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.

Culture, Context, and Development

2019 ◽  
pp. 187-208
Author(s):  
Harry W. Gardiner
Keyword(s):  
Cross Cultural Psychology ◽  
Human Development ◽  
Cultural Psychology ◽  
Social Issues ◽  
Cross Cultural ◽  
Contextual Influences ◽  
Future Directions ◽  
Theoretical Perspectives ◽  
Challenges And Opportunities ◽  
Future Path

Cross-cultural psychology and human development are currently experiencing an exciting period of growth. Segall, Lonner, and Berry have noted that when all psychology finally takes into account the effects of culture on human behavior (and vice versa), terms like cross-cultural and cultural psychology will become unnecessary. At that point, all psychology will be truly cultural. In this chapter, the author defines cross-cultural human development; theoretical perspectives and models; emerging themes, such as contextual influences; applications to social issues; and future directions. As the author has earlier stated, tremendous challenges and opportunities lie ahead and speculating about the future path of cross-cultural psychology is difficult.

scholarly journals Big Data is Too Small: Research Implications of Class Inequality for Online Data Collection

2018 ◽  
Author(s):  
Jen Schradie
Keyword(s):  
Big Data ◽  
Data Science ◽  
Digital Data ◽  
The Internet ◽  
Sociological Research ◽  
Marginalized Populations ◽  
Online Data ◽  
Persistent Problem ◽  
Current State ◽  
Using Data

With a growing interest in data science and online analytics, researchers are increasingly using data derived from the Internet. Whether for qualitative or quantitative analysis, online data, including “Big Data,” can often exclude marginalized populations, especially those from the poor and working class, as the digital divide remains a persistent problem. This methodological commentary on the current state of digital data and methods disentangles the hype from the reality of digitally produced data for sociological research. In the process, it offers strategies to address the weaknesses of data that is derived from the Internet in order to represent marginalized populations.

scholarly journals Data Science: Trends, Perspectives, and Prospects

2021 ◽  
Author(s):  
Chaolemen Borjigin ◽  
Chen Zhang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Scientific Discovery ◽  
Big Data Analytics ◽  
Theoretical Studies ◽  
Data Intensive ◽  
Data Ethics ◽  
Big Data Visualization ◽  
Data Products ◽  
And Performance

Abstract Data Science is one of today’s most rapidly growing academic fields and has significant implications for all conventional scientific studies. However, most of the relevant studies so far have been limited to one or several facets of Data Science from a specific application domain perspective and fail to discuss its theoretical framework. Data Science is a novel science in that its research goals, perspectives, and body of knowledge is distinct from other sciences. The core theories of Data Science are the DIKW pyramid, data-intensive scientific discovery, data science lifecycle, data wrangling or munging, big data analytics, data management and governance, data products development, and big data visualization. Six main trends characterize the recent theoretical studies on Data Science: growing significance of DataOps, the rise of citizen data scientists, enabling augmented data science, diversity of domain-specific data science, and implementing data stories as data products. The further development of Data Science should prioritize four ways to turning challenges into opportunities: accelerating theoretical studies of data science, the trade-off between explainability and performance, achieving data ethics, privacy and trust, and aligning academic curricula to industrial needs.

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

scholarly journals Growth and remodelling of living tissues: perspectives, challenges and opportunities

2019 ◽  
Vol 16 (157) ◽  
pp. 20190233 ◽  
Author(s):  
Davide Ambrosi ◽  
Martine Ben Amar ◽  
Christian J. Cyron ◽  
Antonio DeSimone ◽  
Alain Goriely ◽  
...  
Keyword(s):  
Time Scales ◽  
Early Life ◽  
Soft Tissues ◽  
Surgical Intervention ◽  
Living Matter ◽  
Future Directions ◽  
Current State ◽  
Challenges And Opportunities ◽  
Living Tissues ◽  
Short Time

One of the most remarkable differences between classical engineering materials and living matter is the ability of the latter to grow and remodel in response to diverse stimuli. The mechanical behaviour of living matter is governed not only by an elastic or viscoelastic response to loading on short time scales up to several minutes, but also by often crucial growth and remodelling responses on time scales from hours to months. Phenomena of growth and remodelling play important roles, for example during morphogenesis in early life as well as in homeostasis and pathogenesis in adult tissues, which often adapt to changes in their chemo-mechanical environment as a result of ageing, diseases, injury or surgical intervention. Mechano-regulated growth and remodelling are observed in various soft tissues, ranging from tendons and arteries to the eye and brain, but also in bone, lower organisms and plants. Understanding and predicting growth and remodelling of living systems is one of the most important challenges in biomechanics and mechanobiology. This article reviews the current state of growth and remodelling as it applies primarily to soft tissues, and provides a perspective on critical challenges and future directions.

scholarly journals Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

2019 ◽  
Vol 9 (11) ◽  
pp. 2331 ◽  
Author(s):  
Luis Bote-Curiel ◽  
Sergio Muñoz-Romero ◽  
Alicia Gerrero-Curieses ◽  
José Luis Rojo-Álvarez
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Science ◽  
Critical Discussion ◽  
Healthcare Sector ◽  
Simple Description ◽  
Future Evolution ◽  
Success Stories ◽  
Current State ◽  
Application Fields

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

scholarly journals Big Data Challenges and Opportunities in Agriculture

2020 ◽  
Vol 11 (1) ◽  
pp. 48-66
Author(s):  
Maya Gopal P.S. ◽  
Bhargavi Renta Chintala
Keyword(s):  
Big Data ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Complete Picture ◽  
Data Generation ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Challenges And Opportunities ◽  
Technical Challenges ◽  
Exciting Area

This article reviews various aspects of research concerning the background and state-of-the-art of big data in agriculture. This article focuses on data generation, storage, analysis and visualization in big data. In every phase, technical challenges and the latest advancement are discussed, and these discussions aim to provide a comprehensive overview and complete picture of this exciting area. This survey is concluded with a discussion on the application of big data in precision agriculture and its future directions.

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