The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term.The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

The Matchmaker Exchange API allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant-disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enables matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.

Coral reefs are increasingly affected by hightemperature stress events and associated bleaching. Monitoring and predicting these events have largely utilized sea surface temperature data, due to the convenience of using large-scale remotely sensed satellite measurements. However, coral bleaching has been observed to vary in severity throughout the water column, and variations in coral thermal stress across depths have not yet been well investigated. In this study, in situ water temperature data from 1999 to 2011 from three depths were used to calculate thermal stress on a coral reef in Bahia Almirante, Bocas del Toro, Panama, which was compared to satellite surface temperature data and thermal stress calculations for the same area and time period from the National Oceanic and Atmospheric Administration Coral Reef Watch Satellite Bleaching Alert system. The results show similar total cumulative annual thermal stress for both the surface and depth-stratified data, but with a striking difference in the distribution of that stress among the depth strata during different high-temperature events, with the greatest thermal stress unusually recorded at the deepest measured depth during the most severe bleaching event in 2005. Temperature records indicate that a strong density-driven temperature inversion may have formed in this location in that year, contributing to the persistence and intensity of bleaching disturbance at depth. These results indicate that depth may not provide a stress refuge from high water temperature events in some situations, and in this case, the water properties at depth appear to have contributed to greater coral bleaching at depth compared to near-surface locations. This case study demonstrates the importance of incorporating depth-stratified temperature monitoring and small-scale oceanographic and hydrologic data for understanding and predicting local reef responses to elevated water temperature events.