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Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.

Summary Peri-ictal autonomic dysregulation is best studied using a “polygraphic” approach (EEG, 3-channel EKG, pulse Oximetry, respiration and continuous non-invasive blood pressure [BP]) and may help elucidate agonal pathophysiological mechanisms leading to Sudden Unexpected Death in Epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the commonest seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias and changes in skin conductance. Post-ictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP and PGES has been found to correlate with post GTCS autonomic dysregulation in some patients. Here, we describe a patient with a GTCS in whom polygraphic measurements, including continuous non-invasive blood pressure recordings, were obtained. Significant post-ictal hypotension lasting >60 seconds was found which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon and consequent cerebral hypo-perfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged and irrecoverable hypotension may comprise one potential SUDEP mechanism.

EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.

E d i t o r s : M . B r i a n B l a k e • m b7@ g e o r g e t o w n .e d u M i c h a e l N . H u h n s • h u h n s @ s c .e d u Semantic Provenance for eScienceManaging Provenance information in eScience is metadata that's critical to effectively manage the exponentially increasing volumes of scientific data from industrialscale experiment protocols. Semantic provenance, based on domain-specific provenance ontologies, lets software applications unambiguously interpret data in the correct context. The semantic provenance framework for eScience data comprises expressive provenance information and domain-specific provenance ontologies and applies this information to data management. The authors' "two degrees of separation" approach advocates the creation of high-quality provenance information using specialized services. In contrast to workflow engines generating provenance information as a core functionality, the specialized provenance services are integrated into a scientific workflow on demand. This article describes an implementation of the semantic provenance framework for glycoproteomics.e Science, also known as cyber infrastructure, represents a par adigm shift in scientific research that lets scientists harness Webbased computing and data resources to achieve their objectives faster, more efficiently, and on an industrial scale. Using remote software and experi mental equipment, scientists can not only access but also generate and pro cess data from distributed sources. The resulting data deluge demands computing solutions that can use highquality metadata -specifically, domainspecific provenance infor mation -to automatically interpret, integrate, and process data. Such so lutions bring real value to scientists by answering domainspecific queries effectively to support knowledge dis covery over large volumes of scientific data. But creating provenance infor mation of the requisite quality in the heterogeneous, distributed, and high throughput environment of eScience is a daunting challenge.We argue that incorporating domain knowledge and ontological underpin ning in provenance using expressive domainspecific provenance ontologies is an approach equal to the challenge. This semantic provenance imposes a JULY/AUGUST 2008 47 Semantic Provenance for eScience formally defined domainspecific conceptual view on scientific data (domain semantics), mitigates or eliminates terminological hetero geneity, and enables the use of reasoning tools for knowledge discovery. Furthermore, we de fine a "two degrees of separation" approach for creating semantic provenance using special ized software tools. Unlike many prevalent workflowenginecentric approaches, these tools refer to domainspecific provenance on tologies to create provenance information and are integrated into a scientific workflow on demand.We combine the essential aspects of high quality provenance -characteristics, a repre sentation model, the creation process, and usage -into a single semantic provenance framework. This framework will pave the way for software...

Abstract. Data provenance graphs are form of metadata that can be used to establish a variety of properties of data products that undergo sequences of transformations, typically specified as workflows. Their usefulness for answering user provenance queries is limited, however, unless the graphs are enhanced with domain-specific annotations. In this paper we propose a model and architecture for semantic, domain-aware provenance, and demonstrate its usefulness in answering typical user queries. Furthermore, we discuss the additional benefits and the technical implications of publishing provenance graphs as a form of Linked Data. A prototype implementation of the model is available for data produced by the Taverna workflow system.

Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. RESOURCE PAGE: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

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