VO Course 06: VO Data-models
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Transcript of VO Course 06: VO Data-models
VO, Data Models, and VO ArchivesJuan de Dios Santander Vela (IAA-CSIC)
Overview
Data Model Definition
IVOA and Data Modelling
Radio Astronomy DAta-Model for Single-dish
Data Models
Data Model Definition
Complete description of the set of entities needed for information storage in a particular field, which specifies both the data being stored, and the relationships between them.
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
JUAN DE DIOS SANTANDER [email protected]
INSTITUTO DE ASTROFÍSICA DE ANDALUCÍA (CSIC)BECARIO I3P DE POSTGRADO (01/2006-12/2006)BECARIO PREDOCTORAL IAA (03/2005-12/2005)
Information Structure
DATA MODEL DEPENDENT
Data model elements
Fields (Attributes in OOP, XML)
Data types
Semantics
Relationships
Information Structure
Information Structure
Field Object Number Source RA Dec Epoch
Data Type BigInt String Float Float String Code
Example Data
3123453445OBGGALAX_1
‘CIG 96’96
132.34788h12º50’52’’
-05.3345-0h5º20’42’’
‘J2000’‘Epoch J2000’
ATTRIBUTES & DATA TYPES
Information StructureSEMANTICS & UCDS
Field Object Number Source RA Dec Epoch
Meaning Object identifier Source name Right
Ascension Declination Epoch code
UCD meta.id; meta.code
meta.id; meta.name
pos.eq.ra; meta.main
pos.eq.dec; meta.main
time.epoch; meta.code
Elements of a DMEntities
Data model building blocksGrouping of related attributes
FieldsActual data elements of the model
RelationshipsBetween attributes in different Entities
Cardinalities Data types
Units
Semanticsvia UCDs & UTypes
Data Model Roles
Discovery
Evaluation
Data Access
Transformation
Data Model Roles
Discovery
Evaluation
Data Access
Transformation
IVOA Data Modelling
IVOA Data Modelling
IVOA Data Modelling
IVOA Data Modelling
IVOA Data Modelling
IVOA Data Modelling
Observation Data Model
Observation
ObsData Characterisation Target
Coverage
Location Support Sensitivity
Resolution Precision
Curation Provenance
Observing Configuration
Observing Elements
Processing
Calibration
Independent
Dependent
of observationdata reduction
DRAFT
Observation Data Model
Observation
ObsData Characterisation Target
Coverage
Location Support Sensitivity
Resolution Precision
Curation Provenance
Observing Configuration
Observing Elements
Processing
Calibration
Independent
Dependent
of observationdata reduction
DRAFT
Characterisation DM
Figure 4: The layered structure of Characterisation: This diagram synthe-sises the Property/Axis/Layer approach. The concepts are represented in yel-low. The coarse description is designed by the first level (blue boxes), while thepale blue ones represent the complementary metadata. The Bounds, Supportand Sensitivity classes are nested levels of detail to add knowledge about theCoverage of an Observation. Symmetrically, Resolution and Sampling mayalso have the 4-level structure of description. The complete Characterisa-tion for one observation is obtained by filling the tree for each relevant axis:spatial, spectral, temporal, etc.
17
Characterisation DM: Errors
Figure 5: This class diagram illustrates the CharacterisationAxis class andits relationship with the Accuracy class, which encompasses various types oferrors such as systematic or statistical.
20
Spectral Data Model
Spectrum
Target Data Characterisation CoordSys Curation DataID Derived
Spectral DM: Data
TimeAxis
SpectralAxis
n
Accuracy
value,unit
FluxAxis
StatError SysError
DataAxis
Data
StatErrHighStatErrLow
BinSize
BinHigh
BinLow
Background
Model
ucd
unit
ResolutionQualityValue
1,..SegmentSize
Figure 2: Diagram for Data object
Characterization
CharacterizationAxis
SpatialAxis
TimeAxis
SpectralAxis
n
Accuracy Resolution
value,unit
FluxAxis
Locationvalue
Bounds
Extent Start
Stop
Support
Area Fill
StatError SysError
Calibration
BinSize
Coverage
unit
ucd
name
Figure 3: Diagram for Characterization object8
Spectral Characterisation
TimeAxis
SpectralAxis
n
Accuracy
value,unit
FluxAxis
StatError SysError
DataAxis
Data
StatErrHighStatErrLow
BinSize
BinHigh
BinLow
Background
Model
ucd
unit
ResolutionQualityValue
1,..SegmentSize
Figure 2: Diagram for Data object
Characterization
CharacterizationAxis
SpatialAxis
TimeAxis
SpectralAxis
n
Accuracy Resolution
value,unit
FluxAxis
Locationvalue
Bounds
Extent Start
Stop
Support
Area Fill
StatError SysError
Calibration
BinSize
Coverage
unit
ucd
name
Figure 3: Diagram for Characterization object8
Space-Time Coordinates
Radio Astronomy DAta-Model for Single-dish
RADAMS’ SourcesData Model for Observation
McDowell, Bonnarel et al., IVOA Data Model WG Internal Draft
Data Model for Astronomical Dataset Characterisation
McDowell, Bonnarel et al., IVOA Data Model WG Note
IVOA Spectral Data ModelMcDowell, Tody et al., IVOA Data Model WG Working Draft
IVOA Data Model for Raw Radio Telescope DataLamb and Power, IVOA Radio IG Note
RADAMS StructureObservation
ObsData
Characterization Provenance
Curation
Policy
PackagingTarget
Accuracy
SensitivityPrecisionResolutionCoverage
RADAMS StructureObservation
ObsData
Characterization Provenance
Curation
Policy
PackagingTarget
Accuracy
SensitivityPrecisionResolutionCoverage
Data Model for Observation
RADAMS StructureObservation
ObsData
Characterization Provenance
Curation
Policy
PackagingTarget
Accuracy
SensitivityPrecisionResolutionCoverage
Data Model for Dataset
Characterisation
RADAMS StructureObservation
ObsData
Characterization Provenance
Curation
Policy
PackagingTarget
Accuracy
SensitivityPrecisionResolutionCoverage Undefined Data Models
RADAMS Detail36
CHAPTER 7.DETAILED DESCRIPTION OF RADAM CLASSES
Table 7.1:AxisF
rame.Spatialmetad
ata.
FITS
Attribute
Keyword
UCD
Descrip
tion
axisName
assign
meta.i
d;
meta.m
ain
Axisname.
calibrati
onStatus
assign
obs.ca
lib;
meta.c
ode
Calibrati
on status from
a con-
trolled
vocabulary
:unc
al-
ibrate
d,cal
ibrate
d,rel
a-
tivea , no
rmaliz
edb .
ucd
assign
meta.u
cd;
meta.m
ain
MainUCD for
the axis.
unit
assign
meta.u
nit;
meta.m
ain
Mainunits for
the axis.
refPos
assign
meta.r
ef;
meta.i
d
Identificati
onof the orig
in po-
sition
withinthe spaceR
ef-
Frame from
a control
ledvoc
ab-
ulary; See Space-
Time Coordi-
nateData
Model [13].
spaceRefFram
e
WCSNAM
E
orRAD
ESYS
pos.fr
ame;
meta.i
d
Identificati
onof the refe
rence
system
froma con
trolled
vo-
cabulary
; seeSpace-
Time Co-
ordinate
DataModel [13]
: FK4,
FK5, EL
LIPTIC
. . .
coordEquinox
assign
pos;
time.e
quinox
Equinox (only if need
ed).
epoch
assign
pos;
time.e
poch
Epoch(on
ly if needed).
independentAxis
assign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagindicat
ing
whetherthe axis
is indepen-
dentof the rest
or not.
undersamplingStatu
sass
ign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagindicat
ing
whether the dataare
sampled
in this axisor not.
regularS
tatus
assign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagused
incase
ofsam
pleddata,
indicating
whether sampling is regu
laror
not.
a relati
verefe
rs to calibrate
d data,exce
pt foran additiv
e or multiplica
tivecon
stant.
b normal
ized refe
rs to dimensionless
quantities
, such as thoseresu
lting from
the division
between
two commensurab
le datasets.
7.5. PACKAGING
61
Table 7.40: Calibration metadataa .
FITS
AttributeKeyword UCD
Description
timestampDATE-RED
obs.param;
time.epoch
Timestamp for the calibration
step being performed.
parameter.nameassign
obs.calib;
obs.param;
meta.id
Keyword defining the parameter
that we will characterisewith the
remaining attributes.
parameter.typeassign
obs.calib;
obs.param;
meta.code
Type of calibration parameter
used, from a controlled vo-
cabulary: additive, factor,
polynomial, exponent
ial,
logarithmic.
parameter.valueassign
obs.calib;
obs.param;
meta.number
Value for the main calibra-
tion parameter, where parame-
ter.type is not polynomial.
parameter.sigma assignobs.cali
b;
obs.param;
meta.number
Value of sigma, for exponential
calibrations.
parameter.calCoe�.[n] assignobs.cali
b;
obs.param;
meta.number
nth degree coe⇥cient for a
polynomial calibration parame-
ter; polynomial degree is derived
from the maximum n.
aIt is mandatory that at least one [parameter.name,
parameter.type,
parameter.value]
triplet appears, with fluxScale as parameter.name, and one of
antennaTemperatu
re, mbBrightnessTemp
erature, or S nu as the parameter.value, with a
parameter.type of string.
Observation
• Target.Name
• Target.Descrip
tion
• Target.Class
• Target.Pos
• Target.spectra
lClass
• Target.redshift
.statError
• Target.redshift
.Confidence
Target
Figure 7.9: Target data model.
order to find a maser, but that Target will be most likely included within an
Astronomical object, such as a planetary nebula, associated to that Target.
7.5 Packaging
The Packaging class has to deal with how di�erent pieces of data will be
presented. For instance, if we wanted to retrieve data belonging to the maser
60CHAPTER 7. DETAILED DESCRIPTION OF RADAM CLASSES
Table 7.39: Processing Step metadata.FITS
AttributeKeyword UCD
Description
timestampDATE-RED obs.param;time.epoch Timestamp for the processing
step being performed.
type
assignobs.param;meta.code Type of processing applied
to source data; comes from a
controlled vocabulary: unproc-
essed, noiseWeightedAverage,
nonWeightedAverage.
softwarePackageassign
meta.software;meta.id Software package used for data
processing; should come from
a controlled vocabulary: CLASS,
AIPS, AIPS++, CASA, MOPSIC,
GILDAS, MIRA, MIR, other. In the
case of other, the actual package
that was used should be added
as a parameter, with param-
eter.name as softwarePackage
and the parameter.value as the
package name.
parameter[n].name assignobs.param;meta.code Additional processing parameter
name, whose value will be in pa-
rameter.value; eventually, we will
have a controlled list of possible
parameter.name values.
parameter[n].type assignobs.param;meta.code From a controlled vocabulary:
integer, float, string. . . At
least all of FITS data types should
be present.
parameter[n].value assignobs.parama
Value for the parameter indicated
by parameter.name.
aThe final UCD to mark parameter[n].value will be calculated when writing the VOTable,
as it depends on parameter.type; it will be obs.param; meta.number most of the time, but
it could be obs.param; meta.name or obs.param; meta.code, depending on the context.
RADAMS Detail36
CHAPTER 7.DETAILED DESCRIPTION OF RADAM CLASSES
Table 7.1:AxisF
rame.Spatialmetad
ata.
FITS
Attribute
Keyword
UCD
Descrip
tion
axisName
assign
meta.i
d;
meta.m
ain
Axisname.
calibrati
onStatus
assign
obs.ca
lib;
meta.c
ode
Calibrati
on status from
a con-
trolled
vocabulary
:unc
al-
ibrate
d,cal
ibrate
d,rel
a-
tivea , no
rmaliz
edb .
ucd
assign
meta.u
cd;
meta.m
ain
MainUCD for
the axis.
unit
assign
meta.u
nit;
meta.m
ain
Mainunits for
the axis.
refPos
assign
meta.r
ef;
meta.i
d
Identificati
onof the orig
in po-
sition
withinthe spaceR
ef-
Frame from
a control
ledvoc
ab-
ulary; See Space-
Time Coordi-
nateData
Model [13].
spaceRefFram
e
WCSNAM
E
orRAD
ESYS
pos.fr
ame;
meta.i
d
Identificati
onof the refe
rence
system
froma con
trolled
vo-
cabulary
; seeSpace-
Time Co-
ordinate
DataModel [13]
: FK4,
FK5, EL
LIPTIC
. . .
coordEquinox
assign
pos;
time.e
quinox
Equinox (only if need
ed).
epoch
assign
pos;
time.e
poch
Epoch(on
ly if needed).
independentAxis
assign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagindicat
ing
whetherthe axis
is indepen-
dentof the rest
or not.
undersamplingStatu
sass
ign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagindicat
ing
whether the dataare
sampled
in this axisor not.
regularS
tatus
assign
pos;
obs.pa
ram;
meta.c
ode
Boolean
flagused
incase
ofsam
pleddata,
indicating
whether sampling is regu
laror
not.
a relati
verefe
rs to calibrate
d data,exce
pt foran additiv
e or multiplica
tivecon
stant.
b normal
ized refe
rs to dimensionless
quantities
, such as thoseresu
lting from
the division
between
two commensurab
le datasets.
7.5. PACKAGING
61
Table 7.40: Calibration metadataa .
FITS
AttributeKeyword UCD
Description
timestampDATE-RED
obs.param;
time.epoch
Timestamp for the calibration
step being performed.
parameter.nameassign
obs.calib;
obs.param;
meta.id
Keyword defining the parameter
that we will characterisewith the
remaining attributes.
parameter.typeassign
obs.calib;
obs.param;
meta.code
Type of calibration parameter
used, from a controlled vo-
cabulary: additive, factor,
polynomial, exponent
ial,
logarithmic.
parameter.valueassign
obs.calib;
obs.param;
meta.number
Value for the main calibra-
tion parameter, where parame-
ter.type is not polynomial.
parameter.sigma assignobs.cali
b;
obs.param;
meta.number
Value of sigma, for exponential
calibrations.
parameter.calCoe�.[n] assignobs.cali
b;
obs.param;
meta.number
nth degree coe⇥cient for a
polynomial calibration parame-
ter; polynomial degree is derived
from the maximum n.
aIt is mandatory that at least one [parameter.name,
parameter.type,
parameter.value]
triplet appears, with fluxScale as parameter.name, and one of
antennaTemperatu
re, mbBrightnessTemp
erature, or S nu as the parameter.value, with a
parameter.type of string.
Observation
• Target.Name
• Target.Descrip
tion
• Target.Class
• Target.Pos
• Target.spectra
lClass
• Target.redshift
.statError
• Target.redshift
.Confidence
Target
Figure 7.9: Target data model.
order to find a maser, but that Target will be most likely included within an
Astronomical object, such as a planetary nebula, associated to that Target.
7.5 Packaging
The Packaging class has to deal with how di�erent pieces of data will be
presented. For instance, if we wanted to retrieve data belonging to the maser
60CHAPTER 7. DETAILED DESCRIPTION OF RADAM CLASSES
Table 7.39: Processing Step metadata.FITS
AttributeKeyword UCD
Description
timestampDATE-RED obs.param;time.epoch Timestamp for the processing
step being performed.
type
assignobs.param;meta.code Type of processing applied
to source data; comes from a
controlled vocabulary: unproc-
essed, noiseWeightedAverage,
nonWeightedAverage.
softwarePackageassign
meta.software;meta.id Software package used for data
processing; should come from
a controlled vocabulary: CLASS,
AIPS, AIPS++, CASA, MOPSIC,
GILDAS, MIRA, MIR, other. In the
case of other, the actual package
that was used should be added
as a parameter, with param-
eter.name as softwarePackage
and the parameter.value as the
package name.
parameter[n].name assignobs.param;meta.code Additional processing parameter
name, whose value will be in pa-
rameter.value; eventually, we will
have a controlled list of possible
parameter.name values.
parameter[n].type assignobs.param;meta.code From a controlled vocabulary:
integer, float, string. . . At
least all of FITS data types should
be present.
parameter[n].value assignobs.parama
Value for the parameter indicated
by parameter.name.
aThe final UCD to mark parameter[n].value will be calculated when writing the VOTable,
as it depends on parameter.type; it will be obs.param; meta.number most of the time, but
it could be obs.param; meta.name or obs.param; meta.code, depending on the context.
RADAMS: OverviewObservation
ObsData
Characterization
Coverage Resolution Precision
Target Provenance Curation Policy Packaging
Accuracy
perAxisType
LocationBoundsSupportSensitivity
RADAMS: OverviewObservation
ObsData
Characterization
Coverage Resolution Precision
Target Provenance Curation Policy Packaging
Accuracy
perAxisType
LocationBoundsSupportSensitivity
Original to RADAMS
RADAMS: OverviewObservation
ObsData
Characterization
Coverage Resolution Precision
Target Provenance Curation Policy Packaging
Accuracy
perAxisType
LocationBoundsSupportSensitivity
Original to RADAMS
Specified forradio astronomy
RADAMS — ObsDataObsData
AxisFrame.Observable
-location-bounds-support
Coverage.Observable
Accuracy
Resolution.Observable
SamplingPrecision.Observable
AxisFrame.Spectral
Accuracy
-location-bounds-support
Coverage.Spectral
Resolution.Spectral
SamplingPrecision.Spectral
AxisFrame.Temporal
-location-bounds-support
Coverage.Temporal
Accuracy
Resolution.Temporal
AxisFrame.Spatial
-location-bounds-support
Coverage.Spatial
Accuracy
Resolution.Spatial
CharDM — Spatial axis-dataID
ObsData
+ObsData.dataID-axisName: spatial-calibrationStatus-ucd: pos-unit: deg-spaceRefFrame: FK5-coordEquinox: J2000-refPos-independentAxis: true-undersamplingStatus: false-regularStatus: true
AxisFrame.Spatial
+ObsData.dataID+AxisFrame.Spatial-refVal
Resolution.Spatial
+ObsData.dataID+AxisFrame.Spatial-locationName-bounds
Coverage.Spatial
-quality-statError-sysError
Accuracy
+ObsData.dataID+AxisFrame.Spatial+Resolution.Spatial.refVal-value: HPBW
Resolution.Spatial.RefVal
+Coverage.Spatial.locationName-coord.Name: {RA, DEC}-coord.Value
Coverage.Spatial.Location
CharDM — Temporal Axis
-dataID
ObsData
+ObsData.dataID-axisName: spatial-calibrationStatus-ucd: time-unit: s-spaceRefFrame: UTC-refPos: TOPOCENTRIC-independentAxis: true-undersamplingStatus: true-regularStatus: false
AxisFrame.Temporal
+ObsData.dataID-refVal
Resolution.Temporal
+ObsData.dataID+AxisFrame.Temporal-locationName-bounds
Coverage.Temporal
-quality-statError-sysError
Accuracy
+Coverage.Temporal.locationName-coord.Name: time-coord.Value
Coverage.Time.Location
+Coverage.Temporal.bounds-coord.maxValue-coord.minValue
Coverage.Time.Bounds
-startValue[n]-endValue[n]
Coverage.Time.Support
CharDM — Spectral axis
-dataID
ObsData
+ObsData.dataID-axisName: spatial
-calibrationStatus-ucd: em
-unit: Tmb, Tsky, Jy
-refPos:-dopplerDef-independentAxis: true
-undersamplingStatus: false
-numBins: 384
-regularStatus: true
AxisFrame.Spectral
+ObsData.dataID-refVal
Resolution.Spectral
+ObsData.dataID+AxisFrame.Spectral-locationName-bounds
Coverage.Spectral
-quality-statError-statErrorHigh-statErrorLow-sysError-sysErrorHigh-sysErrorLow
Accuracy
+Coverage.Spectral.locationName-coord.Name: CentralFreq
-coord.Value-coord.Name: MaxPeakFreq
-coord.Value-coord.Name: MaxPeakValue
-coord.Value
Coverage.Spectrum.Location
+Coverage.Spectral.bounds-coord.maxValue-coord.minValue
Coverage.Spectral.Bounds
-startValue[n]-endValue[n]
Coverage.Spectrum.Support
+ObsData.dataID-refVal: pixelScale
SamplingPrecision.Spectral
CharDM — Observable axis
-dataID
ObsData
+ObsData.dataID-axisName: spatial
-calibrationStatus-ucd: phot.flux
-unit: Jy/K, Jy/Beam
-independentAxis: true
-undersamplingStatus: false
-numBins: ADC bits/sample?
-regularStatus: true
AxisFrame.Observable
+ObsData.dataID-refVal: flux/SNR
Resolution.Observable
+ObsData.dataID+AxisFrame.Observable-locationName-bounds
Coverage.Observable
-quality-statError-statErrorHigh-statErrorLow-sysError-sysErrorHigh-sysErrorLow
Accuracy
+Coverage.Observable.locationName-coord.Name: MaxPeakFlux
-coord.Value
Coverage.Observable.Location
+Coverage.Observable.bounds-coord.maxValue-coord.minValue
Coverage.Observable.Bounds
-startValue[n]-endValue[n]
Coverage.Observable.Support
+ObsData.dataID-refVal: ADC bits per sample?
SamplingPrecision.Observable
Target
• targetName
Observation
• name
• description
• class
• pos
• spectralClass
• redshift.statError
• redshift.Confidence
Target
ObservationProvenance
InstrumentConf
-name
AntennaConf
-polarisation: L,R,X,Y
Feed
-name
BeamConf
+AntennaConf.name
-mount
-majorAxis
-minorAxis
-effectiveArea
Antenna
-name
-description
-shortName
-locationName
-URL
Instrument
+Instrument.locationName
–latitude
-longitude
-height
Location
+beamConf.name
-beamMajor
-beamMinor
-sensitivity
-meanBeamSolidAngle
-minorBeamSolidAngle
-directivity
-gain
-resolution
Beam
+beamConf.Name
-skyCentreFreq
-ifCentreFreq
-bandwidth
Receiver
+beamConf.name
-chanSeparation
-freqResol
-velRefFrame
-refChanNum
-refChanFreq
-restFreq
-molecule
-transition
Spectrum
+beamConf.name
-chanSeparation
-velResol
-velRefFrame
-refChanNum
-refChanVel
-restFreq
-molecule
-transition
Velocity
{This set is defined
for each instrument
band AND receiver
configuration
Provenance.Instrument
Observation Provenance InstrumentConf
-timeStamp
-opacity
-temperature
-humidity
-wind
AmbientConditions+AmbientConditions.timeStamp
-opacity
-skydipStart
-azimuth
-elevation.[n]
-tsky.[n]
-atmosphericModel
OpacityCurve
Provenance. AmbientConditions
InstrumentConf
-timeStamp
Processing
Observation Provenance AmbientConditions
+Processing.timeStamp
-parameter.name
-parameter.kind
-parameter.value
-parameter.sigma
-parameter.calCoeff[n]
Calibration
+Processing.timeStamp
-kind
-softwarePackage
-parameter.name
-parameter.kind
-parameter.value
ProcessingStep
Provenance.Processing
Curation
Observation
-Publisher-PublisherID-Logo-Version-Contributor-Contact.Name-Contact.Email-Subject–Service.InterfaceURL-Service.BaseURL-Service.StandardURI-Service.StandardURL
Curation
-ProjectID-Title-Description
Project
-Name-Operator
Observer
-projCategory
ProjCategory
-projType
ProjType
-Title-Description-scheduleType
Proposal
-Name-Mail
Author
-startDate-endDate
Term
ObservingProgram
-observationID-title-description-creator-observerID-date-facility-instrument
DataID
-Name-Mail
PrincipalInvestigator
-Name-Mail
coInvestigator
common curation data
belongsto
encom
passes
has data
belongs to
observed by
origin
ate
s fro
m
written bywill perform
is one of
is o
ne o
f
scheduled at
Policy
+projectID
-observationID
-title
-description
-creator
+observerID
+operatorID
-date
-facility
-instrument
DataID
-ProjectID
+principalInvestigator
+coInvestigator[n]
-Title
-Description
Project
-userID
-name
-institution
-contactInfo
Users
+projectID
-roleKind
-permissions
Policy
providesuseridentification
User RoleAssignationAlgorithm
Role and Permissions
PolicyStart
Stop
obsData.PrincipalInvestigator.ID == loggedUser.ID?
loggedUser.role =principalInvestigator
obsData.observer.ID == loggedUser.ID?
loggedUser.role =observer
loggedUser.ID found in obsData.coInvestagators array?
loggedUser.role =coInvestigator
loggedUser.ID found on dbUsers, and observatoryStaff is true?
loggedUser.role =observatoryStaff
loggedUser.role =none
yes
no
yes
yes
yes
no
no
no
VOPack Schema/ schema originatingQuery
voPack@
ID
ID
@ version
description
originatingQuery
packUnit
1..
packUnit@
packUnitType
type
@
anyURI
URI
@
string
informationPath
description
packUnit
0..
characterisation
description
characterisation
References & Links
Integrating Radio Astronomical Archives and Legacy Tools in the VO Framework, Ph.D. Thesis, Chapter 5
Data Model for Astronomical DataSet Characterisation
Spectral Data Model