The paper under review/recommendation deals with Corpus Nummorum (Peter et al. 2024). The Corpus Nummorum (CN) is web portal for ancient Greek coins from various collections (https://www.corpus-nummorum.eu/). The CN is a database and research tool for Greek coins dating between 600 BCE to 300 CE. While many traditional collection databases aim at collecting coins, CN also includes coin dies, coin types and issues. It aims at achieving a complete online coin type catalogue. The paper is not a paper in a traditional sense, but presents the CN as a tool and shows the functionalities in the system. The relevance and the possibilities of the CN for numismatists is made clear in the paper and the merits are clear even for me as a Roman archaeologist and non-numismatist.

The CN was presented as a poster at the CAA 2023 in Amsterdam during “S03. Our Little Minions pt. V: small tools with major impact”, organized by Moritz Mennenga, Florian Thiery, Brigit Danthine and myself (Mennenga et al. 2023). Little Minions help us significantly in our daily work as small self-made scripts, home-grown small applications and small hardware devices. They often reduce our workload or optimize our workflows, but are generally under-represented during conferences and not often presented to the outside world. Therefore, the Little Minions form a platform that enables researchers and software engineers to share these tools (Thiery, Visser and Mennenga 2021). Little Minions have become a well known happening within the CAA-community since we started this in 2018, also because we do not only allow 10-minute lightning talks, but also spontaneous stand-up presentations during the conference. A full list of all minions presented in the past, can be found online: https://caa-minions.github.io/minions/. In a strict sense the CN would not count as a Little Minion, because it is a large project consisting of many minions that help a numismatist in his/her daily work. The CN seems a very Big Minion in that sense.

Personally, I am very happy to see the database being developed as a fully open system and that code can be found on Github (https://github.com/telota/corpus-nummorum-editor), and also made citable with citation information in GitHub (see https://citation-file-format.github.io/) and a version deposited in Zenodo with DOI (Köster and Franke 2024). In addition, the authors claim that the CN will be shared based on the FAIR-principles (Wilkinson et al. 2016, 2019). These guidelines are developed to improve the Findability, Accessibility, Interoperability, and Reuse of digital data. I feel that CN will be a way forward in open numismatics and open archaeology.

The CN is well known within the numismatist community and it was hard to find reviewers in this close community, because many potential reviewers work together with one or more of the authors, or are involved in the project. This also proves the relevance of the CN to the research community and beyond. Luckily, a Roman numismatist and a specialist in digital/computational archaeology were able to provide valuable feedback on the current paper. The reviewers only submitted feedback on the first version of the paper (Peter et al. 2023).

The numismatist was positive on the content and the usefulness of CN for the discipline in general. However, she pointed out some important points that need to be addressed. The digital specialist was positive is various aspects, but also raised some important issues in relation to technical aspects and the explanation thereof. While both were positive on the project and the paper in general, both reviewers pointed out some issues that were largely addressed in the second version of this paper. The revised version was edited throughout and the paper was strongly improved.

The Corpus Nummorum is well presented in this easy to read paper, although the explanations can sometimes be slightly technical. This paper gives a good introduction to the CN and I recommend this for publication. I sincerely hope that the CN will contribute and keep on contributing to the domains of numismatics, (digital) archaeology and open science in general.

References

Köster, J and Franke, C. 2024 Corpus Nummorum Editor. https://doi.org/10.5281/zenodo.10458195

Mennenga, M, Visser, RM, Thiery, F and Danthine, B. 2023 S03. Our Little Minions pt. V: small tools with major impact. In:. Book of Abstracts. CAA 2023: 50 Years of Synergy. Amsterdam: Zenodo. pp. 249–251. https://doi.org/10.5281/ZENODO.7930991

Peter, U, Franke, C, Köster, J, Tolle, K, Gampe, S and Stolba, VF. 2023 CORPUS NUMMORUM – A Digital Research Infrastructure for Ancient Coins. https://doi.org/10.5281/ZENODO.8263518

Peter, U., Franke, C., Köster, J., Tolle, K., Gampe, S. and Stolba, V. F. (2024). CORPUS NUMMORUM – A Digital Research Infrastructure for Ancient Coins, Zenodo, 8263517, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8263517

Thiery, F, Visser, RM and Mennenga, M. 2021 Little Minions in Archaeology An open space for RSE software and small scripts in digital archaeology. https://doi.org/10.5281/ZENODO.4575167

Wilkinson, MD, Dumontier, M, Aalbersberg, IjJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J-W, da Silva Santos, LB, Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJG, Groth, P, Goble, C, Grethe, JS, Heringa, J, ’t Hoen, PAC, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S-A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, van der Lei, J, van Mulligen, E, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2016 The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3(1): 160018. https://doi.org/10.1038/sdata.2016.18

Wilkinson, MD, Dumontier, M, Jan Aalbersberg, I, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J-W, da Silva Santos, LB, Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJG, Groth, P, Goble, C, Grethe, JS, Heringa, J, Hoen, PAC ’t, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S-A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, van der Lei, J, van Mulligen, E, Jan Velterop, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2019 Addendum: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 6(1): 6. https://doi.org/10.1038/s41597-019-0009-6

This paper [1] presents an innovative approach to address the prevalent challenge of unbalanced datasets in coin type recognition, shifting the focus from coin class type recognition to coin mint recognition. Despite this shift, the issue of unbalanced data persists. To mitigate this, the authors introduce a method to split larger classes into smaller ones, integrating them into an 'additional class layer'.

Three distinct machine learning (ML) methodologies were employed to identify new possible classes, with one approach utilising unsupervised clustering alongside manual intervention, while the others leverage object detection, and Natural Language Processing (NLP) techniques. However, despite these efforts, overfitting remained a persistent issue, prompting the authors to explore alternative methods such as dataset improvement and Generative Adversarial Networks (GANs).

The paper contributes significantly to the intersection of ML techniques and archaeology, particularly in addressing overfitting challenges. Furthermore, the authors' candid acknowledgment of the limitations of their approaches serves as a valuable resource for researchers encountering similar obstacles.

This study stems from the D4N4 project, aimed at developing a machine learning-based coin recognition model for the extensive "Corpus Nummorum" dataset, comprising over 19,600 coin types and 49,000 coins from various ancient landscapes. Despite encountering challenges with overfitting due to the dataset's imbalance, the authors' exploration of multiple methodologies and transparent documentation of their limitations enriches the academic discourse and provides a foundation for future research in this field.

Reference

[1] Gampe, S. and Tolle, K. (2024). Creating an Additional Class Layer with Machine Learning to counter Overfitting in an Unbalanced Ancient Coin Dataset. Zenodo, 8298077, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8298077

The paper outlines the ClaReNet project's exploration of computer-based methods for classifying Celtic coin series, specifically focusing on a hoard from Jersey [1]. They collaborated with Jersey Heritage and numismatists, utilising a large dataset of coin images. The process involves stages such as pre-sorting, size-based sorting, class/type identification, and die studies. They employed IT methods, including object detection and unsupervised learning, followed by supervised learning for data refinement. Collaboration with numismatic experts ensured data quality. The study highlighted challenges in classifying coins, suggesting techniques like image matching alongside convolutional neural networks (CNNs). The results demonstrate the efficacy of semi-automatic processes in coin classification, emphasising the importance of human-computer collaboration for successful outcomes.

Overall, this is a good paper, showing how we as archaeologists and numismatics can use existing tools and finetune them for our purposes; without the need for huge domain specific datasets. This research and related papers show how we can more effectively deal with the increasingly bigger data we deal with, saving time on the monotonous and labour intensive tasks, leaving us more time to deal with the big picture. An important strength of the work is the provided public software repository and the dataset. The paper is well written, and a number of images illustrate the methodology as well as the objects used.

Reference

[1] Deligio, C., Tolle, K., and Wigg-Wolf, D. (2024). Supporting the analysis of a large coin hoard with AI-based methods. Zenodo, 8301464, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8301464

The paper (Callieri, M. et al. 2023) describes the “Dynamic Collections” project, an online platform initially created to showcase digital archaeological collections of Lund University. During a phase of testing by department members, new functionalities and artefacts were added resulting in an interactive platform adapted to university-level teaching and learning. The paper introduces into the topic and related works after which it starts to explain the project itself. The idea is to resemble the possibilities of interaction of non-digital collections in an online platform. Besides the objects themselves, the online platform offers annotations, measurement and other interactive tools based on the already known 3DHOP framework. With the possibility to create custom online collections a collaborative working/teaching environment can be created.

The already wide-spread use of the 3DHOP framework enabled the authors to develop some functionalities that could be used in the “Dynamic Collections” project. Also, current and future plans of the project are discussed and will include multiple 3D models for one object or permanent identifiers, which are both important additions to the system. The paper then continues to explain some of its further planned improvements, like comparisons and support for teaching, which will make the tool an important asset for future university-level education.

The paper in general is well-written and informative and introduces into the interactive tool, that is already available and working. It is very positive, that the authors rely on up-to-date methodologies in creating 3D online repositories and are in fact improving them by testing the tool in a teaching environment. They mention several times the alignment with upcoming EU efforts related to the European Collaborative Cloud for Cultural Heritage (ECCCH), which is anticipatory and far-sighted and adds to the longevity of the project. Comments of the reviewers were reasonably implemented and led to a clearer and more concise paper. I am very confident that this tool will find good use in heritage research and presentation as well as in university-level teaching and learning.

Although the authors never answer the introductory question explicitly (What characteristics should a virtual environment have in order to trigger dynamic interaction?), the paper gives the implicit answer by showing what the "Dynamic Collections" project has achieved and is able to achieve in the future.

Bibliography

Callieri, M., Berggren, Å., Dell'Unto, N., Derudas, P., Dininno, D., Ekengren, F., and Naponiello, G. (2023). The Dynamic Collections – a 3D Web Platform of Archaeological Artefacts designed for Data Reuse and Deep Interaction, Zenodo, 10067103, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.10067103

The paper entitled 'Social Network Analysis, Community Detection Algorithms, and Neighbourhood Identification in Pompeii' [1] presents a significant contribution to the field of archaeological network research, particularly in the challenging task of identifying urban neighborhoods within the context of Pompeii. This study focuses on the relational dynamics within urban neighborhoods and examines their indistinct boundaries through advanced analytical methods. The methodology employed provides a comprehensive analysis of community detection, including the Louvain and Leiden algorithms, and introduces a novel Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm. The incorporation of a network approach into this domain is both innovative and timely.

The potential impact of this research is substantial, offering new perspectives and analytical tools. This opens new avenues for understanding social structures in ancient urban settings, which can be applied to other archaeological contexts beyond Pompeii. Moreover, the manuscript is not only methodologically solid but also well-written and structured, making complex concepts accessible to a broad audience.

In conclusion, this study represents a valuable contribution to the field of archaeology, particularly for archaeological network research. Their results not only enhance our knowledge of Pompeii but also provide a robust framework for future studies in similar historical contexts. Therefore, this publication advances our understanding of social dynamics in historical urban environments. The rigorous analysis, combined with the innovative application of network algorithms, makes this study a noteworthy addition to the existing body of network science literature. It is recommended for a wide range of scholars interested in the intersection of archaeology, history, and network science.

Reference

[1] Notarian, Matthew. 2024. Social Network Analysis, Community Detection Algorithms, and Neighbourhood Identification in Pompeii. https://doi.org/10.5281/zenodo.8305968