# Presentation: 089645

## EDRN and JPL Activities in Informatics

- Jet Propulsion Laboratory
- California Institute of Technology
- National Aeronautics and Space Administration
- Pasadena, CA, USA

**Dan Crichton**
**PI, EDRN Informatics**
**Program Manager, Planetary Data System Engineering**
**Principal Computer Scientist**
**Solar System Exploration Directorate**
**Jet Propulsion Laboratory**
- March 2006

## NASA/NIH Collaboration

- NASA/JPL has captured science data from planetary, earth and astrophysics missions for years.  It has (both good and bad) experiences in constructing national systems.
- Related experience in planetary science - Planetary Data System
- A national science for management of all planetary science data
- It has a core grid infrastructure for connecting scientific institutions.  It currently connects 9 planetary science repositories across the country (consisting of 10 TBs of data). Will be 100 TB in 2007.
- It has a core ontology model with > 1200 data elements
- It has a pipeline for processing and putting data into the archive
- It has a peer-review process for creating high quality science data
- Scientists want to be able to build integrated models to simulate and validate science and engineering
- Other similar efforts: EOSDIS, NVO, Earth System Grid, SPASE, ECHO, ...
- Collaborations with other agencies (DOE, DOD, etc)
- EDRN Informatics is joint collaboration between Fred Hutchinson-DMCC, JPL and NCI

## The Object Oriented Data Technology Framework

- Started in 1998 as a research and development task funded at JPL by the Office of Space Science to address
- Application of Information Technology to Space Science
- Provide an infrastructure for distributed data management
- Research methods for interoperability, knowledge management and knowledge discovery
- Develop software frameworks for data management to reuse software, manage risk, reduce cost and leverage IT experience
- OODT Initial focus
- Data archiving – Manage heterogeneous data products and resources in a distributed, metadata-driven environment
- Data location and discovery – Locate data products across multiple archives, catalogs and data systems
- Data retrieval – Retrieve diverse data products from distributed data sources and integrate
- Runner-up NASA Software of the Year, 2003
- Used in planetary, earth and biomedical sciences
- Java framework available via Open source at http://www.openchannelsoftware.com

## OODT Architectural Principles*

- Separate the technology and the information architecture
- Encapsulate the messaging layer to support different messaging implementations
- Encapsulate individual data systems to hide uniqueness
- Provide data system location independence
- Require that communication between distributed systems use metadata
- Define a model for describing systems and their resources
- Provide scalability in linking both number of nodes and size of data sets
- Allow systems using different data dictionaries and metadata implementations to be integrated
- Leverage open source

- * Crichton, D, Hughes, J. S, Hyon, J, Kelly, S.  “Science Search and Retrieval using XML”,
- Proceedings of the *2**nd** National Conference on Scientific and Technical Data*, National Academy of
- Science, Washington DC, 2000.

## OODT Data Architecture Focus

- Decomposition of _data_ within a software system to its logical components and interactions
- Components:  Data Elements, Data Dictionary, Data Models of individual data sources
- Interactions:  Mappings between Data Dictionary to Data Models, Data Element structural comparison
- Some standards currently exist for data architecture
- ISO: ISO-11179 Standardization and Specification of Data Elements
- Dublin Core Metadata Initiative:  Dublin Core Data Elements to describe *any electronic resource*
- Specifications for the Data Architecture
- Common XML schema for managing information about data resources
- Common XML schema for messaging between distributed services
- Methods for integrating existing domain models within architecture

## Distributed Architecture

**3. Repositories **for
-     storing and retrieving
-     many types of data

**1. Science data tools and applications **use “APIs” to connect to a virtual data repository

***Visualization Tools***

***Analysis Tools***

**OODT**
**Reusable**
**Data**
**Grid**
**Framework**

**Mission**
**Data**
**Repositories**

**OODT**
**API**

**2. Middleware **creates the
- data grid infrastructure connecting distributed heterogeneous systems and data

**Biomedical**
**Data**
**Repositories**

**Engineering**
**Data **
**Repositories**

***Web Search Tools***

**OODT**
**API**

**OODT**
**API**

**Notes:**
This is a view of the distributed OODT architecture. You see OODT as the reusable middleware, publishing APIs, and providing the unification of distributed repositories.  OODT is not about building client tools, but rather publishing common interfaces so that other developers can build the tools.  This is proving to be highly leveraged in that a community of tool developers is emerging that are creating tools that leverage the virtual data enterprise.

## Characteristics of the science data

- High-quality, peer-reviewed archive of data from Solar System Exploration missions
- Stored for long-term viability
- Described by metadata
- Distributed either online or on physical media
- Defined by a set of data standards
- Planetary Science Data Dictionary
- Planetary Community Model
- Standard Structural Specifications for products, data sets, volumes, etc supported by the model
*Very, very diverse, but defined by a common conceptual model*

## 2001 Mars Odyssey: A paradigm change

- Pre-Oct 2002, no unified view across distributed operational planetary science data repositories
- Science data distributed across the country
- Science data distributed on physical media
- Planetary data archive increasing from 4 TBs in 2001 to 300 TBs in 2007
- Traditional distribution infeasible due to cost and system constraints
- Mars Odyssey could not be distributed using traditional method
- Current work with the *OODT Data Grid Framework* has provided the technology for NASA’s planetary data management infrastructure to
- Support online distribution of science data to planetary scientists (up to 500 MB products)
- Enable interoperability between nine institutions
- Support real-time access to data products
- Provided uniform software interfaces to all Mars Odyssey data allowing scientists and developers to link in their own tools
- Operational October 1, 2002

*2001 Mars Odyssey*

## OODT Models

*Resource Metadata Model*

*Request/Response Model*

**Based on ISO/IEC 11179**

**Based on Dublin Core**

## OODT Software Components

*Profile Server* – A server-based registry that is able to either serve local XML profiles or plug-into an existing catalog.  This component provides resource discovery.
*Product Server* – A server component that plugs into existing repositories and serves products.  This includes translation serves, etc
*Catalog and Archive Server* – Transaction-based server that catalogs and archives products providing profile and product servers for discovery and distribution
*Query Server* – Provides query management across distributed services to enable discovery.

## Profile Servers

- A profile is a set of resource definitions implemented in XML for data products residing in one or more distributed systems
- Profile servers are distributed servers that manage XML profile definitions as a set of cooperating registries
- Profile servers communicate via XML-over-standard middleware
- Developed Java classes that map XML profiles to a Java object

- Profile Distributed Node Architecture

## Product Server Architecture

**Distributed**
**Product**
**Servers**

- HTTP, IIOP, SOAP,
- Java, C++
**  ****APIs**

- Data Source Interface
- For
- Dynamically Loaded Query Handlers

- Java Server Framework

- Database Access via JDBC

- Java Server Framework

- Application/File System Access

- Data Source Interface
- For
- Dynamically Loaded Query Handlers

- HTTP, IIOP, SOAP
- Java, C++
**  ****APIs**

**Distributed**
**Data**
**Repositories**

## EDRN Informatics Tools and Initiatives

- EDRN Resource Network Exchange (ERNE)
- EDRN Catalog and Archive Service (eCAS)
- Validation Study Information Management System (VSIMS)
- Biomarker Database
- Public Portal for EDRN scientific and program information dissemination
- Integrated knowledge base
- Development of a high level information model/ontology
- Common Data Elements (CDEs)
- CDE Tools

## EDRN Knowledge Environment

**Science Warehouse**

**CDE Repository**

**ERNE**

**VSIMS**

**Participant DB**

**Protocol DB**

**Public Portal**

**Distributed Specimen**
**Databases**

**EDRN science data results **
**(local, distributed and varying **
**degrees of validation)**

**Descriptions of biomarkers**
**and their use (protocol_id)**

**Descriptions of EDRN studies**
**Participants**
**Specimen tracking, etc**

**Protocols and their**
**descriptions**

**Data elements and their descriptions**

**BIOINFORMATICS**
**TOOLS**

**EDRN science data results**

**(protocol_id,**
**participant_id)**

**(protocol_id,**
**participant_id)**

**(protocol_id,**
**participant_id)**

**Biomarker_DB**

**(protocol_id)**

**Participants and their**
**characteristics**

**EDRN Knowledge Environment**

## EDRN Informatics Goals

- Develop a knowledge system that **links** together EDRN data assets into a virtual data system
- Establish an EDRN bioinformatics program that promotes the use of a **common informatics infrastructure** by EDRN sites.
- Provide an infrastructure for **capturing** and distributing EDRN validation study results
- Adopt, adapt and define data and software **standards** for EDRN informatics systems
**Collaborate** with both EDRN and non-EDRN sites on informatics.
- Develop a public portal that provides **information dissemination** about EDRN programs and progress.
- Enable tools that support **scientific inquiry** both within and across databases and data sets.

## ERNE Operational Informatics Infrastructure

## Deployed ERNE Infrastructure

**Deployed ERNE Infrastructure**

## Specimen Types in ERNE

## EDRN Catalog and Archive System (eCAS)

***Instrument***
*** ******Operations***

***Science***
***Data ******Processing***

***Data***
***Distribution (EDRN Public Portal)***

***EDRN Bioinformatics Tools***

***Instrument***

***eCAS - EDRN ***
***Biorepository***

***External***
***Sci Community***

***EDRN Researchers***

***Laboratory Biorepository***

***Analysis***
***Team***

- Local Laboratory Science Data System

- Publish Data Sets

## Science Data Warehousing

- A national database for capturing and distributing EDRN published science data
- Can be configured to capture different EDRN data sets
- Based on the EDRN CDEs
- Integrates into an EDRN Public Portal
- Provides secure transfer of science data
- Enables searching based on the EDRN CDEs
- Provides versioning of data sets
- Can store both raw and processed data

## Biomarker Database

- Definition
- Biomarker Database is a structured, electronic data management system within EDRN.
- A collection of biomarker study information will be organized in such a way that a computer program can quickly select desired pieces of data.
- Purpose
- To aid in the capture and distribution of high quality information biomarkers.
- To establish common standards for sharing electronic biomarker data.
- To provide a means for effective and easy utilization of data.
- To improve the ability of the EDRN to collaborate on multi-function, multi-institutional research on a continuous basis.
- To aid in the reporting of research progress at regular intervals.

## EDRN Acknowledgements

- Data Management and Coordinating Center, Fred Hutchinson Cancer Research Center
- H. Lee Moffitt Cancer Center
- University of Texas, San Antonio
- Creighton University
- University of Colorado
- University of Pittsburgh
- University of Michigan/Dartmouth University (Great Lakes New England Consortium)
- Brigham and Womens
- MD Anderson

- University of California, San Francisco,
- Beth Israel
- Center for Disease Control
- Duke University
- Fox Chase
- Johns Hopkins
- New York University
- Cancer Biomarkers Group, NCI
- NCI Center for Bioinformatics
- NASA Jet Propulsion Laboratory