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Data Observatory Web Page Content
The RENCI Data Observatory program integrates domain science, data science, and advanced cyberinfrastructure to develop and implement a science as a service platform addressing researchers critical need for a bridge to advanced cyberinfrastructure capabilities. These capabilities include access to disparate data from a variety of sources, access to compute resources, and environments to compile and execute models.
The RENCI Data Observatory also constitutes a research activity in and of itself whereby the interactions between properties of data, particularly large data sets, and cyberinfrastructure may be investigated systematically.
The projects included in the Data Observatory program address various aspects of the vision in a variety of domains.
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Risk Analytics Discovery Environment (RADE) Team: W. Christopher Lenhardt (RENCI), Brian Blanton (RENCI), Charles Schmitt (RENCI), Casey Deitrich (NCSU), Jean-Claude Thill (UNCC)
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Natural language processing (NLP) for Regional, Micro-level Economic Impacts (Charles Schmitt)
Recognize business events relevant to micro economics and risk
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Predicting future community housing behavior values based on sea level rise scenarios and hazards assessments (Casey Dietrich)
The purpose of this research is to develop a toolbox which can be used by coastal managers and researchers to explore different scenarios of sea level rise and storminess or study areas using an existing coupled coast – town modeling framework. More info.
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Vector-borne Disease (Jean-Claude Thill)
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The DSai and RADE projects are part of the North Carolina Data Science and Analytics Initiative Data Science Initiative, hosted by UNC-Charlotte and funded through a grant from the UNC General Administration Research Opportunities Initiative.
Official title of the project: Biomedical Data Translator Technical Feasibility Assessment and Architecture Design PIs, Senior personnel: PI - Stan Ahalt; Co-PI - Alexander Tropsha
The Biomedical Data Translator Technical Feasibility Assessment and Architecture Design project will address the significant challenges in translational research by permitting biomedical researchers, and clinical scientists to query, exploit, and contribute to a federated collection of complex and highly disparate data with medical importance. This data federation will enable interrogators to pose questions about human health and disease that could not heretofore have been imagined. We anticipate that these analyses will increase, in ways that are paradigm shifting, our understanding of new treatments and treatment targets, mechanisms of disease and disease transmission, environmental triggers, opportunities for drug repurposing, molecular candidates, and systems biology pathways.
Personnel: Ray Idaszak, Michael Stealey, and Hong Yi
To help make the NWM forecasts more accessible to scientists and the public, NWM data is ingested and stored daily on a secure server at RENCI. RENCI mirrors the NWM output in NOAA’s National Operational Model Archive and Distributed System (NOMADS) and shares the data with the broad water science community through HydroShare. Launched in 2012, HydroShare is an open source water science research and discovery environment that allows scientists and the public to access, visualize, and manipulate a broad set of hydrologic data types and models, and connect with data and models published by others.
(RENCI Hydroshare/NWM News Item)[http://renci.org/news/hydroshare-extends-the-reach-of-breakthrough-national-water-model/]
(RENCI Hydroshare/NWM Technical Information)[https://www.hydroshare.org/resource/6041109de34f4ab2b8c82b6982d71311/]
Mouse Brain Visualization
Our lab is performing cutting-edge secondary data analyses on the large, highly controlled neuroimaging data set from the Human Connectome Project (HCP). Specifically, we seek to examine the impact of obesity on aspects of connectivity in the brain capitalizing on HCP’s unique and large sample, as well as cutting edge graph theory techniques. RENCI supports storage and high throughput analyses of these data, that would not be feasible locally.
The present investigation would be one of the first to experimentally manipulate dopaminergic functioning via acute bromocriptine administration and assess neural, behavioral and perceptual hedonic responses to food while simultaneously evaluating homeostatic satiation signaling in those at high risk for development of diabetes. This study will also provide vital knowledge about the effectiveness of the D2 agonist bromocriptine by determining the impact of the DRD2 TaqIA A1 genetic polymorphism on drug response, thereby informing the basis for personalized medicine in this population. This study is funded by the American Diabetes Association. RENCI supports storage and high throughput analyses of our human neuroimaging (functional and structural MRI) data from this investigation.
There is much to learn about what foods women eat during this exciting time and the reasons for these choices. We would like to understand better what motivates mothers' eating behaviors in order to develop programs to help women achieve optimal nutrition for a healthy pregnancy. This study is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the National Institutes of Health. RENCI supports storage and high throughput analyses of our human neuroimaging (functional and structural MRI) data of the “Sugar Moms” sub study.