Giriprakash Palanisamy

A Linked Science investigation: enhancing climate change data discovery with semantic technologies

Linked Science is the practice of inter-connecting scientific assets by publishing, sharing and linking scientific data and processes in end-to-end loosely coupled workflows that allow the sharing and re-use of scientific data. Much of this data does not live in the cloud or on the Web, but rather in multi-institutional data centers that provide tools and add value through quality assurance, validation, curation, dissemination, and analysis of the data. In this paper, we make the case for the use of scientific scenarios in Linked Science. We propose a scenario in river-channel transport that requires biogeochemical experimental data and global climate-simulation model data from many sources. We focus on the use of ontologies—formal machine-readable descriptions of the domain—to facilitate search and discovery of this data. Mercury, developed at Oak Ridge National Laboratory, is a tool for distributed metadata harvesting, search and retrieval. Mercury currently provides uniform access to more than 100,000 metadata records; 30,000 scientists use it each month. We augmented search in Mercury with ontologies, such as the ontologies in the Semantic Web for Earth and Environmental Terminology (SWEET) collection by prototyping a component that provides access to the ontology terms from Mercury. We evaluate the coverage of SWEET for the ORNL Distributed Active Archive Center (ORNL DAAC).

OME: Tool for generating and managing metadata to handle BigData

The next-generation On-line Metadata Editor (OME) is an easy-to-use tool to help document scientific data in a well-structured popular metadata format. In this paper, we discuss the newest tool that Oak Ridge National Laboratory has developed to input, edit, and manage metadata and how it is helping data intensive science centers across many federal agencies to prepare metadata and to make their BigData discoverable.

An open source framework to add spatial extent and geospatial visibility to Big Data

Advancement in the field of computing and remote handheld devices has made the process of collecting geospatial data easy. Most of the time researchers and scientists have easy access to these data as well. However, the process of extracting and processing a large volume of data from several sources can be very time consuming and difficult. In most cases scientists rely on expensive proprietary software [1]. This paper discusses how Computational Scientists at Oak Ridge National Laboratory are extracting, normalizing, and processing millions of geospatial data points from multiple data sources and integrating them into a common data format which helps user to find and access these data using a flexible visualization-based user interface.