cat Drew
Keyword(s):  
Data Science ◽  
New Technologies ◽  
Unintended Consequences ◽  
Ethical Framework ◽  
Social Good ◽  
Science Ethics ◽  
Policy Goals ◽  
Challenges And Opportunities ◽  
Government Data ◽  
The Government

Data science can offer huge opportunities for government. With the ability to process larger and more complex datasets than ever before, it can provide better insights for policymakers and make services more tailored and efficient. As with all new technologies, there is a risk that we do not take up its opportunities and miss out on its enormous potential. We want people to feel confident to innovate with data. So, over the past 18 months, the Government Data Science Partnership has taken an open, evidence-based and user-centred approach to creating an ethical framework. It is a practical document that brings all the legal guidance together in one place, and is written in the context of new data science capabilities. As part of its development, we ran a public dialogue on data science ethics, including deliberative workshops, an experimental conjoint survey and an online engagement tool. The research supported the principles set out in the framework as well as provided useful insight into how we need to communicate about data science. It found that people had a low awareness of the term ‘data science’, but that showing data science examples can increase broad support for government exploring innovative uses of data. But people's support is highly context driven. People consider acceptability on a case-by-case basis, first thinking about the overall policy goals and likely intended outcome, and then weighing up privacy and unintended consequences. The ethical framework is a crucial start, but it does not solve all the challenges it highlights, particularly as technology is creating new challenges and opportunities every day. Continued research is needed into data minimization and anonymization, robust data models, algorithmic accountability, and transparency and data security. It also has revealed the need to set out a renewed deal between the citizen and state on data, to maintain and solidify trust in how we use people's data for social good. This article is part of the themed issue ‘The ethical impact of data science’.

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