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NIH 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.

NSF DIBBs Whole Tale

Scholarly publications today are still mostly disconnected from the underlying data and code used to produce the published results and findings, despite an increasing recognition of the need to share all aspects of the research process. As data become more open and transportable, a second layer of research output has emerged, linking research publications to the associated data, possibly along with its provenance. This trend is rapidly followed by a new third layer: communicating the process of inquiry itself by sharing a complete computational narrative that links method descriptions with executable code and data, thereby introducing a new era of reproducible science and accelerated knowledge discovery. In the Whole Tale (WT) project, all of these components are linked and accessible from scholarly publications. The third layer is broad, encompassing numerous research communities through science pathways (e.g., in astronomy, life and earth sciences, materials science, social science), and deep, using interconnected cyberinfrastructure pathways and shared technologies. The goal of this project is to strengthen the second layer of research output, and to build a robust third layer that integrates all parts of the story, conveying the holistic experience of reproducible scientific inquiry by (1) exposing existing cyberinfrastructure through popular frontends, e.g., digital notebooks (IPython, Jupyter), traditional scripting environments, and workflow systems; (2) developing the necessary 'software glue' for seamless access to different backend capabilities, including from DataNet federations and Data Infrastructure Building Blocks (DIBBs) projects; and (3) enhancing the complete data-to-publication lifecycle by empowering scientists to create computational narratives in their usual programming environments, enhanced with new capabilities from the underlying cyberinfrastructure (e.g., identity management, advanced data access and provenance APIs, and Digital Object Identifier-based data publications). The technologies and interfaces will be developed and stress-tested using a diverse set of data types, technical frameworks, and early adopters across a range of science domains.

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.

MDF is developing key data services for materials researchers with the goal of promoting open data sharing, simplifying data publication and curation workflows, encouraging data reuse, and providing powerful data discovery interfaces for data of all sizes and sources. Specifically, MDF services will allow individual researchers and institutions to 1) enable publication of large research datasets with flexible policies; 2) grant the ability to publish data directly from local storage, institutional data stores, or from cloud storage, without third-party publishers; 3) build extensible domain-specific metadata and automated metadata ingestion scripts for key data types; 4) develop publication workflows; 5) register a variety of resources for broader community discovery; and 6) access a discovery model that allows researchers to search, interrogate, and build upon existing published data.

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.

As a leader in plant biology, crop sciences and computer science, Illinois is uniquely positioned to head this initiative. Developments in high-performance computing, open-source version-controlled software, advanced visualization tools, and functional knowledge of plants make achieving the concept realistic. The interdisciplinary Plants in silico team will take advantage of resources in the National Center for Supercomputing Applications (NCSA) and the Institute for Sustainability, Energy, and Environment (iSEE) — and its academic and research expertise in plant biology, crop sciences, and bioengineering — to build a user-friendly platform for plant scientists around the globe who are working on the food security challenge.

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

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