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Department of Archaeology

 
Read more at: ArchBiMod – Agent-Based Modelling to assess the quality and bias of the archaeological record

ArchBiMod – Agent-Based Modelling to assess the quality and bias of the archaeological record

Archaeological data is often biased and incomplete. This is a well-known issue for most archaeologists. Although studies of specific sites and small regions can have this into account, the effect of this problem increases exponentially as archaeologists expand their chronological and geographic frame, and try to answer questions related to general dynamics and broad human processes.


Read more at: EHSCAN-Exploring Early Holocene Saharan Cultural Adaptation and Social Networks through socio-ecological inferential modelling

EHSCAN-Exploring Early Holocene Saharan Cultural Adaptation and Social Networks through socio-ecological inferential modelling

EHSCAN is a Horizon-MSCA-2022-PF scheme Fellowship Funded by UKRI and hosted by the McDonald Institute for Archaeological Research, University of Cambridge. 


Read more at: Mapping Africa’s Endangered Archaeological Sites and Monuments

Mapping Africa’s Endangered Archaeological Sites and Monuments

The Mapping Africa’s Endangered Archaeological Sites and Monuments (MAEASaM) project, funded by Arcadia charitable foundation, is documenting and compiling a trans-national inventory of Africa’s rich archaeological heritage, including many previously unidentified sites and monuments. Particular emphasis is being given to mapping and recording sites under threat, whether from urban growth, conflict, sea-level change or infrastructure development, among other adverse impacts.


Read more at: Mapping Archaeological Heritage in South Asia

Mapping Archaeological Heritage in South Asia

The Mapping Archaeological Heritage in South Asia (MAHSA) project, now in its Phase 2, will continue to document the endangered archaeology and cultural heritage of the Indus River Basin and the surrounding areas and publish this information in an Open Access Arches geospatial database. Over the course of Phase 2, the project will expand its scope to include the Ganges River Basin, Baluchistan and the coastal areas of India and Pakistan.


Read more at: Neanderthals as engineers? Investigating the link between tool design, functionality and use

Neanderthals as engineers? Investigating the link between tool design, functionality and use

Stone tool artefacts represent the only continuous material record from early hominins across a period of three million years. Lithics provide information about early human technological adaptations and innovations, and in turn, understanding these technologies allows insights into early human behaviour. This assessment is based on the fact that lithic artefacts reflect (un-)conscious decision-making. Tool design, for instance, is characterised by the selection of the raw material and choices about overall tool morphology, edge retouch, and other factors.


Read more at: Palaeoanalytics

Palaeoanalytics

Human evolution is a central research area in biology and anthropology and has a history of research going back more than 150 years. For most of that time, evidence has come from digging up fossils and archaeological remains. Research in human evolution has been transformed by the impact of genomics and the development of ancient DNA methodologies, producing new insights into past demography, dispersal and admixture patterns, social behaviour, selection, disease history, and more.


Read more at: The Cambridge Heritage Science Hub Initiative (CHERISH)

The Cambridge Heritage Science Hub Initiative (CHERISH)

Cambridge is home to world-leading researchers across archaeological science, technical art history and heritage science, based at Department of Archaeology, the Fitzwilliam Museum, and the Hamilton Kerr Institute, among others. There are multiple synergies across these institutions in terms of research methodologies, goals and ambitions in the field of technical and scientific investigation of works of art and archaeological objects.


Read more at: Transitions in early stone tool technologies: a computer vision and machine learning approach

Transitions in early stone tool technologies: a computer vision and machine learning approach

The transition from Oldowan to Acheulean technologies are hypothesised to be concomitant with advances in cognition and behaviour. However, the nature of these shifts, and their cultural and evolutionary implications are poorly defined and understood. While extensive literature exists on these technologies, significant differences in research methods and traditions make comparative and comprehensive analyses problematic.


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