Observations Data Model 2.0

Slides:



Advertisements
Similar presentations
Closing the Gap Between Global Environmental Sensing Needs and Cyber Infrastructure Tools Jim Gray Jeff Burch Mark Ellisman Miron Livny David Maidment.
Advertisements

Guy McGarva, EDINA National Data Centre Rajendra Bose, DCC and School of Informatics University of Edinburgh Tuesday 15 May 2007 CLADDIER Project Workshop,
NG-CHC Northern Gulf Coastal Hazards Collaboratory Simulation Experiment Integration Sandra Harper 1, Manil Maskey 1, Sara Graves 1, Sabin Basyal 1, Jian.
Some notes on CyberGIS in hydrology Ilya Zaslavsky Spatial Information Systems Lab San Diego Supercomputer Center UCSD TeraGrid CyberGIS Workshop, February.
ASCR Data Science Centers Infrastructure Demonstration S. Canon, N. Desai, M. Ernst, K. Kleese-Van Dam, G. Shipman, B. Tierney.
HydroServer A Platform for Publishing Space- Time Hydrologic Datasets Support EAR CUAHSI HIS Sharing hydrologic data Jeffery.
ICEWATER: INRA Constellation of Experimental Watersheds Cyberinfrastructure to Support Publication of Water Resources Data Jeffery S. Horsburgh, Utah State.
SONet (Scientific Observations Network) and OBOE (Extensible Observation Ontology): Mark Schildhauer, Director of Computing National Center for Ecological.
ODM2: Developing a Community Information Model and Supporting Software to Extend Interoperability of Sensor and Sample Based Earth Observations Jeffery.
CUAHSI HIS Data Services Project David R. Maidment Director, Center for Research in Water Resources University of Texas at Austin (HIS Project Leader)
Components of an Integrated Environmental Observatory Information System Cyberinfrastructure to Support Publication of Water Resources Data Jeffery S.
Metadata Standards for Sample- Based Observations Kerstin Lehnert EGU General Assembly 2011.
This work was funded by the U.S. National Science Foundation under grant EAR Any opinions, findings and conclusions or recommendations expressed.
HydroServer A Platform for Publishing Space- Time Hydrologic Datasets Support EAR CUAHSI HIS Sharing hydrologic data Jeffery.
Time Series Analyst An Internet Based Application for Viewing and Analyzing Environmental Time Series Jeffery S. Horsburgh Utah State University David.
Development of a Community Hydrologic Information System Jeffery S. Horsburgh Utah State University David G. Tarboton Utah State University.
Introducing the CUAHSI Hydrologic Information System Desktop Application (HydroDesktop) and Open Development Community Jiří Kadlec, Daniel Ames, Teva Velupillai.
Deployment and Evaluation of an Observations Data Model Jeffery S Horsburgh David G Tarboton Ilya Zaslavsky David R. Maidment David Valentine
1 CYBERINFRASTRUCTURE FOR THE GEOSCIENCES Global Earth Observation Grid Workshop, Bangkok, Thailand, March Integration Platform.
SAN DIEGO SUPERCOMPUTER CENTER Developing a CUAHSI HIS Data Node, as part of Cyberinfrastructure for the Hydrologic Sciences David Valentine Ilya Zaslavsky.
Tools for Publishing Environmental Observations on the Internet Justin Berger, Undergraduate Researcher Jeff Horsburgh, Faculty Mentor David Tarboton,
Using HydroServer Organize, Manage, and Publish Your Data Support EAR CUAHSI HIS Sharing hydrologic data Jeffery S. Horsburgh.
Introduction to Geospatial Metadata – FGDC CSDGM National Coastal Data Development Center A division of the National Oceanographic Data Center Please .
What is Physical Geography?. Physical geography- CGF3M  This course examines the main elements of the physical environment (climate, soils, landforms,
HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing David Tarboton, Ray Idaszak, Jeffery Horsburgh, Dan Ames, Jon Goodall, Larry.
About CUAHSI The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) is an organization representing 120+ universities.
Information Requirements for Integrating Spatially Discrete, Feature- Based Earth Observations Jeffery S. Horsburgh Anthony Aufdenkampe, Kerstin Lehnert,
David R. Maidment Center for Research in Water Resources University of Texas at Austin Presented to Geospatial World Forum Rotterdam, the Netherlands |16.
Crossing the Digital Divide Presented by: Fernando R. Salas David Maidment, Enrico Boldrini, Stefano Nativi, Ben Domenico OGC Technical Meeting – Met/Occean.
U.S. Department of the Interior U.S. Geological Survey CDI Data Management Working Group December 12, 2011 Sally Holl, USGS Texas Water Science Center.
Abstract Building an integrated information system for publishing heterogeneous Critical Zone Observatory data Thomas Whitenack 1, Mark Williams 2, David.
Data Interoperability in the Hydrologic Sciences The CUAHSI Hydrologic Information System David Tarboton, David Maidment, Ilya Zaslavsky, Dan Ames, Jon.
Advancing an Information Model for Environmental Observations Jeffery S. Horsburgh Anthony Aufdenkampe, Richard P. Hooper, Kerstin Lehnert, Kim Schreuders,
Imagine a World…. With easy, unlimited access to scientific data from any field Where you can easily plot data of interest and display it any way you want.
1 Data Integration Community of Practice Meeting September 15, 2009 Science Data Integration.
U.S. Department of the Interior U.S. Geological Survey CDI Webinar Sept. 5, 2012 Kevin T. Gallagher and Linda C. Gundersen September 5, 2012 CDI Science.
National Spatial Data Infrastructure The Spatial Information Services Stack Dr Robert Woodcock.
Unit 1 Introduction To Earth Science. Topic 1: Earth Systems As A Science  Earth Science differs from other sciences in that: 1. Earth Science has a.
Metadata Lessons Learned Katy Ginger Digital Learning Sciences University Corporation for Atmospheric Research (UCAR)
CBEO:N Chesapeake Bay Environmental Observatory as a Network Node About CBEO The mission of the CBEO project is development of a Chesapeake Bay Environmental.
EarthCube Building Block for Integrating Discrete and Continuous Data (DisConBB) David Maidment, University of Texas at Austin (Lead PI) Alva Couch, Tufts.
CUAHSI Hydrologic Information System and its role in Hydrologic Observatories Core Team: D. Maidment, J. Helly, P. Kumar, M. Piasecki, R. Hooper Collaborators:
GIS in Water Resources: Lecture 1 In-class and distance learning Geospatial database of hydrologic features GIS and HIS Curved earth and a flat map.
GEON2 and OpenEarth Framework (OEF) Bradley Wallet School of Geology and Geophysics, University of Oklahoma
The Long Tail of Sample-based Data in the Next Decade FROM DARKNESS TO LIGHT Kerstin Lehnert
HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing David Tarboton, Ray Idaszak, Jeffery Horsburgh, Dan Ames, Jon Goodall, Larry.
Critical Zone Observatory Data Discovery Each CZO maintains its own data management system(s) using the data formats it prefers The three CZO’s have a.
GEOSCIENCE NEEDS & CHALLENGES Dogan Seber San Diego Supercomputer Center University of California, San Diego, USA.
Exporting WaterML from the Earth System Modeling Framework Xinqi Wang Louisiana State University NCAR SIParCS Program August 4, 2009.
The CUAHSI Observations Data Model Jeff Horsburgh David Maidment, David Tarboton, Ilya Zaslavsky, Michael Piasecki, Jon Goodall, David Valentine,
HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing David Tarboton, Ray Idaszak, Jeffery Horsburgh, Dan Ames, Jon Goodall, Larry.
CUAHSI HIS: Science Challenges Linking small integrated research sites (
ILYA ZASLAVSKY RAQUEL CALDERON CHRIS CONDIT JEFFREY GRETHE AMARNATH GUPTA BURAK OZYURT THOMAS WHITENACK DAVID VALENTINE ALICE GILIARINI AARON GONG University.
HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing David Tarboton, Ray Idaszak, Jeffery Horsburgh, Dan Ames, Jon Goodall, Larry.
National CZO program (network): Established 2007: Southern Sierra, Boulder Creek (Suzanne Anderson), Susquehanna-Shale.
Transformative Earth Sciences through Data: Neotoma, EarthCube & Flyover Country Simon Goring Assistant Scientist University of Wisconsin - Madison S i.
Lecture 5 Data Model Design Jeffery S. Horsburgh Hydroinformatics Fall 2012 This work was funded by National Science Foundation Grant EPS
Hydroinformatics Lecture 15: HydroServer and HydroServer Lite The CUAHSI HIS is Supported by NSF Grant# EAR CUAHSI HIS Sharing hydrologic data.
Developing a community hydrologic information system David G Tarboton David R. Maidment (PI) Ilya Zaslavsky Michael Piasecki Jon Goodall
The CUAHSI Hydrologic Information System Spatial Data Publication Platform David Tarboton, Jeff Horsburgh, David Maidment, Dan Ames, Jon Goodall, Richard.
Sharing Hydrologic Data with the CUAHSI* Hydrologic Information System
Ilya Zaslavsky Jeffrey Grethe amarnath Gupta burak Ozyurt
The CUAHSI Hydrologic Information System and NHD Plus A Services Oriented Architecture for Water Resources Data David G Tarboton David R. Maidment (PI)
Susquehanna/Shale Hills Critical Zone Observatory
Lecture 8 Database Implementation
CUAHSI HIS Sharing hydrologic data
Susquehanna/Shale Hills Critical Zone Observatory
Bird of Feather Session
Brokering as a Core Element of EarthCube’s Cyberinfrastructure
Presentation transcript:

Observations Data Model 2.0 A community information model for interoperability among feature-based earth observations Jeff Horsburgh, USU. Project PI. Anthony K. Aufdenkampe, Stroud Water Research Center Kerstin Lehnert, IEDA/Columbia Emilio Mayorga, UW-APL Ilya Zaslavsky, SDSC David Valentine, SDSC David Tarboton, USU David Lubinski, UC-Boulder I’ll present a combination of the history behind this effort, our achievements and our vision – and along the way explain how the complexities of multiple disciplines and data types has required a community building and engagement effort that serves as an excellent prototype for EarthCube.

Critical Zone Science Earth's permeable near-surface layer from the tops of the trees to the bottom of actively cycling groundwater. Where rock, soil, water, air, and living organisms interact and shape the Earth's surface. Critical to sustaining the earth’s sustaining services Clean water Productive soil Balanced atmosphere Atmosphere Biosphere Hydrosphere Lithosphere Minutes Decades Millenia Eons Data management needs for critical zone science – seamlessly integrate sensor data logged every few minutes to complex geochemical sample preparation and analysis and sample sharing Hillslope Catchment Watershed

CZO Disciplines Biogeochemistry Biology/Ecology Biology/Molecular Climatology/Meteorolog y Data Management/CyberInfr astructure Engineering/Method Development Geochemistry/Mineralo gy Geology/Chronology Geomorphology Geophysics GIS/Remote Sensing Hydrology Modeling/ Computational Science Outreach/ Education Research Soil Science/Pedology Water Chemistry

CZO Disciplines Big Data Long Tail Data Biogeochemistry Geomorphology Biology/Ecology Geophysics Biology/Molecular GIS/Remote Sensing Climatology/Meteorology Hydrology Modeling/ Computational Science Data Management/CyberIn frastructure Outreach/ Education Research Engineering/Method Development Soil Science/Pedology Geochemistry/ Mineralogy Water Chemistry Geology/Chronology

Geospatial Grids & Vectors CZO Disciplines Big Data Long Tail Data Sample-based Sensor-based Geospatial Grids & Vectors Categorical

ODM2: Common to Most Data Types Sensor Extension Equipment & Lab Extensions Observations Core Feature Model Generic Extension Common Semantics for Earth Observations A common information model is critically important to the effectiveness and interoperability of domain Cyberinfrastructure. CUAHSI HIS EarthChem CZOData IOOS Domain Cyberinfrastructures

ODM2: Common to All Components Database Encoding XML Schema Legend Data and Metadata Transfer Catalog Metadata Catalog Metadata Transfer Metadata Transfer Metadata Harvesting Data Discovery Information Model It forms the conceptual foundation for each component of a data system. In practice, however, information models for each component are often arbitrarily different, limiting capabilities. Data Storage Data Delivery Data Server Clients Data Transfer

ODM2: Additional Goals Driven by Community & Use Cases: 3 workshops + ~12 data models + much feedback use cases: CZOData, Little Bear River, PetDB, IOOS Balance between general vs. understandable External unique identifiers, vocabularies & taxonomies Rich Specimen, Site & other Sampling Features Granular Methods, Data Quality & Equipment Dataset publishing & archiving via: Result “packages”, Versions, Citations, Provenance Strong Annotations & general extensibility

ODM2Core Showing Entity Relationship Diagrams, but Class-Object Model is equally important and we’re actively developing an Object Relation Map

ODM2Core At the very center of ODM2 is the concept of Observations; taken from OGC O&M. We consider ODM2 a profile of O&M. “An Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, obtained using a specified procedure” Use cases pointed out the need to separate Action from Results, and to allow a single Action on many SamplingFeatures.

ODM2SamplingFeatures Relationships between “Specimens” and the “Site” at which they were collected are captured in “FeatureParents”, which may also include other feature types

ODM2Results

ODM2ExternalIdentifiers

ODM2Provenance

ODM2Annotations

ODM2Equipment

ODM2DataQuality

ODM2LabAnalyses

ODM2Sensors

NSF Scientific Software Integration BiG CZ SSI project (2014-2015): The community-driven BiG CZ software system for integration and analysis of bio- and geoscience data in the critical zone Community Engagement in Software Design through co-design, training & testing workshops. BiG CZ Portal web application for high-performance map-based discovery, visualization, access & publication of data on critical zone structure & function BiG CZ Toolbox to enable cyber-savvy CZ scientists & data managers to manage and publish the data they produce through a single scientist-focused toolkit BiG CZ Central software stack to bridge data systems developed for multiple critical zone domains Required all software developed be Open Source, and required us to name the specific licenses that we will use.

Thank You Funded by the National Science Foundation EAR 1224638 ACI 1339834 ODM2 is on GitHUB: https://github.com/UCHIC/ODM2

ODM2: Object-Relation Map Our project objectives requires that we create both object models and relationtional models

What can we do with ODM2? (that we couldn’t do before) Add multiple comments/annotations to any entity Represent Actions and sequences of Actions that lead to observation Results More granularly represent people and organizations Store information about Actions that do not have Results

What can we do with ODM2? (that we couldn’t do before) Separate Results from ResultValues – enables multiple ResultTypes Move DataValues out of the Core – better facilitates cataloging Add taxonomic classifiers to Results, adding an additional dimension to observations Create relationships among Results and store provenance Group Results into Datasets

What can we do with ODM2? (that we couldn’t do before) Store information about the equipment used to create observations Add extension properties to any record in any entity Link many entities to external identifier systems Support SamplingFeatures of multiple types - Sites and Specimens, among others Not limited to a single spatial offset Not Limited to a single qualifier

Observation Data Model 2.0 NSF funded project: PI. Jeff Horsburgh “Developing a Community Information Model and Supporting Software to Extend Interoperability of Sensor and Sample Based Earth Observations” To achieve interoperability between IEDA, EarthCHEM, CUAHSI HIS, and other data systems Better support for samples and unique identifiers (IGSN/SESAR) Extensibility to table attributes Better annotation and provenance Enable integrated web service based publication of a broader class of CZO data

ODM2 Functional Use Cases Information Model (All) Storage Encoding (USU/LDEO) Catalog Encoding (SDSC) Web Service Interface (UW) Archival Encoding (USU) XML Schema Encoding

Future Directions for CZO Science Report prepared by CZO community, Dec. 2010 Develop a unifying theoretical framework of CZ evolution; Develop coupled systems models to explore how CZ services respond to anthropogenic, climatic, and tectonic forcings; Develop four dimensional data sets that document differing CZ geologic and climatic settings, inform our theoretical framework, constrain our conceptual and coupled systems models, test model-generated hypotheses. Report Prepared by the CZO Community December 29, 2010

EarthCube Critical Zone Domain Workshop Engaging the Critical Zone community to bridge long tail science with big data Convened by A.K. Aufdenkampe, C.J. Duffy, G.E. Tucker Univ. of Delaware: Jan. 21-23, 2013 Organizing Committee: Kerstin Lehnert, IEDA/Columbia. Ilya Zaslavsky, SDSC. David Tarboton, USU Jeff Horsburgh, USU. Emilio Mayorga, UW-APL James Syvitski, CSDMS. Susan Brantley, PSU & SH-CZO. Susan Gill, SWRC.

103 Participants from 16 Disciplines Biogeochemistry (30) Biology/Ecology (15) Biology/Molecular (3) Climatology/ Meteorology (15) Data Management/CyberInfra structure (46) Engineering/Method Development (8) Geochemistry/Mineralog y (13) Geology/Chronology (14) Geomorphology (15) Geophysics (8) GIS/Remote Sensing (31) Hydrology (46) Modeling/ Computational Science (36) Outreach/ Education Research (7) Soil Science/Pedology (16) Water Chemistry (14) Early-Career (28)