Facilitating Open Science Through NITRC
On February 12th, TCG’s Nina Preuss will give a talk to the National Institutes of Health (NIH) Biomedical Computing Interest Group (BCIG). Her lecture, “Researchers Openly Share Tools and Data for Neuroimaging Research,” will highlight the successes of the Neuroimaging Informatics and Resources Clearinghouse (NITRC), an online service that is “used to recreate studies… garner new insights… and help NIH research dollars go further.” Ms. Preuss is the Principal Investigator and Project Manager of NITRC. The lecture is aimed toward researchers with an interest in software and data sharing, big data, analytics, and cloud computing.
“TCG’s work on NITRC and other NIH contracts has moved the needle towards open science,” says Nina. “Research data is expensive to procure and often too big to easily share and compute against. I’m quite excited to have the opportunity to share our experience with the BCIG community.”
The event is being held in NIH Building 50, room 1328/1334 at 3 p.m. At 8 p.m., a self-hosted dinner is being held at Bistro LaZeez, where Ms. Preuss will be available for questions and conversation. For more information, please contact Jim DeLeo of the NIH at firstname.lastname@example.org or at 301–496-3848.
About the National Institutes of Health
The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, is the nation’s medical research agency—making important discoveries that improve health and save lives.
Thanks in large part to NIH-funded medical research, Americans today are living longer and healthier. Life expectancy in the United States has jumped from 47 years in 1900 to 78 years as reported in 2009, and disability in people over age 65 has dropped dramatically in the past 3 decades. In recent years, nationwide rates of new diagnoses and deaths from all cancers combined have fallen significantly.
About the Biomedical Computing Interest Group (BCIG)
The primary purpose of BCIG is to further novel uses of computers and automation in support of clinical care and biomedical research. The group will seek to go beyond the use of computers to enhance or facilitate traditional tasks. We will promote computational methods as a research tool.
Particular areas of interest include data visualization, data mining, machine learning, neural networks, receiver operating characteristic methodology, genetic algorithms, novel data models, novel automation systems, component software development, modern statistical methodology, data base registries, hospital and community health care information systems, and biomedical computing technology transfer.