“Research perspectives and their influence for typologies” by E. Giannichedda (1) is a contribution to the upcoming volume on the role of typology and type-thinking in current archaeological theory and praxis edited by the recommenders. Taking a decidedly Italian perspective on classificatory practice grounded in what the author dubs the “history of material culture”, Giannichedda offers an inventory of six divergent but overall complementary modes of ordering archaeological material: i) chrono-typological and culture-historical, ii) techno-anthropological, iii) social, iv) socio-economic and v) cognitive. These various lenses broadly align with similarly labeled perspectives on the archaeological record more generally. According to the author, they lend themselves to different ways of identifying and using types in archaeological work. Importantly, Giannichedda reminds us that no ordering practice is a neutral act and typologies should not be devised for their own sake but because we have specific epistemic interests. Even though this view is certainly not shared by everyone involved in the broader debate on the purpose and goal of systematics, classification, typology or archaeological taxonomy (2–4), the paper emphatically defends the long-standing idea that ordering practices are not suitable to elucidate the structure and composition of reality but instead devise tools to answer certain questions or help investigate certain dimensions of complex past realities. This position considers typologies as conceptual prosthetics of knowing, a view that broadly resonates with what is referred to as epistemic instrumentalism in the philosophy of science (5, 6). Types and type-work should accordingly reflect well-defined means-end relationships.

Based on the recognition of archaeology as part of an integrated “history of material culture” rooted in a blend of continental and Anglophone theories, Giannichedda argues that type-work should pay attention to relevant relations between various artefacts in a given historical context that help further historical understanding. Classificatory practice in archaeology – the ordering of artefactual materials according to properties – must thus proceed with the goal of multifaceted “historical reconstruction in mind”. It should serve this reconstruction, and not the other way around. By drawing on the example of a Medieval nunnery in the Piedmont region of northwestern Italy, Giannichedda explores how different goals of classification and typo-praxis (linked to i-v; see above) foreground different aspects, features, and relations of archaeological materials and as such allow to pinpoint and examine different constellations of archaeological objects. He argues that archaeological typo-praxis, for this reason, should almost never concern itself with isolated artefacts but should take into account broader historical assemblages of artefacts. This does not necessarily mean to pay equal attention to all available artefacts and materials, however. To the contrary, in many cases, it is necessary to recognize that some artefacts and some features are more important than others as anchors grouping materials and establishing relations with other objects. An example are so-called ‘barometer objects’ (7) or unique pieces which often have exceptional informational value but can easily be overlooked when only shared features are taken into consideration. As Giannichedda reminds us, considering all objects and properties equally is also a normative decision and does not render ordering less subjective. The archaeological analysis of types should therefore always be complemented by an examination of variants, even if some of these variants are idiosyncratic or even unique. A type, then, may be difficult to define universally.

In total, “Research perspectives and their influence for typologies” emphasizes the need for “elastic” and “flexible” approaches to archaeological types and typologies in order to effectively respond to the manifold research interests cultivated by archaeologists as well as the many and complex past realities they face. Complexity is taken here to indicate that no single research perspective and associated mode of ordering can adequately capture the dimensionality and richness of these past realities and we can therefore only benefit from multiple co-existing ways of grouping and relating archaeological artefacts. Different logics of grouping may simply reveal different aspects of these realities. As such, Giannichedda’s proposal can be read as a formulation of the now classic pluralism thesis (8–11) – that only a plurality of ways of ordering and interrelating artefacts can unlock the full suite of relationships within historical assemblages archaeologists are interested in.

Bibliography

1. Giannichedda, E. (2023). Research perspectives and their influence for typologies, Zenodo, 7322855, ver. 9 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7322855

2. Dunnell, R. C. (2002). Systematics in Prehistory, Illustrated Edition (The Blackburn Press, 2002).

3. Reynolds, N. and Riede, F. (2019). House of cards: cultural taxonomy and the study of the European Upper Palaeolithic. Antiquity 93, 1350–1358. https://doi.org/10.15184/aqy.2019.49

4. Lyman, R. L. (2021). On the Importance of Systematics to Archaeological Research: the Covariation of Typological Diversity and Morphological Disparity. J Paleo Arch 4, 3. https://doi.org/10.1007/s41982-021-00077-6

5. Van Fraassen, B. C. (2002). The empirical stance (Yale University Press).

6. Stanford, P. K. (2006). Exceeding Our Grasp: Science, History, and the Problem of Unconceived Alternatives (Oxford University Press). https://doi.org/10.1093/0195174089.001.0001

7. Radohs, L. (2023). Urban elite culture: a methodological study of aristocracy and civic elites in sea-trading towns of the southwestern Baltic (12th-14th c.) (Böhlau).

8. Kellert, S. H., Longino, H. E. and Waters, C. K. (2006). Scientific pluralism (University of Minnesota Press).

9. Cat, J. (2012). Essay Review: Scientific Pluralism. Philosophy of Science 79, 317–325. https://doi.org/10.1086/664747

10. Chang, H. (2012). Is Water H2O?: Evidence, Realism and Pluralism (Springer Netherlands). https://doi.org/10.1007/978-94-007-3932-1

11. Wylie, A. (2015). “A plurality of pluralisms: Collaborative practice in archaeology” in Objectivity in Science, (Springer), pp. 189–210. https://doi.org/10.1007/978-3-319-14349-1_10

Diamanti et al. (2024) is a significant contribution to the field of underwater robotics and their use in archaeology, with an innovative approach to some major problems in the deployment of said technologies. It identifies issues when it comes to approaching Underwater Cultural Heritage (UCH) sites and does so through an interest in the combination of data, maneuverability, and the interpretation provided by the instruments that archaeologists operate. The article's motives are clear: it is not enough to find the means to reach these sites, but rather is fundamental to take a step forward in methodology and how we can safeguard certain aspects of data recovery with robust mission planning.

To this end, the article does not fail to highlight previous contributions, in an intertwined web of references that demonstrate the marked evolution of the use of Unmanned Underwater Vehicles (UUVs), Remote Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs) and Autonomous Surface Vehicles (ASVs), which are growing exponentially in use (see Kapetanović et al. 2020). It should be emphasized that the notion of ‘aquatic environment’ used here is quite broad and is not limited to oceanic or maritime environments, which allows for a larger perspective on distinct technologies that proliferate in underwater archaeology. There is also a relevant discussion on the typologies of sensors and how these autonomous vehicles obtain their data, where are debated Inertial Measurement Units (IMU) and LiDAR systems. 

Thus, the authors of this article propose the creation of a model that acquires data through simulations, which allows for a better understanding of what a real mission presupposes in the field. Their tripartite method - pre-mission planning; mission plan and post-mission plan - offers a performing algorithm that simplifies and provides reliability to all the parts of the intervention. The use of real cases to create simulation models allows for a substantial approximation to common practice in underwater environments. And yet, the article is at its most innovative status when it combines all the elements it sets out to explore. It could simply focus on the methodological or planning component, on obtaining data, or on theoretical problems. But it goes further, which makes this approach more complete and of interest to the archaeological community. By not taking any part as isolated, the problems and possible solutions arising from the course of the mission are carried over from one parameter to another, where details are worked upon and efficiency goals are set.

One of the most significant cases is the tuning of ocean optics in aquatic environments according to the idiosyncracies of real cases (Diamanti et al. 2024: 8), a complex endeavor but absolutely necessary in order to increase the informative potential of the simulation. The exploration of various data capture models is also welcome, for the purposes of comparison and adaptation on a case-by-case basis. The brief theoretical reflection offered at the end of the article dwells in all these points and problematizes the difference between terrestrial and aquatic archaeology. In fact, the distinction does not only exist in the technical component, as although it draws in theoretical elements from archaeology that is carried out on land (see Krieger 2012 for this matter), the problems and interpretations are shaped by different factors and therefore become unique (Diamanti et al 2024: 15). The future, according to the authors, lies in increasing the autonomy of these vehicles so that the human element does not have to make decisions in a systematic way. It is in that note, and in order for that path to become closer to reality, that we strongly recommend this article for publication, in conjunction with the comments of the reviewers. We hope that its integrated approach, which brings together methods, theories and reflections, can become a broader modus operandi within the field of underwater robotics applied to archaeology.

References:

Diamanti, E., Yip, M., Stahl, A. and Ødegård, Ø. (2024). Advancing data quality of marine archaeological documentation using underwater robotics: from simulation environments to real-world scenarios, Zenodo, 8305098, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8305098

Kapetanović, N., Vasilijević, A., Nađ, Đ., Zubčić, K., and Mišković, N. (2020). Marine Robots Mapping the Present and the Past: Unraveling the Secrets of the Deep. Remote Sensing, 12(23), 3902. MDPI AG. http://dx.doi.org/10.3390/rs12233902

Krieger, W. H. (2012). Theory, Locality, and Methodology in Archaeology: Just Add Water? HOPOS: The Journal of the International Society for the History of Philosophy of Science, 2(2), 243–257. https://doi.org/10.1086/666956

Managing data in archaeology is a perennial problem. As the adage goes, every day in the field equates to several days in the lab (and beyond). For better or worse, past archaeologists did all their organizing and synthesis manually, by hand, but since the 1970s ways of digitizing data for long term management and analysis have gained increasing attention [1]. It is debatable whether this ever actually made things easier, particularly given the associated problem of sustainable maintenance and accessibility of the data. Many older archaeologists, for instance, still have reels and tapes full of data that now require a new form of archaeology to excavate (see [2] for an unrealized idea on how to solve this).

Today, the options for managing digital archaeological data are limited only by one’s imagination. There are systems built specifically for archaeology, such as Arches [3], Ark [4], Codifi [5], Heurist [6], InTerris Registries [7], OpenAtlas [8], S-Archeo [9], and Wild Note [10], as well as those geared towards museum collections like PastPerfect [11] and CatalogIt [12], among others. There are also mainstream databases that can be adapted to archaeological needs like MS Access [13] and Claris FileMaker [14], as well as various web database apps that function in much the same way (e.g., Caspio [15], dbBee [16], Amazon's Simpledb [17], Sci-Note [18], etc.) — all with their own limitations in size, price, and utility. One could also write the code for specific database needs using pre-built frameworks like those in Ruby-On-Rails [19] or similar languages. And of course, recent advances in machine-learning and AI will undoubtedly bring new solutions in the near future.

But let’s be honest — most archaeologists probably just use Excel. That's partly because, given all the options, it is hard to decide the best tool and whether its worth changing from your current system, especially given few real-world examples in the literature. Bastien Rueff’s new paper [20] is therefore a welcomed presentation on the use of Omeka S [21] to manage data collected for the Timbers in Minoan and Mycenaean Architecture (TiMMA) project. Omeka S is an open-source web-database that is based in PHP and MySQL, and although it was built with the goal of connecting digital cultural heritage collections with other resources online, it has been rarely used in archaeology. Part of the issue is that Omeka Classic was built for use on individual sites, but this has now been scaled-up in Omeka S to accommodate a plurality of sites. 

Some of the strengths of Omeka S include its open-source availability (accessible regardless of budget), the way it links data stored elsewhere on the web (keeping the database itself lean), its ability to import data from common file types, and its multi-lingual support. The latter feature was particularly important to the TiMAA project because it allowed members of the team (ranging from English, Greek, French, and Italian, among others) to enter data into the system in whatever language they felt most comfortable.

However, there are several limitations specific to Omeka S that will limit widespread adoption. Among these, Omeka S apparently lacks the ability to export metadata, auto-fill forms, produce summations or reports, or provide basic statistical analysis. Its internal search capabilities also appear extremely limited. And that is not to mention the barriers typical of any new software, such as onerous technical training, questionable long-term sustainability, or the need for the initial digitization and formatting of data. But given the rather restricted use-case for Omeka S, it appears that this is not a comprehensive tool but one merely for data entry and storage that requires complementary software to carry out common tasks.

As such, Rueff has provided a review of a program that most archaeologists will likely not want or need. But if one was considering adopting Omeka S for a project, then this paper offers critical information for how to go about that. It is a thorough overview of the software package and offers an excellent example of its use in archaeological practice.

NOTES

[1] Doran, J. E., and F. R. Hodson (1975) Mathematics and Computers in Archaeology. Harvard University Press.

[2] Snow, Dean R., Mark Gahegan, C. Lee Giles, Kenneth G. Hirth, George R. Milner, Prasenjit Mitra, and James Z. Wang (2006) Cybertools and Archaeology. Science 311(5763):958–959.

[3] https://www.archesproject.org/

[4] https://ark.lparchaeology.com/

[5] https://codifi.com/

[6] https://heuristnetwork.org/

[7] https://www.interrisreg.org/

[8] https://openatlas.eu/

[9] https://www.skinsoft-lab.com/software/archaelogy-collection-management

[10] https://wildnoteapp.com/

[11] https://museumsoftware.com/

[12] https://www.catalogit.app/

[13] https://www.microsoft.com/en-us/microsoft-365/access

[14] https://www.claris.com/filemaker/

[15] https://www.caspio.com/

[16] https://www.dbbee.com/

[17] https://aws.amazon.com/simpledb/

[18] https://www.scinote.net/

[19] https://rubyonrails.org/

[20] Rueff, Bastien (2023) Dealing with Post-Excavation Data: The Omeka S TiMMA Web-Database. peer-reviewed and recommended by Peer Community in Archaeology. https://zenodo.org/records/7989905

[21] https://omeka.org/

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

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