Benjamin Ducke’s research while affiliated with German Archaeological Institute, Head Office and other places

Romanisation is a multi-faceted historical phenomenon with profound and lasting cultural impact on the ancient world. In the modern-day territory of Tunisia, this is particularly manifest during the first four centuries AD, under the reign of the Roman Empire. We derive a reduced, operational concept of Romanisation as a cultural diffusion process that is observable in the archaeological remains of the Roman era settlement system. We then introduce a novel mathematical model that computes spatio-temporal approximations for the Romanisation of the settlement system. The model is based on the concept of temporal road activation and makes minimal assumptions regarding input data quality. The results of our study contribute to the understanding of the time dynamics of the region’s road network, under the influence of Romanisation. Our model can be applied in similar archaeological research scenarios, to generate spatio-temporal backbones for the analysis of otherwise intractably complex social processes.

Background: The history of rare diseases is largely unknown. Research on this topic has focused on individual cases of prominent (historical) individuals and artistic (e.g., iconographic) representations. Medical collections include large numbers of specimens that exhibit signs of rare diseases, but most of them date to relatively recent periods. However, cases of rare diseases detected in mummies and skeletal remains derived from archaeological excavations have also been recorded. Nevertheless, this direct evidence from historical and archaeological contexts is mainly absent from academic discourse and generally not consulted in medical research on rare diseases. Results: This desideratum is addressed by the Digital Atlas of Ancient Rare Diseases (DAARD: https://daard.dainst.org ), which is an open access/open data database and web-based mapping tool that collects evidence of different rare diseases found in skeletons and mummies globally and throughout all historic and prehistoric time periods. This easily searchable database allows queries by diagnosis, the preservation level of human remains, research methodology, place of curation and publications. In this manuscript, the design and functionality of the DAARD are illustrated using examples of achondroplasia and other types of stunted growth. Conclusions: As an open, collaborative repository for collecting, mapping and querying well-structured medical data on individuals from ancient times, the DAARD opens new avenues of research. Over time, the number of rare diseases will increase through the addition of new cases from varied backgrounds such as museum collections and archaeological excavations. Depending on the research question, phenotypic or genetic information can be retrieved, as well as information on the general occurrence of a rare disease in selected space-time intervals. Furthermore, for individuals diagnosed with a rare disease, this approach can help them to build identity and reveal an aspect of their condition they might not have been aware of. Thus, the DAARD contributes to the understanding of rare diseases from a long-term perspective and adds to the latest medical research.

Background The history of rare diseases is largely unknown. Research on this topic has focused on individual cases of prominent (historical) individuals and artistic (e.g., iconographic) representations. Medical collections include large numbers of specimens that exhibit signs of rare diseases, but most of these date to relatively recent periods. However, cases of rare diseases detected in mummies and skeletal remains from archaeological excavations have also been recorded. Nevertheless, this direct evidence from historical and archaeological contexts is mainly absent from academic discourse and generally not consulted in medical research on rare diseases. Results This desideratum is addressed by the Digital Atlas of Ancient Rare Diseases (DAARD: https://daard.dainst.org), which is an open access/open data database and web-based mapping tool that collects evidence of different rare diseases found in skeletons and mummies from all over the world and throughout all historic and prehistoric time periods. Disease or individual data, the preservation level of human remains, research methodology, and information about places of curation and literature references are easy to search for. In this manuscript, the design and functionality of the DAARD are illustrated through the example of achondroplasia and other types of stunted growth. Conclusions As an open, collaborative repository for collecting, mapping and querying well-structured medical data on individuals from ancient times, the DAARD opens new research avenues. The number of rare diseases can increase by adding new cases from a variety of backgrounds such as museum collections and archaeological excavations. Depending on the research question, phenotypic or genetic information can be retrieved, as well as information on the general occurrence of a rare disease in selected space–time intervals. Furthermore, for individuals diagnosed with a rare disease, this approach helps them to build identity and reveal an aspect of their rare disease that they may not have been previously aware of. Thus, the DAARD contributes to the understanding of rare diseases from a long-term perspective and adds to the latest relevant research.

The fact that the physical shapes of man-made objects are subject to overlapping influences—such as technological, economic, geographic, and stylistic progressions—holds great information potential. On the other hand, it is also a major analytical challenge to uncover these overlapping trends and to disentagle them in an unbiased way. This paper explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all shape information in a find collection , it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefined archaeological types and analyzing the associated distinguishing characteristics. Recently, series of high-resolution digital representations of artifacts have become available. Such data sets enable the application of extremely sensitive and flexible methods of shape analysis. We explore this potential on a set of 3D models of ancient Greek and Roman sundials, with the aim of providing alternatives to the traditional archaeological method of “trend extraction by ordination” (typology). In the proposed approach, each 3D shape is represented as a point in a shape space —a high-dimensional, curved, non-Euclidean space. Proper consideration of its mathematical properties reduces bias in data analysis and thus improves analytical power. By performing regression in shape space, we find that for Roman sundials, the bend of the shadow-receiving surface of the sundials changes with the latitude of the location. This suggests that, apart from the inscribed hour lines, also a sundial’s shape was adjusted to the place of installation. As an example of more advanced inference, we use the identified trend to infer the latitude at which a sundial, whose location of installation is unknown, was placed. We also derive a novel method for differentiated morphological trend assertion, building upon and extending the theory of geometric statistics and shape analysis. Specifically, we present a regression-based method for statistical normalization of shapes that serves as a means of disentangling parameter-dependent effects (trends) and unexplained variability . In addition, we show that this approach is robust to noise in the digital reconstructions of the artifact shapes.

This paper explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all the shape information in a find collection, it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefined archaeological types and analyzing the associated distinguishing characteristics. Recently, series of high-resolution digital representations of artifacts have become available, and we explore their potential on a set of 3D models of ancient Greek and Roman sundials, with the aim of providing alternatives to the traditional archaeological method of ``trend extraction by ordination'' (typology). In the proposed approach, each 3D shape is represented as a point in a shape space -- a high-dimensional, curved, non-Euclidean space. By performing regression in shape space, we find that for Roman sundials, the bend of the sundials' shadow-receiving surface changes with the location's latitude. This suggests that, apart from the inscribed hour lines, also a sundial's shape was adjusted to the place of installation. As an example of more advanced inference, we use the identified trend to infer the latitude at which a sundial, whose installation location is unknown, was placed. We also derive a novel method for differentiated morphological trend assertion, building upon and extending the theory of geometric statistics and shape analysis. Specifically, we present a regression-based method for statistical normalization of shapes that serves as a means of disentangling parameter-dependent effects (trends) and unexplained variability.

The construction of ancient road networks spanned generations and exhibits temporal path dependence that is not fully captured by established network formation models that are used to support archaeological reasoning. We introduce an evolutionary model that captures explicitly the sequential nature of road network formation: A central feature is that connections are added successively and according to an optimal cost–benefit trade-off with respect to existing connections. In this model, the network topology emerges rapidly from early decisions, a trait that makes it possible to identify plausible road construction orders in practice. Based on this observation we develop a method to compress the search space of path-dependent optimization problems. We use this method to show that the model’s assumptions on ancient decision making allow the reconstruction of partially known road networks from the Roman era in good detail and from sparse archaeological evidence. In particular, we identify missing links in the major road network of ancient Sardinia that are in good agreement with expert predictions.

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The bronze cup found in Dohnsen (Lower Saxony, Northern Germany) in the 1950s is an enigmatic artefact that bears striking similarities with the metalwork of the Late Aegean Bronze Age. We provide an accurate review of the primary sources of information on the cup's find history and context, and present the results of previously unpublished chemical and Pb isotope analyses. The latter suggest that the vessel might have been produced from Central European ores, but it represents the metalworking skills of a travelling smith with knowledge of foreign shapes and styles.

Current consumer-grade UAV technology is economical, highly automated, and well-suited for large area prospection. At the same time, rapid recording of sites and monuments that cannot be preserved or protected in their entirety is becoming a key research topic. We address the critical issue of designing UAV-based workflows to maximize area coverage on a strictly limited time budget. We illustrate our rationale and the evolution of our methods and results with two case studies from Central Asia: the Medieval fortifications in the Oasis of Bukhara (Uzbekistan) and the Orkhon Valley (Mongolia), which contains a multitude of sites preserved as subtle topographic features that spread across an immense area. We discuss the appropriate use of videogrammetry to complement single-shot imagery and to provide rapid and gap-free data coverage. We also review planning tools and provide best practice guidelines (implementable at low cost) for using current, off-the-shelf hardware and software to highest efficiency.

This contribution discusses methods for reconstructing the links of past physical networks, based on archaeological site locations and mathematical models of few parameters. Networks are ubiquitous features of human culture. They structure the geographical patterning of the archaeological record strongly. But while material evidence of networked social interaction is abundant (e.g. similarities in artefact types and technologies), preserved physical remains of the networks (such as roads) are much rarer, making it difficult, if not impossible, to understand the spatial structure of cultural exchange and diffusion. This raises the intractably complex problem of optimal physical network reconstruction, based solely on the known locations of archaeological finds or sites. We address the tractable subproblem of plausible reconstruction of network links. Using a small sample set of Late Bronze Age settlement sites in and around the Mediterranean, we explore model-based network reconstruction from sparse and legacy archaeological data. A hierarchical model is provided by a network-adapted version of Renfrew and Level’s (1979) classic XTENT formula. We show that a GIS-based implementation, that utilises cost surfaces, offers significantly increased realism and analytical advantages. Our approach represents an alternative (with its specific strengths and weaknesses) to more data-intense and computationally complex ap- proaches, such as agent-based models and simulations. The network reconstruction software used in this study (v.net.models) is available under an open source license.