Identified (documented) osteological collections represent an important resource in the development of forensic anthropology standards and methods as well as a precious tool for learning and training of practitioners. Even though the number of papers presenting identified collections worldwide increases, many of the collections have still not been divulged to the scientific community in sufficient detail to ascertain their exact number. The Forensic Anthropology Society of Europe (FASE) therefore developed a tool that goes beyond sporadic publications: the FASE Map of Identified Osteological Collections, which is freely accessible and continuously updated and revised. The online map is available at http://forensicanthropology.eu/osteological-collections/. The map of skeletal collections was created in 2017 and currently displays information on 153 identified osteological collections (43 of them categorized as contemporary) located in 41 different countries. This article offers a short analysis of the type, geographical location and content of the collections included in the map. The aim of this article and the map as such is to provide a useful resource to facilitate research planning and teaching in forensic anthropology and related disciplines.

The ability to differentiate individuals based on their biological sex is essential for the creation of an accurate anthropological assessment; it is therefore crucial that the standards that facilitate this are likewise accurate. Given the relative paucity of population-specific anthropological standards formulated specifically for application in the contemporary Australian population, forensic anthropological assessments have historically relied on the application of established methods developed using population geographically and/or temporally disparate. The aim of the present paper is, therefore, to assess the accuracy and reliability of established cranial sex estimation methods, developed from geographically distinct populations, as applied to the contemporary Australian population. Comparison between the original stated accuracy and sex bias values (where applicable) and those achieved after application to the Australian population provides insight into the importance of having anthropological standards optimised for application in specific jurisdictions. The sample analysed comprised computed tomographic (CT) cranial scans of 771 (385 female and 386 male) individuals collected from five Australian states/territories. Cranial CT scans were visualised as three-dimensional volume-rendered reconstructions using OsiriX®. On each cranium, 76 cranial landmarks were acquired, and 36 linear inter-landmark measurements were calculated using MorphDB. A total of 35 predictive models taken from Giles and Elliot (1963), Iscan et al. (1995), Ogawa et al. (2013), Steyn and İşcan (1998) and Kranioti et al. (2008) were tested. Application to the Australian population resulted in an average decrease in accuracy of 21.2%, with an associated sex bias range between − 64.0 and 99.7% (average sex bias value of 29.6%), relative to the original studies. The present investigation has highlighted the inherent inaccuracies of applying models derived from geographically and/or temporally disparate populations. It is, therefore, imperative that statistical models developed from a population consistent with the decedent be used for the estimation of sex in forensic casework.

In the global migration crisis, one of the challenges in the effort to identify deceased migrants is establishing their region of origin, which facilitates the search for ante mortem data to be compared with the post mortem information. This pilot study explores the potential of using stable isotope analysis to distinguish between individuals coming from West Africa and the Horn of Africa. Six individuals (four of known origin and two of unknown origin) were sampled. δ¹³C_{VPDB(keratin)}, δ¹⁵N_{VPDB(keratin)} and δ¹⁸O_{VSMOW(keratin)} of hair were analysed using Elemental Analyzers coupled with Isotope Ratio Mass Spectrometry (IRMS). δ¹⁸O_{VSMOW(carbonate)} and δ¹³C_{VPDB(carbonate)} of bone were analysed using GasBench II with IRMS, while ⁸⁷Sr/⁸⁶Sr composition was determined in bone and dental enamel using laser ablation multi-collector inductively coupled plasma mass spectrometry. The stable isotope compositions of the individual from the Horn of Africa differed from the other individuals. The differences found between ⁸⁷Sr/⁸⁶Sr of enamel and bone and between δ¹⁸O and δ¹³C in bone and hair reflect changes in sources of food and water in accordance with regionally typical migration journeys. The analysis of multiple stable isotopes delivered promising results, allowing us to narrow down the region of origin of deceased migrants and corroborate the information about the migration journey.

Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which they were originally developed with. This study explores an automatic deep learning (DL) framework to enhance sex estimation accuracy and reduce bias. Utilising 200 cranial CT scans of Indonesian individuals, various DL network configurations were evaluated against a human observer. The most accurate DL network, which learned to estimate sex and cranial traits as an auxiliary task, achieved a classification accuracy of 97%, outperforming the human observer at 82%. Grad-CAM visualisations indicated that the DL model appears to focus on certain cranial traits, while also considering overall size and shape. This study demonstrates the potential of using DL to assist forensic anthropologists in providing more accurate and less biased estimations of skeletal sex.