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This talk will describe how science is changing as a result of the vast amounts of data we are collecting from gene sequencers to telescopes and supercomputers. This “Fourth Paradigm of Science,” predicted by Jim Gray, is moving at full speed, and is transforming one scientific area after another. The talk will present various examples on the similarities of the emerging new challenges and how Jim Gray’s vision is realized by the scientific community. Scientists are increasingly limited by their ability to analyze the large amounts of complex data available. These data sets are generated not only by instruments but also computational experiments; the sizes of the largest numerical simulations are on par with data collected by instruments, crossing the petabyte threshold this year. The importance of large synthetic data sets is increasingly important, as scientists compare their experiments to reference simulations. All disciplines need a new “instrument for data” that can deal not only with large data sets but the cross product of large and diverse data sets. There are several multi-faceted challenges related to this conversion, e.g., how to move, visualize, analyze, and in general interact with petabytes of data.
The HUBzero platform offers unique features to support collaboration among researchers, among them the deployment of computational tools and the dissemination of scientific resources. A fundamental need for collaborative research projects is the collection, sharing and exploration of data. This presentation describes the Hub-based "data technologies" which are designed to provide interactive, web-based data sharing and have been used to create more than 40 medical and scientific research databases across 10 Hubs. We will present examples that demonstrate how Hub databases connect and serve communities across the globe – with data contribution facilities to store and preserve valuable research data, and powerful feature-rich data exploration facilities to search, navigate, drill-down, link, filter, export and analyze data. Research communities are well served by HUBzero, which unites data, tools and collaborative technologies on a single web-based platform.
Elucidating disease and dysfunction requires understanding how genotypic variation relates to phenotypic outcomes. However, data integration and retrieval are key challenges as phenotype data is largely unstructured and is encoded in a variety of formats. In addition, we only know the functional consequences of mutation for less than 40% of the human coding genome. Use of semantically structured phenotype data from model organisms so as to be comparable to human can supplement the human data to aid disease discovery. The ontologies act not only as data standards within and across species, but provide the connection of anatomical form to phenotypic outcomes — thereby enabling deep anatomical analysis to persevere, grow, and shed new light on how biological systems function across scale. The use of cross-species anatomy and phenotype ontologies can be combined with exome analysis to support disease diagnosis. The integrated data across models provisions for the development of methods to ensure quality structured phenotyping for maximal analytic utility, as well as garnering an understanding for how different organisms provide different phenotypic insights into gene function. Semantically capturing interactions with environmental perturbants (such as exposures or drug treatments) and the change in phenotypic outcomes over time is challenging, but is increasingly relevant for rare disease, cancer, and other more common disorders, as we seek to stratify patients and better support precision medicine.
Dr. Hall will begin this talk by discussing cutting-edge conceptual and empirical work that highlight trends in team science and offer insights into what makes for successful collaboration in science. She will highlight a conceptual model for transdisciplinary research, as well as empirical findings produced by the National Cancer Institute’s SciTS (Science of Team Science) team that provide guidelines and strategies for success in team science. She will then highlight the NCI’s Team Science Toolkit website (www.teamsciencetoolkit.cancer.gov), a “one-stop-shop” for resources to help lead, manage, evaluate, facilitate, or support team science. She will feature key resources available through the Toolkit that can be used by investigators, academic institutions, and funders to maximize the success of team science. Finally, Dr. Hall will discuss needed future directions to build the SciTS evidence base, and discuss current projects of the NCI SciTS team in support of these goals, including development of a systems map of the factors influencing success in team science.
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