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Transcript of Observations Data Model 2.0 Jeff Horsburgh, USU. Project PI. Anthony K. Aufdenkampe, Stroud Water...
![Page 1: Observations Data Model 2.0 Jeff Horsburgh, USU. Project PI. Anthony K. Aufdenkampe, Stroud Water Research Center Kerstin Lehnert, IEDA/Columbia Emilio.](https://reader035.fdocuments.net/reader035/viewer/2022062407/56649c725503460f94923413/html5/thumbnails/1.jpg)
Observations Data Model 2.0
Jeff Horsburgh, USU. Project PI.Anthony K. Aufdenkampe, Stroud Water Research Center
Kerstin Lehnert, IEDA/ColumbiaEmilio Mayorga, UW-APLIlya Zaslavsky, SDSCDavid Valentine, SDSCDavid Tarboton, USUDavid Lubinski, UC-Boulder
A community information model for interoperability among
feature-based earth observations
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Critical Zone Science
Atmosphere
Biosphere
Hydrosphere
Lithosphere
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
Hillslope Catchment Watershed
Minutes
Decades
Millenia
Eons
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CZO Disciplines
• Biogeochemistry• Biology/Ecology• Biology/Molecular• Climatology/
Meteorology• Data
Management/CyberInfrastructure
• Engineering/Method Development
• Geochemistry/Mineralogy
• Geology/Chronology
• Geomorphology• Geophysics• GIS/Remote Sensing• Hydrology• Modeling/
Computational Science• Outreach/
Education Research• Soil Science/Pedology• Water Chemistry
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CZO DisciplinesBig Data Long Tail Data
Biogeochemistry
Biology/Ecology
Biology/Molecular
Climatology/Meteorology
Data Management/CyberInfrastructureEngineering/Method Development
Geochemistry/Mineralogy
Geology/Chronology
Geomorphology
Geophysics
GIS/Remote Sensing
Hydrology
Modeling/Computational Science
Outreach/Education Research
Soil Science/Pedology
Water Chemistry
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CZO DisciplinesBig Data Long Tail Data
Sample-based
Sensor-based
Geospatial Grids & Vectors
Categorical
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ObservationsCore
SensorExtension
Domain Cyberinfrastructures
CUAHSIHIS
EarthChem CZOData IOOS
FeatureModel
Equipment & LabExtensions
GenericExtension
Common Semantics for Earth Observations
ODM2: Common to Most Data Types
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Catalog
Data Server Clients
MetadataCatalog
Data Storage
Met
adat
a Har
vest
ing
Data Discovery
Data Delivery
MetadataTransfer
MetadataTransfer
DataTransfer
DatabaseEncoding
XML SchemaEncoding
Legend
Data and Metadata Transfer
Information Model
ODM2: Common to All Components
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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
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ODM2Core
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ODM2Core
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ODM2SamplingFeatures
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ODM2Results
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ODM2ExternalIdentifiers
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ODM2Provenance
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ODM2Annotations
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ODM2Equipment
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ODM2DataQuality
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ODM2LabAnalyses
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ODM2Sensors
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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
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Thank You
Funded by the National Science Foundation
EAR 1224638EAR 1332257ACI 1339834
ODM2 is on GitHUB: https://github.com/UCHIC/ODM2
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ODM2: Object-Relation Map
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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
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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
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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
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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
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Information Model(All)
StorageEncoding
(USU/LDEO)
CatalogEncoding(SDSC)
Web Service Interface
(UW)
Archival Encoding
(USU)
XML Schema Encoding(SDSC)
ODM2 Functional Use Cases
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Future Directions for CZO Science
• 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 CZO community, Dec. 2010
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EarthCube Critical Zone Domain Workshop
Engaging the Critical Zone community to bridge long tail science with big data
Organizing Committee:
Kerstin Lehnert, IEDA/Columbia.Ilya Zaslavsky, SDSC.David Tarboton, USUJeff Horsburgh, USU.Emilio Mayorga, UW-APL
James Syvitski, CSDMS.Susan Brantley, PSU & SH-CZO.Susan Gill, SWRC.
Convened by A.K. Aufdenkampe, C.J. Duffy, G.E. TuckerUniv. of Delaware: Jan. 21-23, 2013
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103 Participants from 16 Disciplines
• Biogeochemistry (30)• Biology/Ecology (15)• Biology/Molecular (3)• Climatology/
Meteorology (15)• Data
Management/CyberInfrastructure (46)
• Engineering/Method Development (8)
• Geochemistry/Mineralogy (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)