Data Management of Proxy Parameters with PANGAEA
717
detennined from the samples. With the introduction
of new and more efficient analytical methods the
number of parameters and the amount of data obtained has increased by an order of magnitude during the last decade. Some of the parameters are the
proxy data for the calculation of reconstructed
ocean properties (e.g. paleotemperature, paleosalinity, paleoproductivity). Important parameters
(Table 1) are the percentages of different species
of microfossil assemblages, stable isotopes, radionuclides, the biogenic composition of a sediment
or specific elements or elemental ratios. The
results and data are published e.g. in the journal
'Paleoceanography' which is one of the first journals to require that authors store the relevant data
in established databases (Webb et al. 1994).
The recovery and interpretation of proxy data
related to the paleoclimate has traditionally been
carried out by individuals or single laboratories, often examining regional records that cover a limited
temporal domain. The emergence of an integrated
earth systems science calls for a much fuller knowledge of the past, in both space and time, and for
data sets that are drawn as composites from different methods and techniques. The only way to
obtain a useful system for this purpose is to collect
as many proxy data as possible, to store this data
collection in a consistent fonnat and to make it easily
accessible to paleoceanographers. Tools forretrieving the data and for their visualization have to be
closely linked to the collection. This proxy data
collection, implemented in a network between working groups, can then be used as the data source as
well as a common interpretation tool. In addition,
geological proxy data which are used for the definition of ocean boundary conditions have to be used
for model verification (Lorenz et al. 1996). In the
future it could, and should, also be used as a publishing and reference system for data related to new
publications to ensure that all the relevant data are
stored in the same system.
In 1993, scientists from various Gennan
research institutes working in the field of paleoceanography initiated a project in response to
these needs. The goal was the implementation of
an infonnation system which would allow an overview of the sampling material available with the
related meta-infonnation, and which would store
paleoceanographic data of any kind in a consistent
form and make these data easy accessible to
the scientific community. Tools for import/export,
graphical presentation and complex retrievals need
to be closely related to data collection. Based on
the discussion and recommendations of this group,
the infonnation system PANGAEA (PaleoNetwork
for Geological and Environmental Data) was developed at the Alfred Wegener Institute for Polar
and Marine Research (A WI), financed by the German Ministry of Education, Science, Research and
Technology (BMBF) as part of a three year project
( 1994-1997).
This paper gives an overview and a short description ofthe PANGAEA data model, the hardware and software needed, the network concept
and also gives an introduction to the tools for visualizing data. The network is still in the stage of being established in the scientific community. Within
the next two years up to 10 institutes will be connected together to fonn the first real network for
data from research on the variability of climate and
environments.
System Description
The most important generic aspects of PANGAEA
are the quality and availability of the data as well
as the high adaptability and effective use of the
system. Data quality can be described in tenns of
the validity of methods and the precision and objectivity of measurements. It is not essential to have
only excellent quality data sets, however, it is important that the quality can be estimated. The completeness of the meta-infonnation, including, in particular, the analytical method and the reference
where the data have been published first is crucial
in the understanding of the analytical data. The user
of a specific data set must be able to verify the data
by reading the reference and thus to make a decision about the quality and usefulness of the data.
The manual quality check is supplemented by
an evolving system of generic and parameter-specific validation routines. These routines are based
on the definition of analytical methods and parameters, which requires a given standard unit, possible minimum and maximum values and the possible precision of the data. This infonnation will be
717
detennined from the samples. With the introduction
of new and more efficient analytical methods the
number of parameters and the amount of data obtained has increased by an order of magnitude during the last decade. Some of the parameters are the
proxy data for the calculation of reconstructed
ocean properties (e.g. paleotemperature, paleosalinity, paleoproductivity). Important parameters
(Table 1) are the percentages of different species
of microfossil assemblages, stable isotopes, radionuclides, the biogenic composition of a sediment
or specific elements or elemental ratios. The
results and data are published e.g. in the journal
'Paleoceanography' which is one of the first journals to require that authors store the relevant data
in established databases (Webb et al. 1994).
The recovery and interpretation of proxy data
related to the paleoclimate has traditionally been
carried out by individuals or single laboratories, often examining regional records that cover a limited
temporal domain. The emergence of an integrated
earth systems science calls for a much fuller knowledge of the past, in both space and time, and for
data sets that are drawn as composites from different methods and techniques. The only way to
obtain a useful system for this purpose is to collect
as many proxy data as possible, to store this data
collection in a consistent fonnat and to make it easily
accessible to paleoceanographers. Tools forretrieving the data and for their visualization have to be
closely linked to the collection. This proxy data
collection, implemented in a network between working groups, can then be used as the data source as
well as a common interpretation tool. In addition,
geological proxy data which are used for the definition of ocean boundary conditions have to be used
for model verification (Lorenz et al. 1996). In the
future it could, and should, also be used as a publishing and reference system for data related to new
publications to ensure that all the relevant data are
stored in the same system.
In 1993, scientists from various Gennan
research institutes working in the field of paleoceanography initiated a project in response to
these needs. The goal was the implementation of
an infonnation system which would allow an overview of the sampling material available with the
related meta-infonnation, and which would store
paleoceanographic data of any kind in a consistent
form and make these data easy accessible to
the scientific community. Tools for import/export,
graphical presentation and complex retrievals need
to be closely related to data collection. Based on
the discussion and recommendations of this group,
the infonnation system PANGAEA (PaleoNetwork
for Geological and Environmental Data) was developed at the Alfred Wegener Institute for Polar
and Marine Research (A WI), financed by the German Ministry of Education, Science, Research and
Technology (BMBF) as part of a three year project
( 1994-1997).
This paper gives an overview and a short description ofthe PANGAEA data model, the hardware and software needed, the network concept
and also gives an introduction to the tools for visualizing data. The network is still in the stage of being established in the scientific community. Within
the next two years up to 10 institutes will be connected together to fonn the first real network for
data from research on the variability of climate and
environments.
System Description
The most important generic aspects of PANGAEA
are the quality and availability of the data as well
as the high adaptability and effective use of the
system. Data quality can be described in tenns of
the validity of methods and the precision and objectivity of measurements. It is not essential to have
only excellent quality data sets, however, it is important that the quality can be estimated. The completeness of the meta-infonnation, including, in particular, the analytical method and the reference
where the data have been published first is crucial
in the understanding of the analytical data. The user
of a specific data set must be able to verify the data
by reading the reference and thus to make a decision about the quality and usefulness of the data.
The manual quality check is supplemented by
an evolving system of generic and parameter-specific validation routines. These routines are based
on the definition of analytical methods and parameters, which requires a given standard unit, possible minimum and maximum values and the possible precision of the data. This infonnation will be