718
Diepenbroek et al.
used by validation routines during the import to filter out suspect values, e.g. outliers.
To improve the data consistency, data sets can
be stored at different levels of processing. The primary data, e.g. counts of a microfossil assemblage
or weights of granulometric investigations, are the
raw data for calculations and interpretations. Archiving the raw data allows future re-calibration or
new interpretations of the data sets. The secondary data are those values calculated from the raw
data, and in many cases are percentages or other
units of concentration. The secondary data will
usually be the proxy data for the evaluation of parameters describing past environmental conditions.
Parameters evaluated from the secondary data
(e.g. paleotemperatures) are defined as tertiary
data. With the definition of a tertiary parameter
obtained from the proxy data, a method and reference can be given where the formula or method is
published.
PANGAEA also ensures that the copyright is
considered. If an information system also stores unpublished data, it is crucial for the acceptance and
the trust of the database that the data are protected
by a hierarchical system which can be organized
and controlled by the user. The owner of a specific
data set is the principal investigator, who is able to
either define access rights to individual users/groups
or to open data sets across the whole system.
When using foreign data, the given reference has
to be cited, thus giving a benefit to the data producers. For unpublished data the principal investigator has to be asked for permission to use the data.
The great variety of parameters, methods,
calibrations and interpretations used in paleoenvironmental reconstruction, as well as the modification of established methods, are major obstacles to the integrative use of data sets in a common system. The challenge of managing these heterogenic and dynamic data was met in PANGAEA
through a highly flexible data model consisting of a
relational data structure in combination with specialized server software generating an object-oriented view of the data. This 'middle ware' is software to provide the link between clients and server.
It transforms a logical request from a client into
SQL or converts the resulting data from the server
into a client readable format. Thus the middle ware
allows the user to retrieve complex data matrices,
e.g. time slices.
The simplified data structure ofP ANGAEA is
shown as a graphic on the opening screen of the
client software (Fig. 1). The graphic allows users
to enter all levels, tables and tools by selecting the
required field. The structure reflects the standard
processing steps for paleo-data. Different institutes/projects (PROJECT) working in the field
of paleoceanography carry out expeditions
(CAMPAIGN) for sampling. During a cruise at a
number oflocations (SITE) different samples may
be taken or measurements made (EVENT). At distinct points!intervals the medium to be investigated
(e.g. sediment, water or ice) is sub-sampled or
measured for different requirements. Information
about the sampling procedure is stored at the
SAMPLE level. Down to this level, all data are considered to be meta-data. From each sample one or
more analytical data points will be produced which
can be found on the DATA level, with the related
meta-information (Fig. 2). Lists including standardized meta-information are connected to the main
data table (e.g. gear, method). The parameters are
gathered into parameter groups for a better overview and data are grouped as primary, secondary
and tertiary data as described above - data types
can be numerical, textual or pictures. The combination of the DATA, 'Parameter' and 'Method'
table is the essential part of the model, which allows the definition and storage of new, unique parameters by the user at any time. From any table,
data can be exported as tabulator delimited text files
or plotted graphically. The possibility to configure
the tables enables the user to extract data sets in a
format needed for the import in commercial software or in his own programs.
The available meta-data comprises related information about expeditions, sampling sites/sets and
storage facilities. In addition to the sampling/investigation site label, the most important meta-data are
the location (latitudellongitude) and the elevation.
For archiving profiles the defmition of two locations!
elevations, including the date/time, is allowed. All
scientists and institutes related to the data in the
system are stored with their full addresses. Other
related items, such as the names of ships, gears or
sample types, are defined in lists which are regu-
Diepenbroek et al.
used by validation routines during the import to filter out suspect values, e.g. outliers.
To improve the data consistency, data sets can
be stored at different levels of processing. The primary data, e.g. counts of a microfossil assemblage
or weights of granulometric investigations, are the
raw data for calculations and interpretations. Archiving the raw data allows future re-calibration or
new interpretations of the data sets. The secondary data are those values calculated from the raw
data, and in many cases are percentages or other
units of concentration. The secondary data will
usually be the proxy data for the evaluation of parameters describing past environmental conditions.
Parameters evaluated from the secondary data
(e.g. paleotemperatures) are defined as tertiary
data. With the definition of a tertiary parameter
obtained from the proxy data, a method and reference can be given where the formula or method is
published.
PANGAEA also ensures that the copyright is
considered. If an information system also stores unpublished data, it is crucial for the acceptance and
the trust of the database that the data are protected
by a hierarchical system which can be organized
and controlled by the user. The owner of a specific
data set is the principal investigator, who is able to
either define access rights to individual users/groups
or to open data sets across the whole system.
When using foreign data, the given reference has
to be cited, thus giving a benefit to the data producers. For unpublished data the principal investigator has to be asked for permission to use the data.
The great variety of parameters, methods,
calibrations and interpretations used in paleoenvironmental reconstruction, as well as the modification of established methods, are major obstacles to the integrative use of data sets in a common system. The challenge of managing these heterogenic and dynamic data was met in PANGAEA
through a highly flexible data model consisting of a
relational data structure in combination with specialized server software generating an object-oriented view of the data. This 'middle ware' is software to provide the link between clients and server.
It transforms a logical request from a client into
SQL or converts the resulting data from the server
into a client readable format. Thus the middle ware
allows the user to retrieve complex data matrices,
e.g. time slices.
The simplified data structure ofP ANGAEA is
shown as a graphic on the opening screen of the
client software (Fig. 1). The graphic allows users
to enter all levels, tables and tools by selecting the
required field. The structure reflects the standard
processing steps for paleo-data. Different institutes/projects (PROJECT) working in the field
of paleoceanography carry out expeditions
(CAMPAIGN) for sampling. During a cruise at a
number oflocations (SITE) different samples may
be taken or measurements made (EVENT). At distinct points!intervals the medium to be investigated
(e.g. sediment, water or ice) is sub-sampled or
measured for different requirements. Information
about the sampling procedure is stored at the
SAMPLE level. Down to this level, all data are considered to be meta-data. From each sample one or
more analytical data points will be produced which
can be found on the DATA level, with the related
meta-information (Fig. 2). Lists including standardized meta-information are connected to the main
data table (e.g. gear, method). The parameters are
gathered into parameter groups for a better overview and data are grouped as primary, secondary
and tertiary data as described above - data types
can be numerical, textual or pictures. The combination of the DATA, 'Parameter' and 'Method'
table is the essential part of the model, which allows the definition and storage of new, unique parameters by the user at any time. From any table,
data can be exported as tabulator delimited text files
or plotted graphically. The possibility to configure
the tables enables the user to extract data sets in a
format needed for the import in commercial software or in his own programs.
The available meta-data comprises related information about expeditions, sampling sites/sets and
storage facilities. In addition to the sampling/investigation site label, the most important meta-data are
the location (latitudellongitude) and the elevation.
For archiving profiles the defmition of two locations!
elevations, including the date/time, is allowed. All
scientists and institutes related to the data in the
system are stored with their full addresses. Other
related items, such as the names of ships, gears or
sample types, are defined in lists which are regu-