Data Management for CENS

1
Data Management for CENS

• Stasa Milojevic
• Information Studies
• UCLA

2
CENS Data

• CENS will generate massive amounts of
  heterogeneous scientific and technical data from
  the sensors.
• The data need to be useful for CENS researchers
• Real time
• Archived
• The data also need to be useful for other
  researchers in those problem domains (larger
  community).

3
Data Management Goals

• Data Metadata Share with community

- ltdatasetgt   ltalternateIdentifiergtPLT-GCE
M-0311b.1.0lt/alternateIdentifiergt   lttitlegtFall
2003 plant monitoring survey -- biomass
calculated from shoot height and flowering status
of plants in permanent plots at GCE sampling
sites 1-10lt/titlegt - ltcreatorgt  
ltorganizationNamegtGeorgia Coastal Ecosystems LTER
Projectlt/organizationNamegt - ltaddressgt  
ltdeliveryPointgtDept. of Marine Scienceslt/deliveryP
ointgt   ltdeliveryPointgtUniversity of
Georgialt/deliveryPointgt   ltcitygtAthenslt/citygt  
ltadministrativeAreagtGeorgialt/administrativeAreagt
  ltpostalCodegt30602-3636lt/postalCodegt  
ltcountrygtUSAlt/countrygt   lt/addressgt
4
How to make data useful and usable?

• One data model for all of CENS
• Not likely, that presumes that all science
  problems are the same
• One data model for each CENS research area
• More promising approach
• Various scientific communities have agreed on the
  common models

5
Seismology

• Seismic data has been collected via digital
  instruments for over 30 years.
• There are robust and stable standards for
  describing seismic data across systems and data
  formats (SEED Standard for the Exchange of
  Earthquake Data)
• Consortia to centralize and disseminate seismic
  datasets
• IRIS (Incorporated Research Institutions for
  Seismology)
• NEES (Network for Earthquake Engineering
  Simulation)

6
Habitat Monitoring

• Habitat monitoring research
• Draws upon multiple disciplines and technologies
• Integrates data across a wide range of ecological
  scales (chemistry, physiology, ecology, and
  environment)
• Available testbeds include embedded microclimate
  sensor network and embedded phenology network
  (including wildlife and plant monitoring)

• Habitat monitoring data
• Temperature, moisture, and barometric pressure
• Video data

7
James Reserve and habitat monitoring community

• Why we started with this community?
• One of the initial CENS sensor deployments
• The project is at an early stage of defining data
  and metadata requirements
• Data from this project are being used as the
  basis for our initial inquiry learning research
  in CENS

8
Ecological Metadata Language (EML)

• XML- based standard, developed by and for
  ecological community
• Divided into modules such as eml-access,
  eml-attribute, eml-project
• Describes data, literature, software, products
• Not well optimized for sensor data
• Optimized for describing data and not the
  derivation of data
• Uses Morpho Client as a cross-platform for
  creating and organizing data and metadata, either
  locally or on a shared network server

9
Ecological Metadata Language (EML)

• - ltcoveragegt
• - ltgeographicCoveragegt
•   ltgeographicDescriptiongtGCE Study Site GCE1 --
  Eulonia, Georgia, USA. Transitional salt
  marsh/upland forest site at the upper reach of
  the Sapelo River near Eulonia, Georgia. The main
  marsh area is to the north of the channel where
  the upland is controlled by DNR. Several small
  creeks lie within the study area. Residential
  development is increasing on the upland areas
  south of the channel. A hydrographic sonde is
  deployed within this site attached to a private
  dock to the south of the main channel near the
  HW-17 bridge.lt/geographicDescriptiongt
• - ltboundingCoordinatesgt
•   ltwestBoundingCoordinategt-81.427321lt/westBounding
  Coordinategt
•   lteastBoundingCoordinategt-81.410390lt/eastBounding
  Coordinategt
•   ltnorthBoundingCoordinategt31.546173lt/northBoundin
  gCoordinategt
•   ltsouthBoundingCoordinategt31.535095lt/southBoundin
  gCoordinategt
•   lt/boundingCoordinatesgt
•   lt/geographicCoveragegt

10
Describing Instruments

• Sensor Model Language (SensorML)
• Emerging OpenGIS standard for describing sensors
  and sensor data
• Developed to support data discovery, data
  processing and geolocation
• Can be used for in-situ or remote sensors,
  dynamic or static platforms
• Optimized for large sensors and large platforms
• Describes resources for sensor management and
  discoveries, but not sensor-derived data

11
Sensor Model Language (SensorML)
12
Science and Education

• We need to make the science data useful for
  teaching grade 6-12 science.
• Problem because the scientific models describe
  the data, and the education models describe
  lessons (grade level, instruments required for
  the lesson, time required to perform the lesson,
  educational standards, etc.)

13
Science and Education Data Models
14
Science and Education Data Models Possible
Solution

• Manage scientific data with models appropriate to
  the scientific community
• Construct filters and tools to make scientific
  data useful to K-12 students and teachers
• Reduce granularity of data (e.g. temperature at
  hourly, rather than minute intervals)
• Develop tools to display these data (e.g. simple
  charts and graphs)
• Describe filters and tools using models
  appropriate to educational community (e.g. LOM,
  SCORM, GEM)

15
Science and Education Data Models Possible
Solution
Sets of Data collected
run through Filters and Tools
to produce understandable Tables, Charts and
Graphs
16
Current accomplishments and next steps

• James Reserve
• Map current data structures to EML and SensorML
  to determine the fit
• Analyze scientific papers and documents to
  determine required data elements
• Create use scenarios
• Interview scientists

17
Current accomplishments and next steps

• Education
• Work with inquiry module team to identify data
  requirements
• Interview teachers

18
Discussion and Conclusions

• Ensuring accessibility and integrity of CENS data
  to multiple communities
• requires
• Understanding of the practices of each community
• Understanding of relationships between those
  practices
• Means to bridge the gaps

19
Acknowledgements

• Christine Borgman
• Andrew Wu
• Bill Sandoval
• Noel Enyedy
• Joe Wise
• Mike Wimbrow