Ranjeet Devarakonda

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).

Drupal: Collaborative framework for science research

In this paper we present how research projects at Oak Ridge National Laboratory - Distributed Active Archive Center (ORNL - DAAC) are using the Drupal content management system (CMS) for creating collaborative environments for scientists. We will discuss how scientific data and documents are stored and represented in Drupal and how they can be shared with scientists in other organizations.

Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA

This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through late 2016. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASAs Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASAs Soil Moisture Active Passive - SMAP) scales.

Next-gen tools for big scientific data: ARM data center example

The Atmospheric Radiation Measurement (ARM) Climate Research Facility (www.arm.gov) provides atmospheric observations from diverse climatic regimes around the world. Currently, ARM archives over 22 million user assessable data files, primarily stored in NetCDF file format, with total data volumes close to one Petabyte. In this paper, we will discuss how ARM is currently storing, distributing, cataloging and visualizing such large volumes of multi-dimensional climate observations and model data and also describe their future plan.

Accessing and distributing large volumes of NetCDF data

In this paper, we will discuss how NASAs Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) is distributing large volumes of ‘structured’ data using Daily Surface Weather Data and a corresponding Climatological Summaries Dataset (Daymet) as an example.

Preparing, storing, and distributing multi-dimensional scientific data

Data of all sizes, generated by simulation and observation (i.e., instruments and satellites) activities, should be collected, stored, and organized, along with associated tools and research results, so that they are easily discoverable and accessible. Most observational data capture conditions at an exact point in time and are thus not reproducible, therefore it is imperative that initial data be captured and stored correctly the first time. In this paper, we will discuss how NASAs Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) is preparing, storing, and distributing large volumes of multi-dimensional scientific data using Daily Surface Weather Data and a corresponding Climatological Summaries Dataset (Daymet) as an example.

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.

Semantic search integration to climate data

In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data.