A comprehensive database framework for the management and analysis of commingled human remains
Creators
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1.
University of Tübingen
Contributors
Contact person:
Data curator:
Supervisors:
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1.
University of Tübingen
Description
This database template was specially designed for processing and analyzing commingled human remains. It offers the possibility to apply comprehensive analyses and recording methods and to adapt and extend them individually depending on the project. As the input options are based on special codes, several users can work on one project without any typing errors, duplication of data or loss of entries. Currently still for unburnt human remains, but for all types of burials.
The accompanying scripts are designed to help the user to quickly analyses the collected data
Files
Files
(33.2 kB)
| Name | Size | Download all |
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md5:b9a62e200a2ba8227d2b10ca2f9c3a4d
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33.2 kB | Download |
Additional details
Data quality
- Accuracy
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It is a database template that contains all accessible methods from the bioanthropology to work on commingled human remains.The level of accuracy of the individual methods varies, only those with 75% accuracy have been included.
- Completeness
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This database was designed in order that the methods can be updated and adapted to the respective projects at any time by the users themselves (without deeper programming knowledge), thus a completeness when using the database will contra-productive.
- Conformity
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The database follows standard analysis methods in combination with recently published methods and updated versions of older methods. All used methods were listed and cited.
- Consistency
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high consistency
- Credibility
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high credibility
- Processability
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The template can be used as a tool that enables targeted results to be generated quickly, where possible automatically to support the processor.
- Relevance
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The template enables simplified recording without a lot of text through the targeted use of numbers (observations and comments can be documented as usual/reference tables of the individual codes are in the database/the database counts entries automatically so that no duplicate entries are possible), so that automated processing and provision of graphical results of the collected data is possible using statistical programmes (such as R). Enclosed are command scripts in SQL to give the user a view and summary possibility of the data within the database.
- Timeliness
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No restriction
- Understandability
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The structure is user-friendly and reference tables for the individual codes can be found in the database.
Study design and Methodology
- Aggregation method
- count, other, maximum, minimum, percent
- Analysis unit
- other, individual
- Character set
- utf-8, iso-8859-1, ascii, utf-16
- Data source type
- unpublished research data
- Data type
- other, integer
- Date type
- date
- General data format
- numeric, text
- Lifecycle event type
- data collection, data integration
- Mode of collection
- measurements and tests, other
- Numeric type
- integer, count
- Sampling procedure
- total universe/complete enumeration, systematic random
- Software package
- r
- Summary statistic type
- sum, range, minimum, maximum
- Time method
- continuous time series
- Time zone
- utc+02:00, utc+01:00
- Type of frequency
- continuous, unspecified
- Type of instrument
- data collection guidelines, technical instrument(s), programming script
Software documentation
- Application category
- educational software, data analytics and processing software, database management software
- Development status
- work in progress
- Is accessible for free
- Yes
- Operating system
- windows platform, linux platform
- Software requirements
- DB Browser for SQLite, RStudio
- Software suggestions
- README
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It is necessary to install the DB Browser for SQLite software before using the database. Any text editor is suitable for reading the scripts. The folder structure can be extended, but must not be changed, as otherwise the defined paths within the database will not work. These should then be adapted.
- Release notes
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For this version, scripts with R will be made available or delivered later, which, like a pipeline, are intended to provide an automated update from the database to RStudio in order to retrieve the latest data at any time.