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of samples collected by external parties not trained by MAL personnel can be marked with considerable variation.
4.1.1.2 Component details
Data: Sample metadata
Comments: Highly variable and disparate. Many external projects have had
questionable sample labelling policies, and ineffective structures. A system must be devised which is both efficient and can cater for these
problem samples. The effectiveness of the SEAD must not however, be sacrificed in order to cater for badly designed legacy data.
Much of this data could be in paper form in structured files, but some is entered into MS Excel files during logging and processing.
Status: Current level of digitalisation: 2?
Volume of data: 5 Uniformity of data: 1?
Complexity of task: 5
4.1.2 Laboratory data
4.1.2.1 Summary
The majority of laboratory data is subject to routines which ensure the creation of appropriate metadata and data elements. There are, however inconsistencies in the
storage location and quality of this data, particularly where plant macrofossil analyses are concerned.
An ‘Incoming folder’ holds a master list of the information necessary for identifying sample batches. All samples handled in the laboratory at MAL are given a unique
identification number, referred to as its “MALnummer”, constructed by the concatenation of a sample batch number followed by a sample number. This minimizes
the possibility of mixing up samples from different investigations. These unique identifiers follow the samples through every step of the process, from pre-processing to
reporting and are used for all types of samples processed in the laboratory.
4.1.2.2 Component details
Data related to arrival of samples in lab
Data: Sample batch information: type, size, date received, external contact and
project information. Incoming folder Comments:
System was implemented in 1999 and is used as a temporary store for project meta data. This is digitised for 1999-2003.
Can be used as central node for project information if used consistently, and may be useful when creating project structure in SEAD.
Status: Current level of digitalisation: 2
Volume of data: 2 Uniformity of data: pre-1999: 2?; post-1999: 4
Complexity of task: 3
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Proxy data
Data: Soil chemistryphysical properties
Comments: pre 1996 data – probably must be manually typed into new template
post 1996 data - each MS Excel sheet must be opened, checked, and converted to new template
This is a considerable job due to the massive volume of data once converted to standard MS Excel format these will be importable
into SEAD relatively easily. Status:
Current level of digitalisation: 4 Volume of data: 5
Uniformity of data: 4 Complexity of task: 3
Data: Plant macros
Comments: Degree of digitalisation largely unknown, probably much relatively
recent data that must be extracted from tables in Word, earlier data must be scanned of hand typed.
Creation of a central species list creation system, with templates for raw data would significantly enhance the speed and accuracy of this work.
Systematisation of qualitative descriptors few, many etc. is also needed to an extent
Status: Current level of digitalisation: 2?
Volume of data: 4 Uniformity of data: unknown
Complexity of task: 5
Data: Pollen
Comments: All data must exist digitally either on server or staff machines.
Raw data from the pollen analysis program Tilia can easily be exported to dbf, which can be imported to other database systems or analysis
software such as C2. Questions exist as to whether the species dictionary information is easily
extractable and whether it could be used as a lookupcentral index for plantpollen species.
Older pollen diagrams may have to be scanned and their data reverse engineered if considered of great importance a very time consuming
task. Status:
Current level of digitalisation: 5? Volume of data: 4?
Uniformity of data: 5 Complexity of task:4
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Data: C14 Radiocarbon dates
Comments: There is a central list of all dates submitted by MAL since 1999, which is
digitised up to 2002. Details and metadata sample type etc. for dates are however, not digitised.
Although how samples are referenced in the analogue records varies to some extent, it is always possible to relate dates to samples by their
descriptions. Missing dates may be found in printed reports, although there will
inevitably be some loss of data. Status:
Current level of digitalisation: 2? Volume of data: 3?
Uniformity of data: 2? Complexity of task: 4?
Data: Insects
Comments: Fully digitised since inception in 1996.
In addition, the Bugs database contains records of the majority of fossil insect analyses undertaken in Europe excluding chironomids.
Status: Current level of digitalisation: 5
Volume of data: 1 Uniformity of data: 5
Complexity of task: 1
Data: Mollusca
Comments: Raw data primarily analogue, in Word files where it exists digitally.
Similar situation to plant macrofossils. tatus:
Current level of digitalisation: 2? Volume of data: 1?
Uniformity of data: 3? Complexity of task: 5?
Data: Wood
Comments: Raw data primarily analogue, in Word files where it exists digitally.
Similar situation to plant macrofossils. Status:
Current level of digitalisation: 1? Volume of data: 1?
Uniformity of data: 1? Complexity of task: 5?
Data: Other
Comments: Minimal osteological analyses are undertaken at MAL, and only a limited
amount of data stored outside of reports. Status:
Cooperation with other national labs Stockholm, institutions Lund and consultancies where osteological analyses are undertaken is
routine. These links could be explored with an aim towards database cooperation see section 8
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