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BD2K KnowEng

KnowEnG (pronounced "knowing") is a National Institutes of Health-funded initiative that brings together researchers from the University of Illinois and the Mayo Clinic to create a Center of Excellence in Big Data Computing. It is part of the Big Data to Knowledge (BD2K) Initiative that NIH launched in 2012 to tap the wealth of information contained in biomedical Big Data. KnowEnG is one of 11 Centers of Excellence in Big Data Computing funded by NIH in 2014.

This four-year project will create a platform where biomedical scientists, clinical researchers, and bioinformaticians can bring their own data and perform common as well as advanced analysis tasks, guided by the “knowledge network”, a large compendium of public-domain data. The knowledge network embodies community data on genes, proteins, functions, species, and phenotypes, and relationships among them. Instead of analyzing their data set in an isolated fashion, researchers will be able to go straight to asking global questions. The infrastructure, capacity and tools will grow with the datasets.

DIBBs Whole Tale

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NIST Materials Data Facility

The Materials Data Facility (MDF) is a collaboration between Globus at the University of Chicago, the National Center for Supercomputing Applications (NCSA-UIUC), and the Center for Hierarchical Materials Design (CHiMaD) a NIST-funded center of excellence.

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NSF Midwest BigData Hub

The nation faces increasing challenges in collecting, managing, serving, mining, and analyzing rapidly growing and increasingly complex data and information collections to create actionable knowledge and guide decision-making. All sectors of society are profoundly impacted and need novel solutions that leverage the breadth of expertise in academia, industry, and government.  To address this need, a diverse and committed network of partners has created a nimble and flexible regional Midwest Big Data Hub (MBDH), responding to Big Data challenges and capturing special opportunities, interests, and resources unique to the Midwest.

iSEE Plants in silico

As the Earth’s population climbs toward 9 billion by 2050 — and the world climate continues to change, affecting temperatures, weather patterns, water supply, and even the seasons — future food security has become a grand world challenge. Accurate prediction of how food crops react to climate change will play a critical role in ensuring food security.  An ability to computationally mimic the growth, development and response of plants to the environment will allow researchers to conduct many more experiments than can realistically be achieved in the field. Designing more sustainable crops to increase productivity depends on complex interactions between genetics, environment, and ecosystem. Therefore, creation of an in silico — computer simulation — platform that can link models across different biological scales, from cell to ecosystem level, has the potential to provide more accurate simulations of plant response to the environment than any single model could alone.

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ARPAE TERRA

Phenotypes are measurable features that indicate how they will grow and respond to stresses such as heat, drought, and pathogens. Breeding is currently limited by the speed at which phenotypes can be measured, and the information that can be extracted from these measurements. Currently, measurements used to predict yield include measuring leaf thickness with a caliper or height with a meter stick. More sophisticated instruments used to quantify plant architecture, carbon uptake, water use, and root growth do not scale to the thousands or tens of thousands of individual plants that need to be evaluated in a breeding program.  TERRA-REF will develop an integrated phenotyping system for energy Sorghum that leverages genetics and breeding, automation, remote plant sensing, genomics, and computational analytics.

Possible Pilots