Digital Antiquity Data Integration with tDAR The Digital Archaeological Record: The potentials of archaeozoological data integration through tDAR Katherine

Digital Antiquity Data Integration with tDAR

The Digital Archaeological Record:

The potentials of archaeozoological data

integration through tDAR

Katherine Spielmann and Keith Kintigh

Arizona State University

ICAZ 2010


the Digital Archaeological Record, .

• Web-based ingest interface: user/contributors upload data and detailed metadata

• Provides long-term preservation of data & metadata

• Web-based discovery and access for data and documents produced by archaeological projects.

• Provide data integration across inconsistent

databases


A Bit of History• Driven by need for synthetic research

• Genesis of the project (1999) was a long-term collaboration of ASU archaeologists

• Frustrated by the difficulty of• Obtaining data• Integrating data that were collected using

different coding schemes by different investigators

• Teamed up with computer scientists and got funding


Data Ingest


Adding a Project


Enter Resource Metadata


Additional Metadata

• Collection Procedures: • Sampling of site• Sampling of bone assemblage• Screen size• Context (room, trash, hearth, pit)

• Quality of faunal collection• Weathering• % identifiable


Upload Dataset

Digital Antiquity Data Integration with tDAREnter Column Metadata and Attach

Coding Sheet


Coding Sheet Metadata


• Preserve the original data • in original format• in a sustainable format (that can be migrated) • on sustainable media• no transformations of original file’s content • users can always download the data as

recorded

• Preserve database semantics through collection and dissemination of metadata

• Ensure users’ attribution of credit to creators


Creating General Faunal Ontologies


Ontologies

• Ontology is a map of the semantic relationships among a set of concepts.

• In tDAR, ontologies are ordinarily hierarchical (tree-like) and represent an arbitrary number of levels of class-subclass relationships


Burning Intensity

First Level

Second Level

Unburned BurnedProbably Burned

Calcined Charred Singed

Indeter-minate

Not Recorded


Butchering

First Level

Second Level

Probably Butchered

ButcheredIndeter-minate

Cut Marks Saw MarksChop Marks

Not Butchered

Not Recorded

Digital Antiquity Data Integration with tDAROntologies in tDAR


Ontology Mapping

• For a variable to be integrated across databases:• The values for that variable in each

database are mapped to appropriate nodes in the ontology tree• Mapping preferably done by the original

analyst


Ontology Mapping - Example

• Database 1 • Database has taxon value 107• Its coding sheet says taxon 107 = “hare”

• Database 2 • Database has taxon value 237• Coding sheet says 237 = “black-tailed

jackrabbit” described as “Lepus californicus”


Digital Antiquity Data Integration with tDARAssociate Coding Key With

Ontology


Map Coding Key to Ontology

Upper Little Colorado Prehistory Project

Pueblo Blanco


Perform Data Integration:Pilot Analysis


Zuni

Salinas


tDAR Integration

• User queries tDAR to identify databases

• Mark databases to go in user workspace

• Select tables to integrate


Workspace with Bookmarked Databases


Select Tables to Integrate


Select Data Table Columns to Integrate


Specify Aggregation and Filtering


Output

• Output Database• observations from both datasets• integration uses common ontology

values• filtering eliminates cases• aggregation for consistency and

analytical simplicity

• Database is downloaded and analyzed by user.


Integration Output - Species Only


Integration Output – Species & Element


Output Spreadsheet – 2 Sheets


Output Spreadsheet - Combined


Data Table – From SPSS

Species code * DatasetTable Crosstabulation

DatasetTable

TotalPB UCLPP

Species code Artiodactyl Count 2088 5856 7944

% within DatasetTable 40.9% 29.2% 31.6%

Bos/bison Count 26 0 26

% within DatasetTable .5% .0% .1%

Canid Count 31 66 97

% within DatasetTable .6% .3% .4%

Turkey Count 153 698 851


Lagomorph Count 2811 13414 16225


Total Count 5109 20034 25143


Digital Antiquity Data Integration with tDARResource

Depression/Overhunting• How recognize?

•Changing prey abundances • % NISP of large and small taxa• index ratios of large versus small taxa

• Is small game anthropogenic• Burning


•Change in prey demographics• Age of prey

• Fusion

•Change in element frequencies of large game (transport)• FUI

• Increased processing of large game• Condition (% complete)• Weight

Digital Antiquity Data Integration with tDARAcknowledgments

• Andrew W. Mellon Foundation• National Science Foundation• National Endowment for the Humanities• UK Joint Information Systems Committee• Archaeology Data Service, University of York• Digital Antiquity Board of Directors

Sander van der Leeuw, Arizona State University (ASU) [chair]Carol Ackerson, Girl Scouts Arizona Cactus-Pine CouncilJeffrey Altschul, SRI FoundationKim Bullerdick, Owner, BI, L.L.C.John Howard, University College, Dublin

Keith Kintigh, ASUTim Kohler, Washington State UniversityFred Limp, University of ArkansasHarry Papp, L. Roy Papp & Associates Julian Richards, University of YorkDean Snow, The Pennsylvania State University


Questions?http://tdar.org

Documents

Digital Antiquity Data Integration with tDAR The Digital Archaeological Record: The potentials of archaeozoological data integration through tDAR Katherine