scholarly journals Trends in big data analyses by multicenter collaborative translational research in psychiatry

2021 ◽  
Author(s):  
Toshiaki Onitsuka ◽  
Yoji Hirano ◽  
Kiyotaka Nemoto ◽  
Naoki Hashimoto ◽  
Itaru Kushima ◽  
...  
Keyword(s):  
Big Data ◽  
Translational Research ◽  
Data Analyses

Big Data Analyses of Korea's Nation Branding on Google and Facebook

2020 ◽  
Vol 51 (1) ◽  
pp. 151-174
Author(s):  
Chung Joo Chung ◽  
Yunna Rhee ◽  
Heewon Cha
Keyword(s):  
Big Data ◽  
Nation Branding ◽  
Data Analyses

Exploring Support Vector Machines for Big Data Analyses

2021 ◽  
Author(s):  
Siyang Lu ◽  
Yihong Chen ◽  
Xiaolin Zhu ◽  
Ziyi Wang ◽  
Yangjun Ou ◽  
...  
Keyword(s):  
Big Data ◽  
Support Vector Machines ◽  
Support Vector ◽  
Data Analyses ◽  
Vector Machines

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care

2015 ◽  
Vol 22 (3) ◽  
pp. 600-607 ◽  
Author(s):  
Bonnie L Westra ◽  
Gail E Latimer ◽  
Susan A Matney ◽  
Jung In Park ◽  
Joyce Sensmeier ◽  
...  
Keyword(s):  
Health Care ◽  
Big Data ◽  
Translational Research ◽  
Clinical Data ◽  
Action Plan ◽  
Information Structures ◽  
Data Repositories ◽  
National Action Plan ◽  
Quality Reporting ◽  
Nursing Data

Abstract Background There is wide recognition that, with the rapid implementation of electronic health records (EHRs), large data sets are available for research. However, essential standardized nursing data are seldom integrated into EHRs and clinical data repositories. There are many diverse activities that exist to implement standardized nursing languages in EHRs; however, these activities are not coordinated, resulting in duplicate efforts rather than building a shared learning environment and resources. Objective The purpose of this paper is to describe the historical context of nursing terminologies, challenges to the use of nursing data for purposes other than documentation of care, and a national action plan for implementing and using sharable and comparable nursing data for quality reporting and translational research. Methods In 2013 and 2014, the University of Minnesota School of Nursing hosted a diverse group of nurses to participate in the Nursing Knowledge: Big Data and Science to Transform Health Care consensus conferences. This consensus conference was held to develop a national action plan and harmonize existing and new efforts of multiple individuals and organizations to expedite integration of standardized nursing data within EHRs and ensure their availability in clinical data repositories for secondary use. This harmonization will address the implementation of standardized nursing terminologies and subsequent access to and use of clinical nursing data. Conclusion Foundational to integrating nursing data into clinical data repositories for big data and science, is the implementation of standardized nursing terminologies, common data models, and information structures within EHRs. The 2014 National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare builds on and leverages existing, but separate long standing efforts of many individuals and organizations. The plan is action focused, with accountability for coordinating and tracking progress designated.

scholarly journals Big Data Management in Maritime Transport

2019 ◽  
Vol 57 (1) ◽  
pp. 123-141
Author(s):  
Marija Jović ◽  
Edvard Tijan ◽  
Rebecca Marx ◽  
Berit Gebhard
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Understanding ◽  
Future Perspective ◽  
Maritime Transport ◽  
Data Usage ◽  
Data Analyses ◽  
Efficient Data ◽  
Routing Monitoring ◽  
Efficient Data Collection

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport.

Sign in / Sign up

Export Citation Format

Share Document