Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text.)
Line 1: Line 1:
<!-- <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 SprengholzQuantMethSci2018 14-2.png|240px]]</div> -->
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 BezuidenhoutDataSciJo2017 16.png|240px]]</div>
'''"[[Journal:Eleven quick tips for architecting biomedical informatics workflows with cloud computing|Eleven quick tips for architecting biomedical informatics workflows with cloud computing]]"'''
'''"[[Journal:Technology transfer and true transformation: Implications for open data|Technology transfer and true transformation: Implications for open data]]"'''


[[Cloud computing]] has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based [[workflow]]s offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for designing biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world’s largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction. ('''[[Journal:Eleven quick tips for architecting biomedical informatics workflows with cloud computing|Full article...]]''')<br />
When considering the “openness” of data, it is unsurprising that most conversations focus on the online environment—how data is collated, moved, and recombined for multiple purposes. Nonetheless, it is important to recognize that the movements online are only part of the data lifecycle. Indeed, considering where and how data are created—namely, the research setting—are of key importance to open data initiatives. In particular, such insights offer key understandings of how and why scientists engage with in practices of openness, and how data transitions from personal control to public ownership. This paper examines research settings in low/middle-income countries (LMIC) to better understand how resource limitations influence open data buy-in. ('''[[Journal:Technology transfer and true transformation: Implications for open data|Full article...]]''')<br />
<br />
<br />
''Recently featured'':
''Recently featured'':
: ▪ [[Journal:Eleven quick tips for architecting biomedical informatics workflows with cloud computing|Eleven quick tips for architecting biomedical informatics workflows with cloud computing]]
: ▪ [[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system|Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system]]
: ▪ [[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system|Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system]]
: ▪ [[Journal:Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system|Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system]]
: ▪ [[Journal:Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system|Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system]]
: ▪ [[Journal:DataCare: Big data analytics solution for intelligent healthcare management|DataCare: Big data analytics solution for intelligent healthcare management]]

Revision as of 15:28, 20 August 2018

Fig1 BezuidenhoutDataSciJo2017 16.png

"Technology transfer and true transformation: Implications for open data"

When considering the “openness” of data, it is unsurprising that most conversations focus on the online environment—how data is collated, moved, and recombined for multiple purposes. Nonetheless, it is important to recognize that the movements online are only part of the data lifecycle. Indeed, considering where and how data are created—namely, the research setting—are of key importance to open data initiatives. In particular, such insights offer key understandings of how and why scientists engage with in practices of openness, and how data transitions from personal control to public ownership. This paper examines research settings in low/middle-income countries (LMIC) to better understand how resource limitations influence open data buy-in. (Full article...)

Recently featured:

Eleven quick tips for architecting biomedical informatics workflows with cloud computing
Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system
Developing a file system structure to solve healthcare big data storage and archiving problems using a distributed file system