Archaeology specific BERT models for English, German, and Dutch” (Brandsen 2024) explores the use of BERT-based models for Named Entity Recognition (NER) in archaeology across three languages: English, German, and Dutch. It introduces six models trained and fine-tuned on archaeological literature, followed by the presentation and evaluation of three models specifically tailored for NER tasks. The focus on multilingualism enhances the applicability of the research, while the meticulous evaluation using standard metrics demonstrates a rigorous methodology.

The introduction of NER for extracting concepts from literature is intriguing, while the provision of a method for others to contribute to BERT model pre-training enhances collaborative research efforts. The innovative use of BERT models to contextualize archaeological data is a notable strength, bridging the gap between digitized information and computational models.

Additionally, the paper's release of fine-tuned models and consideration of environmental implications add further value.

In summary, the paper contributes significantly to the task of NER in archaeology, filling a crucial gap and providing foundational tools for data mining and reevaluating legacy archaeological materials and archives.

Reference

Brandsen, A. (2024). Archaeology specific BERT models for English, German, and Dutch. Zenodo, 8296920, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8296920

Di Palma et alii manuscript delves into applying remote sensing and photogrammetry methods to document and analyze the castrum at the Umm er-Rasas site in Jordan. This research aimed to map all the known archaeological evidence, detect new historical structures, and create a digital archive of the site's features for study and education purposes [1].

Their research has been organized into two phases. The first one consisted of a remote sensing survey and involved collecting historical and modern aerial and satellite imagery, such as:  aerial photographs by Sir Marc Aurel Stein from 1939; panchromatic spy satellite images from the Cold War period (Corona KH-4B and Hexagon KH-9); high and very high resolution (HR and VHR) modern multispectral satellite images (Pléiades-1A and Pléiades Neo-4) [1]. This dataset was processed using the ENVI 4.4 software and applying multiple image-enhancing techniques (Pansharpening, RGB composite, data fusion, and Principal Component Analysis). Then, the resulting images were integrated into a QGIS project, allowing for visual analyses of the site's features and terrain. These investigations provided:

·         a broad overview of the site,

·         the discovery of a previously unknown archaeological feature (the northeastern dam),

·         a stage for targeted ground-level investigations [1].

The project's second phase was dedicated to intensive fieldwork operations, including pedestrian surveys, stratigraphic excavations, and photogrammetric recordings, such as: photographic reconstructions via Structure from Motion (SfM) and laser scanner sessions (using two FARO X330 HDR). In particular, the laser scanner data were processed with Reconstructor 4.4, which provided highly detailed 3D models for the QGIS database. These results were crucial in validating the information acquired during the first phase.

Overall, the paper is well written, with clear objectives and a systematic presentation of the site [2,3,10,11], the research materials, and the study phases. The dataset was described in meticulous detail (especially the remote sensing sources and the laser scanner recordings). The methods implemented in this study are rigorously described [4,5,6,7,8,9] and show a high level of integration between aerial and field techniques. The results are neatly illustrated and fit into the current debates about the efficacy of remote sensing detection and multiscale approaches in archaeological research.

In conclusion, this manuscript significantly contributes to archaeological research, unveiling new and exciting findings about the site of Umm er-Rasas. Its findings and methodologies warrant publication and further exploration.

References:

1.    Di Palma, F., Gabrielli, R., Merola, P., Miccoli, I. and Scardozzi, G. (2024). Study and enhancement of the heritage value of a fortified settlement along the Limes Arabicus. Umm ar-Rasas (Amman, Jordan) between remote sensing analysis, photogrammetry and laser scanner surveys. Zenodo, 8306381, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8306381

2.    Abela J. and Acconci A. (1997), Umm al‐Rasas Kastron Mefa’a. Excavation Campaign 1997. Church of St. Paul: northern and southern flanks. Liber Annus, 47, 484‐488.

3.    Bujard J. (2008), Kastron Mefaa, un bourg à l'époque byzantine: Travaux de la Mission archéologique de la Fondation Max van Berchem à Umm al‐Rasas, Jordanie (1988‐1997), PhD diss., University of Fribourg 2008.

4.    Cozzolino M., Gabrielli R., Galatà P., Gentile V., Greco G., Scopinaro E. (2019), Combined use of 3D metric surveys and non‐invasive geophysical surveys at the stylite tower (Umm ar‐Rasas, Jordan), Annals of geophysics, 62, 3, 1‐9. http://dx.doi.org/10.4401/ag‐8060

5.    Gabrielli R., Salvatori A., Lazzari A., Portarena D. (2016), Il sito di Umm ar‐Rasas – Kastron Mefaa – Giordania. Scavare documentare conservare, viaggio nella ricerca archeologica del CNR. Roma 2016, 236‐240.

6.    Gabrielli R., Portarena D., Franceschinis M. (2017), Tecniche di documentazione dei tappeti musivi del sito archeologico di Umm al‐Rasas Kastron Mefaa (Giordania). Archeologia e calcolatori, 28 (1), 201‐218. https://doi.org/10.19282/AC.28.1.2017.12

7.    Lasaponara R., Masini N. (2012 ed.), Satellite Remote Sensing: A New Tool for Archaeology, New York 2012.

8.    Lasaponara R., Masini N. and Scardozzi G. (2007), Immagini satellitari ad alta risoluzione e ricerca archeologica: applicazioni e casi di studio con riprese pancromatiche e multispettrali di QuickBird. Archeologia e Calcolatori, 18 (2), 187‐227. https://core.ac.uk/download/pdf/33150351.pdf

9.    Lasaponara R., Masini N., Scardozzi G. (2010), Elaborazioni di immagini satellitari ad alta risoluzione e ricognizione archeologica per la conoscenza degli insediamenti rurali del territorio di Hierapolis di Frigia (Turchia). Il dialogo dei Saperi – Metodologie integrate per i Beni Culturali, Edizioni scientifiche italiane, 479‐494.

10. Piccirillo M., Abela J. and Pappalardo C. (2007), Umm al‐Rasas ‐ campagna 2007. Rapporto di scavo. Liber Annus, 57, 660‐668.

11. Poidebard A. (1934), La trace de Rome dans le désert de Syrie : le limes de Trajan à la conquête arabe ; recherches aériennes 1925 – 1932. Paris : Geuthner.

The paper by Xanthopoulos and colleagues [1] presents an approach to establish a pipeline for the analysis of osteological and biochemical data. This approach integrates novel data collection, FAIR principles for data longevity and accessibility, utilises R markdown and cloud webware. Following the changes recommended by the reviewers this paper presents a welcome contribution to osteoarcheology and bioarchaeology.

Osteoarchaeology and bioarchaeology often involves the collection of vast amounts of data both in the field and from consequential analysis in the lab. From this data we can reconstruct many aspects of past human experiences. However, issues often arise when bringing together these diverse types of data. In this regard, this paper proposes are useful methodology in which osteoarchaeological researchers can bring their data together as part of a streamlined process, from data collection to analyses. 

References

[1] Xanthopoulos, K., Georgiadou, A. and Papageorgopoulou, C. (2024). An approach to establishing a workflow pipeline for synergistic analysis of osteological and biochemical data. The case study of Amvrakia in the context of Corinthian colonisation between 625-189 BC in Epirus, Greece. Zenodo, 11156506, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8298579

The paper entitled “Comparing summed probability distributions of shoreline and radiocarbon dates from the Mesolithic Skagerrak coast of Norway” by Isak Roalkvam and Steinar Solheim (2024) sheds new light on the degree to which shoreline dating may be used as a reliable chronological and palaeodemographic proxy in the Mesolthic of southern Norway.

Based on geologically motivated investigations of eustatic and isostatic sea-level changes, shoreline dating has long been used as a method to date archaeological sites in Scandinavia, not least in Norway (e.g., Bjerck 2008; Astrup 2018). Establishing reliable sea-level curves requires much effort and variations across regions may be substantial. While this topic has seen a great deal of attention in Norway specifically, many purely geological questions remain. In addition, dating archaeological sites by linking their elevation to previously established seal-level curves relies strongly on the foundational assumption that such sites were in fact shore-bound. Given the strong contrast between terrestrial and marine productivity in high-latitude regions such as Norway, this assumption per se is not unreasonable. It is very likely that the sea has played a decisive role in the lives of Stone Age peoples throughout (Persson et al. 2017), just as it has in later periods here. However, many confounding factors relating to both taphonomy and human behaviour are also likely to have loosened the shore/site relationship. Systematic variations driven by cultural norms about settlement location, mobility, as well as factors such as shelter construction, fuel use and a range of other possible factors could variously have impacted the validity or at least the precision of shoreline dating.

By developing a new methodology for handling and assessing a large number of shoreline dated sites, Roalkvam and Solheim use state-of-the-art Bayesian statistical methods to compare shoreline and radiocarbon dates as proxies for population activity. The probabilistic treatment of shoreline dates in this way is novel, and the divergences between the two data sets are interpreted by the authors in light of specific behavioural, cultural, and demographic changes. Many of the peaks and troughs observed in these time-series may be interpreted in light of long-observed cultural transitions while others may relate to population dynamics now also visible in palaeogenomic analyses (Günther et al. 2018; Manninen et al. 2021). Overall, this paper makes an innovative and fresh contribution to the use of shoreline dating in Norwegian archaeology, specifically by articulating it with recent developments in Open Science and data-driven approaches to archaeological questions (Marwick et al. 2017).

References

Astrup, P. M. 2018. Sea-Level Change in Mesolithic Southern Scandinavia : Long- and Short-Term Effects on Society and the Environment. Aarhus: Aarhus University Press.

Bjerck, H. B. 2008. Norwegian Mesolithic Trends: A Review. In Mesolithic Europe, edited by Geoff Bailey and Penny Spikins, 60–106. Cambridge: Cambridge University Press.

Günther, T., Malmström, H., Svensson, E. M., Omrak, A., Sánchez-Quinto, F., Kılınç, G. M., Krzewińska, M. et al. 2018. Population Genomics of Mesolithic Scandinavia: Investigating Early Postglacial Migration Routes and High-Latitude Adaptation. PLOS Biology 16 (1): e2003703. https://doi.org/10.1371/journal.pbio.2003703

Manninen, M. A., Damlien, H., Kleppe, J. I., Knutsson, K., Murashkin, A., Niemi, A. R., Rosenvinge, C. S. and Persson, P. 2021. First Encounters in the North: Cultural Diversity and Gene Flow in Early Mesolithic Scandinavia. Antiquity 95 (380): 310–28. https://doi.org/10.15184/aqy.2020.252

Marwick, B., d’Alpoim Guedes, J. A., Barton, C. M., Bates, L. A., Baxter, M., Bevan, A., Bollwerk, E. A. et al. 2017. Open Science in Archaeology. The SAA Archaeological Record 17 (4): 8–14. https://doi.org/10.31235/osf.io/72n8g

Persson, P., Riede, F., Skar, B., Breivik, H. M. and Jonsson, L. 2017. The Ecology of Early Settlement in Northern Europe: Conditions for Subsistence and Survival. Sheffield: Equinox.

Roalkvam, I. and Solheim, S. (2024). Comparing summed probability distributions of shoreline and radiocarbon dates from the Mesolithic Skagerrak coast of Norway, SocArXiv, 2f8ph, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.31235/osf.io/2f8ph

The paper “Transforming the CIDOC-CRM model into a megalithic monument property graph” describes an interesting endeavour of developing and implementing a CIDOC-CRM based knowledge graph using a native graph database (Neo4J) to represent megalithic information (Câmara et al. 2023). While there are earlier examples of using native graph databases and CIDOC-CRM in diverse heritage contexts, the present paper is useful addition to the literature as a detailed description of an implementation in the context of megalithic heritage. The paper provides a demonstration of a working implementation, and guidance for future projects. The described project is also documented to an extent that the paper will open up interesting opportunities to compare the approach to previous and forthcoming implementations. The same applies to the knowledge graph and use of CIDOC-CRM in the project.

Readers interested in comparing available technologies and those who are developing their own knowledge graphs might have benefited of a more detailed description of the work in relation to the current state-of-the-art and what the use of a native graph database in the built-heritage contexts implies in practice for heritage documentation beyond that it is possible and it has potentially meaningful performance-related advantages. While also the reasons to rely on using plain CIDOC-CRM instead of extensions could have been discussed in more detail, the approach demonstrates how the plain CIDOC-CRM provides a good starting point to satisfy many heritage documentation needs.

As a whole, the shortcomings relating to positioning the work to the state-of-the-art and reflecting and discussing design choices do not reduce the value of the paper as a valuable case description for those interested in the use of native graph databases and CIDOC-CRM in heritage documentation in general and the documentation of megalithic heritage in particular.

References

Câmara, A., de Almeida, A. and Oliveira, J. (2023). Transforming the CIDOC-CRM model into a megalithic monument property graph, Zenodo, 7981230, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7981230