Presentation is loading. Please wait.

Presentation is loading. Please wait.

Observations Data Model 2.0

Similar presentations


Presentation on theme: "Observations Data Model 2.0"— Presentation transcript:

1 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.

2 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

3 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

4 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

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

6 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

7 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

8 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

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

10 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.

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

12 ODM2Results

13 ODM2ExternalIdentifiers

14 ODM2Provenance

15 ODM2Annotations

16 ODM2Equipment

17 ODM2DataQuality

18 ODM2LabAnalyses

19 ODM2Sensors

20 NSF Scientific Software Integration
BiG CZ SSI project ( ): 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.

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

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

23 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

24 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

25 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

26 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

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

28 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

29 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 , 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.

30 103 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)


Download ppt "Observations Data Model 2.0"

Similar presentations


Ads by Google