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 by Plutniak [1] presents a new method that uses refitting to help interpret stratigraphy using the topological distribution of conjoinable material culture. This new method opens up new avenues to the archaeological use of network analysis but also to assess the integrity of interpreted excavation layers. Beyond its evident applicability to standard excavation practice, the paper presents a series of characteristics that exemplify archaeological publication best practices and, as someone more versed in computational than in refitting studies I would like to comment upon.

It was no easy task to find adequate reviewers for this paper as it combines techniques and expertise that are not commonly found together in individual researchers. However, Plutniak, with help from three reviewers, particularly M. Peeples, a leading figure in archaeological applications of network science, makes a considerable effort to be accessible to non-specialist archaeologists. The core Topological Study of Archaeological Refitting (TSAR) method is freely accessible as the R package archeofrag, which is available at the Comprehensive R Archive Network (https://CRAN.R-project.org/package=archeofrag) that can be applied without the need to understand all its mathematical, graph theory and coding aspects. Beside these, an online interface including test data has been provided (https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/), which aims to ease access to the method to those archaeologists inexperienced with R. Finally, supplementary material showing how to use the package and evaluating its potential through excellent examples is provided as both pdf and Rmw (Sweave) files. This is an important companion for the paper as it allows a better understanding of the methods presented in the paper and its practical application.

The author shows particular care in testing the potential and capabilities of the method. For example, a function is provided “frag.observer.failure” to test the robustness of the edge count method against the TSAR method, which is able to prove that TSAR can deal well with incomplete information. As a further step in this direction both simulated and real field-acquired data are used to test the method which further proves that archeofrag is not only able to quantitatively assess the mixture of excavated layers but to propose meaningful alternatives, which no doubt will add an increased methodological consistency and thoroughness to previous quantitative approaches to material refitting work, even when dealing with very complex stratigraphies.

All in all, this paper makes an important contribution to core archaeological practice through the use of innovative, reproducible and accessible computational methods. I fully endorse it for the conscious and solid methods it presents but also for its adherence to open publication practices. I hope that it can become of standard use in the reconstruction of excavated stratigraphical layers through conjoinable material culture.

[1] Plutniak, S. 2021. The Strength of Parthood Ties. Modelling Spatial Units and Fragmented Objects with the TSAR Method – Topological Study of Archaeological Refitting. OSF Preprints, q2e69, ver. 3 Peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.31219/osf.io/q2e69.

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

The paper “HORAI: An integrated management model for historical information” presents a novel model for managing historical information. The study draws from an extensive indepth work in historical information management and a multi-disciplinary corpus of research ranging from heritage infrastructure research and practice to information studies and archival management literature. The paper ties into several key debates and discussions in the field showing awareness of the state-of-the-art of data management practice and theory. The authors argue for a new semantic data model HORAI and link it to a four-phase data management lifecycle model. The conceptual work is discussed in relation to three existing information systems partly predating and partly developed from the outset of the HORAI-model.

While the paper shows appreciable understanding of the practical and theoretical state-of-the-art and the model has a lot of potential, in its current form it is still somewhat rough on the edges. Many of the both practical and theoretical threads introduced in the text warrant also more indepth consideration and it will be interesting to follow how the work will proceed in the future. For example, the comparison of the HORAI model and the ISAD(G): General International Standard Archival Description standard in the figure 1 is interesting but would require more elaboration. A slightly more thorough copyediting of the text would have also been helpful to make it more approachable.

As a whole, in spite of the critique, I find both the paper and the model as valuable contributions to the literature and the practice of managing historical information. The paper reports thorough work, provides a lot of food for thought and several interesting lines of inquiry in the future.

References

Del Fresno Bernal, P., Medina Gordo, S. and Travé Allepuz, E. (2024). HORAI: An integrated management model for historical information. CAA 2023, Amsterdam, Netherlands. https://doi.org/10.5281/zenodo.8185510

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