The NIH Big Data to Knowledge Program (BD2K) is soliciting feedback from the research community on the development of a biomedical data catalog to make biomedical research data findable and citable, as PubMed does for scientific publications.


All responses must be submitted via email to by June 25, 2013.


Below is the wording of the NIH announcement:


The NIH Big Data to Knowledge Program (BD2K) is soliciting feedback from the research community via this Request for Information (RFI). A workshop is planned to help refine our thinking on constructing a data catalogue: a way of ensuring that NIH supported data is findable and citable. A data catalogue is not a repository but would help make data findable and citable. In addition to supplying core, minimal metadata to ensure a valid data reference, it is envisioned that a Data Catalog would include links out to the location of the data, to the NIH Reporter record of the grant that supported the research, to relevant publications within PubMed or journals, and possibly to associated software or algorithms. This RFI is meant to solicit feedback from the research community on this idea. Information from the RFI will help shape the workshop and subsequent development.


Request for Information (RFI): Input on Development of a NIH Data Catalog


Several MSI PIs were recently awarded 2013 Minnesota Futures grants from the Office of the Vice President for Research (OVPR). The Minnesota Futures grant program supports interdisciplinary research projects with the goal of making them competitive for external funding. This year’s grants both involve research into cancer treatments. Complete descriptions of the projects can be found on the OVPR Research blog.


Associate Professor Christy Haynes (Chemistry), Professor John Bischof (Mechanical Engineering), Assistant Professor Chris Hogan (Mechanical Engineering), and Professor Michael Garwood (Center for Magnetic Resonance Research) are co-investigators on a project entitled “Maximizing Magnetic Relaxation and Heating in Nanoparticle Therapeutics.” The goal of this project is to contribute to the development of a treatment for cancer that uses ion oxide nanoparticles.


These four faculty members have all used MSI to support their research projects for several years. Professor Haynes performs investigations into the fundamentals of cellular-level mechanisms of the immune system. Professor Bischof is involved in a number of projects related to phase change in biological systems. Professor Hogan creates Brownian dynamics simulations of nanoparticle transport in aerosols. Professor Garwood develops novel frequency swept pulse sequences for magnetic resonance imaging.


Assistant Professor Benjamin Hackel (Chemical Engineering) is a co-investigator on a project entitled “Targeting Metastatic Breast Cancer With Dual Specificity.” The goal of this project is to develop breast cancer treatments that will significantly reduce the number of metastases.


Professor Hackel uses MSI in support of his work engineering high affinity binding proteins.



MSI PI Professor Bin He (Biomedical Engineering and Director, Institute for Engineering in Medicine) has developed a method for people to control objects with their brains. This technology, which was published recently in the Journal of Neural Engineering, uses brain waves picked up by an electroencephalogram (EEG) cap on a person’s head to control a flying robot. You can read more in the University News story.  


Professor He uses MSI resources in his studies of the brain. His current work includes high-resolution spatio-temporal bioelectromagnetic source imaging, functional MRI, and bio-dielectric properties imaging. He and his group perform computer simulation studies of the electromagnetic field of the brain; these sorts of simulations require supercomputing capabilities.



The OVPR’s Business blog recently posted a story about services that MSI is providing to a research and development team at Cargill. The project involves a problem related to poor aeration in a fermenting process. MSI assisted Cargill staff to license and install specialized software on one of the supercomputers, and are using the system to model possible solutions.


You can read the article on the Business blog. 


The Virtual School of Computational Science and Engineering (VSCSE) is holding two courses this summer. These courses are open to graduate students, post-docs, and young professionals who want to expand their skills with advanced computational resources. The courses are offered at institutions around the country, allowing participants to go to the most convenient location.


Descriptions of the courses are shown below. You can register at the XSEDE portal. Questions can be mailed to


Summer 2013 VSCSE Courses:


Data Intensive Summer School (July 8 – 10, 2013)

From the VSCSE website: The Data Intensive Summer School focuses on the skills needed to manage, process and gain insight from large amounts of data. It is targeted at researchers from the physical, biological, economic and social sciences that are beginning to drown in data. We will cover the nuts and bolts of data intensive computing, common tools and software, predictive analytics algorithms, data management and non-relational database models. Given the short duration of the summer school, the emphasis will be on providing a solid foundation that the attendees can use as a starting point for advanced topics of particular relevance to their work.


Proven Algorithmic Techniques for Many-Core Processors (July 29 – August 2, 2013) 

From the VSCSE website: Studying many current GPU computing applications, we have learned that the limits of an application's scalability are often related to some combination of memory bandwidth saturation, memory contention, imbalanced data distribution, or data structure/algorithm interactions. Successful GPU application developers often adjust their data structures and problem formulation specifically for massive threading and executed their threads leveraging shared on-chip memory resources for bigger impact. We looked for patterns among those transformations, and here present the seven most common and crucial algorithm and data optimization techniques we discovered. Each can improve performance of applicable kernels by 2-10X in current processors while improving future scalability.