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Investigators who choose to resubmit, must do so within 37 months of the new A0 application. The PI can continue to submit the application (hopefully responding to reviewer concerns with each submission) until the PI decides to not submit again, or the application is selected for funding. Learn more about the policy and view Frequently Asked Questions.

The National Center for Biotechnology Information's (NCBI) database of Genotypes and Phenotypes (dbGaP) is a great resource for leveraging existing genomic datasets. NCBI recently updated the dbGaP authorized access system to include some new, time-saving features:

• dbGaP and the eRA Commons Account: Requesting Investigators can now use eRA Commons to provide the Principal Investigator (PI), institution, and Signing Official (SO) names on their dbGaP applications. This information will now auto-populate when a PI creates a project. 


• dbGaP Project Renewal and the Annual Report: The dbGaP annual report is now a part of the project renewal. When a PI requests renewal of authorized access through dbGaP, the PI will be prompted to provide an annual progress report on research using the genomic dataset.


• Project Closeout: Project closeout is now facilitated through the dbGaP system. Within dbGaP, an authorized PI can request project closeout and complete the closeout report using the system. Once the closeout report is completed, the SO will be notified to provide confirmation of data destruction through the dbGaP system.

To learn more about the new features to controlled access in the dbGaP system, investigators can view the following online tutorials:

• Applying for dbGaP datasets 
• Renewal of authorized access 
• Closeout of a dbGaP project 

Image courtesy of Defense Advanced Research Projects Agency

The NIH's Big Data to Knowledge (BD2K) Initiative was created to enable biomedical scientists to capitalize more fully on the "big data" being generated by the biomedical research community.

The BD2K Initiative currently has a number of open or upcoming Funding Opportunity Announcements (FOAs) that may be of interest to cancer epidemiologists. Those opportunities are described and listed below.

Open FOAs:

• RFA-HG-14-020

Development of Software and Analysis Methods for Biomedical Big Data in Targeted Areas of High Need (U01)

Application due date: June 19, 2014

Expires: June 20, 2014 

This FOA solicits development of innovative analytical methods and software tools with the objective of addressing critical current and emerging needs of the biomedical research community for using, managing, and analyzing
larger and more complex datasets inherent to biomedical Big Data, focusing on four topic areas: data compression/reduction, data visualization, data provenance, and data wrangling. 

• RFA-HG-14-008  

Courses for Skills Development in Biomedical Big Data Science (R25)

Application due dates: April 1, 2014; April 1, 2015; and April 1, 2016

Expires: April 2, 2016 

The goal of this FOA is to complement and/or enhance the training of a workforce to meet the nation's biomedical, behavioral, and clinical research needs.  In particular, this FOA will support courses for skills developmentin topics necessary for the utilization of Big Data, including the computational and statistical sciences in a biomedical context.   

• RFA-HG-14-009 

Open Educational Resources for Biomedical Big Data (R25) 

Application due dates: April 1, 2014; April 1, 2015; and April 1, 2016

Expires: April 2, 2016 

This goal of this FOA is to complement and/or enhance the training of a workforce to meet the nation's biomedical, behavioral, and clinical research needs. In particular, this FOA will support curriculum or methods development of innovative open educational resources that enhance the ability of the workforce to use and analyze biomedical Big Data.

Upcoming FOAs:

• NOT-HG-14-011 

Predoctoral Training in Biomedical Big Data Science (T32)

Expected date of FOA: Spring 2014

Expected application due date: Summer 2014

This FOA will support graduate training programs in Big Data science. Because Big Data science is interdisciplinary in nature, it is expected that trainees will acquire during the training experience competency in all three relevant areas -computer science/informatics, statistics/mathematics, and biomedical science - and expertise in aspects of data science that are essential to biomedical science. The training should include those aspects of computer science/informatics and statistics/mathematics that are directly relevant to the biomedical sciences.

• NOT-HG-14-022  and NOT-HG-14-023