Reiner Schlitzer Alfred Wegener Institute for Polar and Marine Research Data Quality Control and Visualization with Ocean Data View 4
Wish: All data are of highest quality. Reality…
Objective: Extend and utilize ODV‘s extensive graphical display capabilities and interactive controls for automatic and visual quality control and data quality flagging.
General data analysis and visualization softwareGeneral data analysis and visualization software >10,000 registered users>10,000 registered users
Ocean Data View - Version 4 Complete software re-coding to overcome many limitations of ODV3. Specifically…
LOCAL_CDI_ID EDMO_code Bot. Depth [m] … etc. required optional required by SeaDataNet …allow an unlimited number of numeric and text metavariables. Cruise Station Type YYYY-MM-DDTHH:MM:SS.SSS Longitude [degrees_east] Latitude [degrees_north]
Flag Description ODVGENERICQF GTSPP ARGO SEADATANET ESEAS WOD WODSTATION WOCEBOTTLE WOCECTD WOCESAMPLE QARTOD BODC PANGAEA SMHI no quality control Q*blank good value blank probably good value blank probably bad value K?? bad value K/B changed value R*| value below detection <<< value in excess >>> interpolated value T*| missing value N*B value phenomenon uncertain100A Q*B QFSetID= SEADATANET Set Description: SeaDataNet quality flags Reference: …support the SeaDataNet quality flag scheme.
Flag Description ODVGENERICQF GTSPP ARGO SEADATANET ESEAS WOD WODSTATION WOCEBOTTLE WOCECTD WOCESAMPLE QARTOD BODC PANGAEA SMHI no quality control Q*blank good value blank probably good value blank probably bad value K?? bad value K/B changed value R*| value below detection <<< value in excess >>> interpolated value T*| missing value N*B value phenomenon uncertain100A Q*B QFSetID= SEADATANET Set Description: SeaDataNet quality flags Reference: …support all important quality flag schemes and mappings between them.
ODV Integration into QC Process Data exchange via extended ODV spreadsheet format adopted by SeaDataNet NODCDataArchive QCwithODV
Data Editing and Quality Control easy spotting and identification of outliers or offsets easy spotting and identification of outliers or offsets painless editing of data value and quality flag painless editing of data value and quality flag logging of all value or flag modifications logging of all value or flag modifications automatic range checks and manual or automatic editing automatic range checks and manual or automatic editing
Manual Data Editing and Quality Control
Automatic Range Checks and Outlier Removal
All changes logged in file.
Using quality flags to filter the data by quality.
Integration of DIVA gridding software into ODV ODV will automatically… create all files for DIVAcreate all files for DIVA run DIVA mesh generation and estimationrun DIVA mesh generation and estimation read and display the gridded fieldread and display the gridded field
Example (3) - Filling large gaps…
Training: Total of 4.5 days of presentations and exercises at two SeaDataNet training courses at IOC Oostende.
Deliverable No Deliverable titleTaskLeaderForeseen Delivery date Nature 1Specifications for ODV Preliminary Version SDN1JRA3.2P15M6report 2ODV – Version SDN1 + documentation ready for beta-tests and first training course JRA3.2P15M10Software + electronic manual 3Specifications for ODV Version SDN2JRA3.3P15M18report 4ODV – Version SDN2JRA3.3P15M22Software + manual 5Specifications for remote sensing moduleJRA3.4P15M30report 6ODV - Version SDN3JRA3.4P15M34Software + manual 7ODV final documented version including maintenance after feedback from the training workshops JRA3.1P15M47Software + manual 8Contribution to Final ReportJRA3.1P15M59report JRA3 Deliverables:
Future: Use of ODV/DIVA in Web Applications Web Application ODV/DIVA X/Y/Z Data Image Gridded Values
Component of Version 2 viewing servicesComponent of Version 2 viewing services Identify partner PIs and data centersIdentify partner PIs and data centers Start discussion of interface designStart discussion of interface design