Semantic Metadata for Scientific Data Access and Management Richard M. Keller, Ph.D. Group Lead for Information Sharing & Integration Intelligent Systems Division NASA Ames Research Center [email protected]http://sciencedesk.arc.nasa.gov/scidesk/ February 17, 2005 ROSES Workshop
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Semantic Metadata for Scientific Data Access and Management Richard M. Keller, Ph.D. Group Lead for Information Sharing & Integration Intelligent Systems.
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Semantic Metadata for Scientific Data Access and Management
Richard M. Keller, Ph.D.Group Lead for Information Sharing & Integration
Intelligent Systems DivisionNASA Ames Research Center
• Nodes: key info resources or organizational structures (describes people, places, measurements, hypotheses)
• date• size• format
Ontology:Specifies the
types of nodes, attributes and
links defined for scientific
investigation
Rules:Add/modify nodes, links & attributes in the network
DNA sequenceimage
document
culture
personsample
photographic image
SEM image
Scientific Data Collection Ontology (partial)
other
experiment
Scientific Information Nodes
project
measurement
site
equipment
camera
gas chromatograph
stub
O2 microsensor
N2 microsensorSEM
O2 concentration
N2 concentration
spectrometer
spectrograph
chromatogram
other
other
micrograph
cultivated-fromcultivated-by
has-genetic-sequence
pictured-in
researcher
lab tech
Benefits of Semantic Metadata Approach
• Semantic context provides a unifying framework for integrating data across data collections
• Sophisticated “semantic search” methods allow retrieval based on semantic relationships among data
• Intuitive data indexing, access, and organization schemes derive from semantic data models
• Formal semantic representation enables automated inference about the data
Challenge
• Semantic metadata approach has been applied to small, PI-maintained data repositories
• Tremendous volume of earth and space science data is stored in huge, curated data repositories maintained by NASA, USGS, ESA, universities, and others.
• How to translate semantic metadata ideas to operate on the scale of large data repositories?
Seeking Collaborators!
SemanticOrganizer System(Mat Sample: Spring-M4-b)
Photo: SprM4b excised
What is ScienceOrganizer?
• A Web-based collaborative knowledge management tool for distributed teams of scientific investigators
• Facilitates information sharing, integration, correlation• A project information repository / digital library: users upload/download heterogeneous project information products -- images, datasets, documents, and various types of scientific records (describing samples, field sites, measurements, instruments, etc.)
• Features cross-linkage: enables rapid access to interrelated information; permits linking data and observations to scientific hypotheses
• Supports inference capabilities: permits formal reasoning about the repository contents
• A “project archive” system: tracks history of project team’s fieldwork, labwork, and associated data collection activities
ScienceOrganizer Users
• ARC Microbial Ecosystems Group: field & lab science, experiments, data analysis.
• NAI Ecogenomics Focus Group: cross-discipline collaboration, data analysis.
• ARC Electron Microscopy Lab: electron microscopy image archiving, sample cataloging.
• MARTE Mission: analog Mars drilling mission, support for remote science data acquisition, storage, and access
• JSC Astrobiology Institute for the Study of Biomarkers: electron microscopy image archive, sample collection, cataloging, and storage; support for education & outreach.
• NIH/NASA Malaria Control Study: African malaria study - data collection and archiving.