This is a fascinating paper for anyone interested in network analysis or the chronology and cultures of the case study, namely the Late prehistoric burial sites in Dorset, for which the author’s approach allowed a new perspective over an already deeply studied area [1]. This paper's implementation of Similarity Network Fusion (SNF) is noteworthy. This method is typically utilized within genetic research but has yet to be employed in Archaeology. SNF has the potential to benefit Archaeology due to its unique capabilities and approach significantly. 

The author exhibits a deep and thorough understanding of previous investigations concerning material and similarity networks while emphasizing the innovative nature of this particular study. The SNF approach intends to improve a lack of the most used (in Archaeology) similarity coefficient, the Brainerd-Robinson, in certain situations, mainly in heterogenous and noisy datasets containing a small number of samples but a large number of measurements, scale differences, and collection biases, among other things. The SNF technique, demonstrated in the case study, effectively incorporates various similarity networks derived from different datatypes into one network. 

As shown during the Dorset case study, the SNF application has a great application in archaeology, even in already available data, allowing us to go further and bring new visions to the existing interpretations. As stated by the author, SNF shows its potential for other applications and fields in archaeology coping with similar datasets, such as archaeobotany or archaeozoology, and seems to complement different multivariate statistical approaches, such as correspondence or cluster analysis.

This paper has been subject to two excellent revisions, which the author mostly accepted. One of the revisions was more technical, improving the article in the metadata part, data availability and clarification, etc. Although the second revision was more conceptual and gave some excellent technical inputs, it focused more on complementary aspects that will allow the paper to reach a wider audience. I vividly recommend its publication.

References

[1] Geitlinger, T. (2024). Similarity Network Fusion: Understanding Patterns and their Spatial Significance in Archaeological Datasets. Zenodo, 7998239, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7998239

This well-written and interesting paper ‘Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys’ deals with the development of a ‘modular, accessible, and simple web-based platform for survey planning and quality assurance’ in the area of pedestrian field survey methods (Banning et al. 2024).

Although there have been excellent treatments of statistics in archaeological field survey (among which various by the first author: Banning 2020, 2021), and there is continuous methodological debate on platforms such as the International Mediterranean Survey Workshop (IMSW), in papers dealing with the current development and state of the field (Knodell et al. 2023), good practices (Attema et al. 2020) or the merits of a quantifying approach to archaeological densities (cf. de Haas et al. 2023), this paper rightfully addresses the lack of rigorous statistical approaches in archaeological field survey. As argued by several scholars such as Orton (2000), this mainly appears the result of lack of knowledge/familiarity/resources to bring in the required expertise etc. with the application of seemingly intricate statistics (cf. Waagen 2022). In this context this paper presents a welcome contribution to the feasibility of a robust archaeological field survey design. 

The SPACE application, under development by the authors, is introduced in this paper. It is a software tool that aims to provide different modules to assist archaeologists to make calculations for sample size, coverage, stratification, etc. under the conditions of survey goals and available resources. In the end, the goal is to ensure archaeological field surveys will attain their objectives effectively and permit more confidence in the eventual outcomes. The module concerning Sweep Widths, an issue introduced by the main author in 2006 (Banning 2006) is finished; the sweep width assessment is a methodology to calibrate one’s survey project for artefact types, landscape, visibility and person-bound performance, eventually increasing the quality (comparability) of the collected samples. This is by now a well-known calibration technique, yet little used, so this effort to make that more accessible is certainly laudable. An excellent idea, and another aim of this project, is indeed to build up a database with calibration data, so applying sweep-width corrections will become easier accessible to practitioners who lack time to set up calibration exercises. 

It will be very interesting to have a closer look at the eventual platform and to see if, and how, it will be adapted by the larger archaeological field survey community, both from an academic research perspective as from a heritage management point of view. I happily recommend this paper and all debate relating to it, including the excellent peer reviews of the manuscript by Philip Verhagen and Tymon de Haas (available as part of this PCI recommendation procedure), to any practitioner of archaeological field survey.

References

Attema, P., Bintliff, J., Van Leusen, P.M., Bes, P., de Haas, T., Donev, D., Jongman, W., Kaptijn, E., Mayoral, V., Menchelli, S., Pasquinucci, M., Rosen, S., García Sánchez, J., Luis Gutierrez Soler, L., Stone, D., Tol, G., Vermeulen, F., and Vionis. A. 2020. “A guide to good practice in Mediterranean surface survey projects”, Journal of Greek Archaeology 5, 1–62. https://doi.org/10.32028/9781789697926-2

Banning, E.B., Alicia L. Hawkins, S.T. Stewart, Sweep widths and the detection of artifacts in archaeological survey, Journal of Archaeological Science, Volume 38, Issue 12, 2011, Pages 3447-3458. https://doi.org/10.1016/j.jas.2011.08.007 

Banning, E.B. 2020. Spatial Sampling. In: Gillings, M., Hacıgüzeller, P., Lock, G. (eds.) Archaeological Spatial Analysis. A Methodological Guide. Routledge.

Banning, E.B. 2021. Sampled to Death? The Rise and Fall of Probability Sampling in Archaeology. American Antiquity, 86(1), 43-60. https://doi.org/10.1017/aaq.2020.39

Banning, E. B. Steven Edwards, & Isaac Ullah. (2024). Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys. Zenodo, 8072178, ver. 9 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8072178

Knodell, A.R., Wilkinson, T.C., Leppard, T.P. et al. 2023. Survey Archaeology in the Mediterranean World: Regional Traditions and Contributions to Long-Term History. J Archaeol Res 31, 263–329 (2023). https://doi.org/10.1007/s10814-022-09175-7 

Orton, C. 2000. Sampling in Archaeology. Cambridge University Press. https://doi.org/10.1017/CBO9781139163996

Waagen, J. 2022. Sampling past landscapes. Methodological inquiries into the bias problems of recording archaeological surface assemblages. PhD-Thesis. https://hdl.handle.net/11245.1/e9cb922c-c7e4-40a1-b648-7b8065c46880 

de Haas, T., Leppard, T. P., Waagen, J., & Wilkinson, T. (2023). Myopic Misunderstandings? A Reply to Meyer (JMA 35(2), 2022). Journal of Mediterranean Archaeology, 36(1), 127-137. https://doi.org/10.1558/jma.27148

After a thorough review and consideration of the revised manuscript titled "Cultural Significance Assessment of Archaeological Sites for Heritage Management: From Text to Spatial Networks of Meanings" by Yael Alef and Yuval Shafriri [1], I am recommending the paper for publication. The authors have made significant strides in addressing the feedback from the initial review process, notably enhancing the manuscript's clarity, methodological detail, and overall contribution to the field of Archaeological Resource Management (ARM).

On balance I think the paper competently navigates the shift from a traditional significance-focused assessment of isolated archaeological sites to a more holistic and interconnected approach, leveraging graph data models and spatial networks. This transition represents an advancement in the field, offering deeper insights into the sociocultural dynamics of archaeological sites. The case study of ancient synagogues in northern Israel, particularly the Huqoq Synagogue, serves as a compelling illustration of the potential of semantic technologies to enrich our understanding of cultural heritage.

Significantly, the authors have responded to the call for a clearer methodological framework by providing a more detailed exposition of their use of knowledge graph visualization and semantic technologies. This response not only strengthens the paper's scientific rigor but also enhances its accessibility and applicability to a broader audience within the conservation and heritage management community.

However, I do think it remains important to acknowledge areas where further work could enrich the paper's contribution. While the manuscript makes notable advancements in the technical and methodological domains, the exploration of the ethical and political implications of semantic technologies in ARM remains less developed. Recognizing the complex interplay of ethical and political considerations in archaeological assessments is crucial for the responsible advancement of the field. Thus, I suggest that future work could productively focus on these dimensions, offering a more comprehensive view of the implications of integrating semantic technologies into heritage management practices. I don't think that this omission is a reason to withold the paper for publication or seek further review. In fact I think it stands alone a paper quite well. Perhaps the authors might consider this as a complementary line of inquiry in their future work in the field.

In conclusion then, I believe the revised manuscript represents a valuable addition to the literature, pushing boundaries of how we assess, understand, and manage archaeological resources. Its focus on semantic technologies and the creation of spatial networks of meanings marks a significant step forward in the field. I believe its publication will stimulate further research and discussion, particularly in the realms of ethical and political considerations, which remain ripe for exploration. Therefore, I'm happy to endorse the publication of this manuscript.

Reference

[1] Alef, Y and Shafriri, Y. (2024). Cultural Significance Assessment of Archaeological Sites for Heritage Management: From Text of Spatial Networks of Meanings. Zenodo, 8309992, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8309992

In a recent article, Fumihiro Sakahira and Hiro'omi Tsumura (2023) used social network analysis methods to analyze change in obsidian trade networks in Japan throughout the 13,000-year-long Jomon period. In the paper recommended here (Sakahira and Tsumura 2024), Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction they revisit that data and describe additional analyses that confirm the robustness of their social network analysis. The data, analysis methods, and substantive conclusions of the two papers overlap; what this new paper adds is a detailed examination of the data and methods, including use of bootstrap analysis to demonstrate the reasonableness of the methods they used to group sites into clusters.

Both papers begin with a large dataset of approximately 21,000 artifacts from more than 250 sites dating to various times throughout the Jomon period. The number of sites and artifacts, varying sample sizes from the sites, as well as the length of the Jomon period, make interpretation of the data challenging. To help make the data easier to interpret and reduce problems with small sample sizes from some sites, the authors assign each site to one of five sub-periods, then define spatial clusters of sites within each period using the DBSCAN algorithm. Sites with at least three other sites within 10 km are joined into clusters, while sites that lack enough close neighbors are left as isolates. Clusters or isolated sites with sample sizes smaller than 30 were dropped, and the remaining sites and clusters became the nodes in the networks formed for each period, using cosine similarities of obsidian assemblages to define the strength of ties between clusters and sites.

The main substantive result of Sakahira and Tsumura’s analysis is the demonstration that, during the Middle Jomon period (5500-4500 cal BP), clusters and isolated sites were much more connected than before or after that period. This is largely due to extensive distribution of obsidian from the Kozu-shima source, located on a small island off the Japanese mainland. Before the Middle Jomon period, Kozu-shima obsidian was mostly found at sites near the coast, but during the Middle Jomon, a trade network developed that took Kozu-shima obsidian far inland. This ended after the Middle Jomon period, and obsidian networks were less densely connected in the late and last Jomon periods.

The methods and conclusions are all previously published (Sakahira and Tsumura 2023). What Sakahira and Tsumura add in Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction are:

·       an examination of the distribution of cosine similarities between their clusters for each period

·       a similar evaluation of the cosine similarities within each cluster (and among the unclustered sites) for each period

·       bootstrap analyses of the mean cosine similarities and network densities for each time period

These additional analyses demonstrate that the methods used to cluster sites are reasonable, and that the use of spatially defined clusters as nodes (rather than the individual sites within the clusters) works well as a way of reducing bias from small, unrepresentative samples. An alternative way to reduce that bias would be to simply drop small assemblages, but that would mean ignoring data that could usefully contribute to the analysis.

The cosine similarities between clusters show patterns that make sense given the results of the network analysis. The Middle Jomon period has, on average, the highest cosine similarities between clusters, and most cluster pairs have high cosine similarities, consistent with the densely connected, spatially expansive network from that time period. A few cluster pairs in the Middle Jomon have low similarities, apparently representing comparisons including one of the few nodes on the margins on the network that had little or no obsidian from the Kozu-shima source. The other four time periods all show lower average inter-cluster similarities and many cluster pairs have either high or low similarities. This probably reflects the tendency for nearby clusters to have very similar obsidian assemblages to each other and for geographically distant clusters to have dissimilar obsidian assemblages. The pattern is consistent with the less densely connected networks and regionalization shown in the network graphs. Thinking about this pattern makes me want to see a plot of the geographic distances between the clusters against the cosine similarities. There must be a very strong correlation, but it would be interesting to know whether there are any cluster pairs with similarities that deviate markedly from what would be predicted by their geographic separation.

The similarities within clusters are also interesting. For each time period, almost every cluster has a higher average (mean and median) within-cluster similarity than the similarity for unclustered sites, with only two exceptions. This is partial validation of the method used for creating the spatial clusters; sites within the clusters are at least more similar to each other than unclustered sites are, suggesting that grouping them this way was reasonable.

Although Sakahira and Tsumura say little about it, most clusters show quite a wide range of similarities between the site pairs they contain; average within-cluster similarities are relatively high, but many pairs of sites in most clusters appear to have low similarities (the individual values are not reported, but the pattern is clear in boxplots for the first four periods). There may be value in further exploring the occurrence of low site-to-site similarities within clusters. How often are they caused by small sample sizes? Clusters are retained in the analysis if they have a total of at least 30 artifacts, but clusters may contain sites with even smaller sample sizes, and small samples likely account for many of the low similarity values between sites in the same cluster. But is distance between sites in a cluster also a factor? If the most distant sites within a spatially extensive cluster are dissimilar, subdividing the cluster would likely improve the results. Further exploration of these within-cluster site-to-site similarity values might be worth doing, perhaps by plotting the similarities against the size of the smallest sample included in the comparison, as well as by plotting the cosine similarity against the distance between sites. Any low similarity values not attributable to small sample sizes or geographic distance would surely be worth investigating further.

Sakahira and Tsumura also use a bootstrap analysis to simulate, for each time period, mean cosine similarities between clusters and between site pairs without clustering. They also simulate the network density for each time period before and after clustering. These analyses show that, almost always, mean simulated cosine similarities and mean simulated network density are higher after clustering than before. The simulated mean values also match the actual mean values better after clustering than before. This improved match to actual values when the sites are clustered for the bootstrap reinforces the argument that clustering the sites for the network analysis was a reasonable result.

The strength of this paper is that Sakahira and Tsumura return to reevaluate their previously published work, which demonstrated strong patterns through time in the nature and extent of Jomon obsidian trade networks. In the current paper they present further analyses demonstrating that several of their methodological decisions were reasonable and their results are robust. The specific clusters formed with the DBSCAN algorithm may or may not be optimal (which would be unreasonable to expect), but the authors present analyses showing that using spatial clusters does improve their network analysis. Clustering reduces problems with small sample sizes from individual sites and simplifies the network graphs by reducing the number of nodes, which makes the results easier to interpret.

Reference

Sakahira, F. and Tsumura, H. (2023). Tipping Points of Ancient Japanese Jomon Trade Networks from Social Network Analyses of Obsidian Artifacts. Frontiers in Physics 10:1015870. https://doi.org/10.3389/fphy.2022.1015870

Sakahira, F. and Tsumura, H. (2024). Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction, Zenodo, 10057602, ver. 7 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7969330

The paper “A Focus on the Future of our Tiny Piece of the Past: Digital Archiving of a Long-term Multi-participant Regional Project” (Madry et al., 2023) describes practices, challenges and opportunities encountered in digital archiving of a landscape research project running in Burgundy, France for more than 45 years. As an unusually long-running multi-disciplinary undertaking working with a large variety of multi-modal digital and non-digital data, the Burgundy project has lived through the development of documentation and archiving technologies from the 1970s until today and faced many of the challenges relating to data management, preservation and migration.

The major strenght of the paper is that it provides a detailed description of the evolution of digital data archiving practices in the project including considerations about why some approaches were tested and abandoned. This differs from much of the earlier literature where it has been more common to describe individual solutions how digital archiving was either planned or was performed at one point of time. A longitudinal description of what was planned, how and why it has worked or failed so far, as described in the paper, provides important insights in the everyday hurdles and ways forward in digital archiving. As a description of a digital archiving initiative, the paper makes a valuable contribution for the data archiving scholarship as a case description of practices and considerations in one research project. For anyone working with data management in a research project either as a researcher or data manager, the text provides useful advice on important practical matters to consider ahead, during and after the project. The main advice the authors are giving, is to plan and act for data preservation from the beginning of the project rather than doing it afterwards. To succeed in this, it is crucial to be knowledgeable of the key concepts of data management—such as “digital data fixity, redundant backups, paradata, metadata, and appropriate keywords” as the authors underline—including their rationale and practical implications. The paper shows also that when and if unexpected issues raise, it is important to be open for different alternatives, explore ways forward, and in general be flexible.

The paper makes also a timely contribution to the discussion started at the session “Archiving information on archaeological practices and work in the digital environment: workflows, paradata and beyond” at the Computer Applications and Quantitative 2023 conference in Amsterdam where it was first presented. It underlines the importance of understanding and communicating the premises and practices of how data was collected (and made) and used in research for successful digital archiving, and the similar pertinence of documenting digital archiving processes to secure the keeping, preservation and effective reuse of digital archives possible.

References

Madry, S., Jansen, G., Murray, S., Jones, E., Willcoxon, L. and Alhashem, E. (2023) A Focus on the Future of our Tiny Piece of the Past: Digital Archiving of a Long-term Multi-participant Regional Project, Zenodo, 7967035, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7967035