The advent of biomolecular methods has certainly increased our overall comprehension of archaeological societies. One of the materials of choice to perform ancient DNA or proteomics analyses is dental calculus[1,2], a mineralised biofilm formed during the life of one individual. Research conducted in the past few decades has demonstrated the potential of dental calculus to retrieve information about past societies health[3–6], diet[7–11], and more recently, as a putative proxy for isotopic analyses[12]. 

Based on a proof-of-concept previously published by their team[13], Bartholdy and collaborators’ paper presents the identification of compounds and their secondary metabolites derived from consumed plants in individuals from a XIXth century rural Dutch archaeological deposit[14]. Sørensen indeed demonstrated that drug intake is recorded in dental calculus, which are mineralised biofilms that can encapsulate drug compounds long after the latter are no longer detectable in blood. The liquid-chromatography coupled to mass spectrometry (LC-MS/MS)-based method developed showed the potential for archaeological applications[13]. 

Bartholdy et al. utilised the developed LC-MS/MS method to 41 buried individuals, most of them bearing pipe notches on their teeth, from the cemetery of the 19th rural settlement of Middenbeemster, the Netherlands. Along with dental calculus sampling and analysis, they undertook the skeletal and dental examination of all of the specimens in order to assess sex, age-at-death, and pathology on the two tissues. The results obtained on the dental calculus of the sampled individuals show probable consumption of tea, coffee and tobacco indicated by the detection of the various plant compounds and associated metabolites (caffeine, nicotine and salicylic acid, amongst others). 

The authors were able to place their results in perspective and propose several interpretations concerning the ingestion of plant-derived products, their survival in dental calculus and the importance of their findings for our overall comprehension of health and habits of the XIXth c. Dutch population. The paper is well-written and accessible to a non-specialist audience, maximising the impact of their study. I personally really enjoyed handling this manuscript that is not only a good piece of scientific literature but also a pleasant read, the reason why I warmly recommend this paper to be accessible through PCI Archaeology.

References

1.      Fagernäs, Z. and Warinner, C. (2023) Dental Calculus. in Handbook of Archaeological Sciences 575–590. https://doi.org/10.1002/9781119592112.ch28

2.      Wright, S. L., Dobney, K. & Weyrich, L. S. (2021) Advancing and refining archaeological dental calculus research using multiomic frameworks. STAR: Science & Technology of Archaeological Research 7, 13–30. https://doi.org/10.1080/20548923.2021.1882122

3.      Fotakis, A. K. et al. (2020) Multi-omic detection of Mycobacterium leprae in archaeological human dental calculus. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190584. https://doi.org/10.1098/rstb.2019.0584

4.      Warinner, C. et al. (2014) Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344. https://doi.org/10.1038/ng.2906

5.      Weyrich, L. S. et al. (2017) Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361. https://doi.org/10.1038/nature21674

6.      Jersie-Christensen, R. R. et al. (2018) Quantitative metaproteomics of medieval dental calculus reveals individual oral health status. Nat. Commun. 9, 4744. https://doi.org/10.1038/s41467-018-07148-3

7.      Hendy, J. et al. (2018) Proteomic evidence of dietary sources in ancient dental calculus. Proc. Biol. Sci. 285. https://doi.org/10.1098/rspb.2018.0977

8.      Wilkin, S. et al. (2020) Dairy pastoralism sustained eastern Eurasian steppe populations for 5,000 years. Nat Ecol Evol 4, 346–355. https://doi.org/10.1038/s41559-020-1120-y

9.      Bleasdale, M. et al. (2021) Ancient proteins provide evidence of dairy consumption in eastern Africa. Nat. Commun. 12, 632. https://doi.org/10.1038/s41467-020-20682-3

10.   Warinner, C. et al. (2014) Direct evidence of milk consumption from ancient human dental calculus. Sci. Rep. 4, 7104. https://doi.org/10.1038/srep07104

11.   Buckley, S., Usai, D., Jakob, T., Radini, A. and Hardy, K. (2014) Dental Calculus Reveals Unique Insights into Food Items, Cooking and Plant Processing in Prehistoric Central Sudan. PLoS One 9, e100808. https://doi.org/10.1371/journal.pone.0100808

12.   Salazar-García, D. C., Warinner, C., Eerkens, J. W. and Henry, A. G. (2023) The Potential of Dental Calculus as a Novel Source of Biological Isotopic Data. in Exploring Human Behavior Through Isotope Analysis: Applications in Archaeological Research (eds. Beasley, M. M. & Somerville, A. D.) 125–152. https://doi.org/10.1007/978-3-031-32268-6_6

13.   Sørensen, L. K., Hasselstrøm, J. B., Larsen, L. S. and Bindslev, D. A. (2021) Entrapment of drugs in dental calculus - Detection validation based on test results from post-mortem investigations. Forensic Sci. Int. 319, 110647. https://doi.org/10.1016/j.forsciint.2020.110647

14.   Bartholdy, Bjørn Peare, Hasselstrøm, Jørgen B., Sørensen, Lambert K., Casna, Maia, Hoogland, Menno, Historisch Genootschap Beemster and Henry, Amanda G. (2023) Multiproxy analysis exploring patterns of diet and disease in dental calculus and skeletal remains from a 19th century Dutch population, Zenodo, 7649150, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7649150

Over the last decade, a small but growing community of archaeologists, from a diversity of intellectual and demographic backgrounds, have been striving for computational reproducibility in their published research. In their survey of the accomplishments of this thriving community, Emma Karoune and Esther Plomp (2022) analyzed the wide variety of approaches researchers have taken to enhance the reproducibility of their research. A key contribution of this paper is their excellent synthesis of diverse approaches into three levels of increasing complexity. This is helpful because it provides multiple entry points for researchers new to the challenge of fortifying their research. Many researchers assume that computational reproducibility is only achievable if they have a high degree of technical skill with computers, or is only necessary if their work is very computationally intensive. Karoune and Plomp give three compelling reasons why reproducibility is important for all archaeological research, and through their three levels they demonstrate that how these levels can be accomplished with basic, non-specialized computer skills and widely used free software. They showcase exemplary work from a variety of archaeologists to show how practical and achievable reproducible research is for all archaeologists. They advocate for archaeologists to use the most widely used and supported tools and services to support their reproducible research, such as the R and Python programming languages for data analysis, and Git and GitHub for collaboration. 

This paper, with its extensive appendix including thoughtful responses to frequently asked questions about reproducible research in archaeology, is likely to have a wide reach and influence, beyond previous works on this topic that have largely focused on technical details. Karoune and Plomp have provided the on-ramp for a generation of archaeologists who will find their questions about reproducible research answered here. They will also find an agreeable entry point to reproducible research in one of the three levels described by the authors. Will every archaeologist embrace this way of working? Should they? The work of Leonelli (2018) can help us anticipate the answers to these questions. Leonelli asks where are the limits to reproducibility, and how do the characteristics of different ways of knowing affect the desirability of reproducibility? Leonelli's work invites us to consider that there will be archaeologists coming from different epistemic cultures for whom the motivations presented by Karoune and Plomp will not resonate. For example, archaeologists engaged in mostly hermeneutical social science and humanities research, who do little or no quantitative analysis and statistics, are unlikely to see reproducibility as meaningful or desirable for their work. We can describe these researchers as working in interpretative or constructivist epistemic cultures. In these cultures, the particulars of how an individual researcher engages with their subject are exclusive and unique, and they would argue it cannot be fully captured or shared in an meaningful way (Elman and Kapiszewski 2017). Here, knowledge is situational, emerging from a specific, once-off combination of people and circumstances. One example in archaeology is the chaîne opératoire approach of stone artefact analysis, which Monnier and Missal (2014:61) describe as "based upon the analyst's experience and intuition, and it is not replicable, nor quantifiable". To make sense of this example we can draw on Galison's (1997) concept of 'image traditions' and 'logic traditions'. An image tradition is a way of knowing that is qualitative, based on composing narratives from drawings and photographs. A logic tradition is based on the use of instruments and statistical methods to collect standardised quantitative data. Chaîne opératoire approaches fall into the image tradition, along with many other ways of working in archaeology that do not generate numbers or use them to support claims about the past. Archaeologists working in a logic tradition will find reproducible research to be more meaningful than those working in an image tradition.

We should be mindful not to claim that one epistemic culture is superior to another because reproducibility is not meaningful or attainable for researchers in one culture. Such a claim would threaten the plurality that is essential for the reliability of scientific knowledge (Massimi 2022). Instead we should identify those communities in archaeology where reproducible research is both meaningful and attainable, but has not yet been widely embraced. That is the where the most beneficial effects can be expected. According to Leonelli's (2018) framework, we can recognise these communities by a few basic characteristics. For example: they are doing computationally intensive archaeology, such as using or writing software to collect, simulate, analyse or visualise data; they are doing experimental archaeology; or they are making knowledge claims that are supported by tables of numeric data and data visualisations. Archaeologists whose work shares one or more of these characteristics will find the guidance provided here by Karoune and Plomp to be highly instructive and relevant, and stand the most to benefit from it.  ​​

But it is not only individual archaeological scientists that have potential to benefit from how Karoune and Plomp have lowered the barriers to reproducible research. An especially important implication of this paper is that by lowering the barriers to reproducible research, Karoune and Plomp help us all to lower barriers to participation in archaeology in general. Documenting our research transparently, and sharing our materials (such as data and code and so on) openly, can profoundly change how others can participate in archaeology. By doing this, we are enabling students and researchers elsewhere, for example in low and middle income locations, to use our materials in their teaching and learning. Other researchers and students can apply our methods to their data, and combine their data with ours to achieve syntheses beyond what a single project can do. Similarly, for archaeologists working with local, descendant or marginalized communities, the tools of reproducible research are vital for enabling community members to have full access to the archaeological process, and thus reproducibility may be considered a necessity for decolonising the discipline. Karoune and Plomp present the CARE principles (Carroll et al. 2020) to guide archaeologists in ensuring community control of data so that reproducibility can be ethically accomplished with community safety and well-being as a priority. This may have a profoundly positive impact on the demographics of archaeology, as it lowers the barriers of meaningful participation by people far beyond our immediate groups of collaborators. 

Making archaeology more accessible is of critical importance in stemming the negative social impacts of pseudoarchaeologists, who often claim that archaeologists actively suppress the truth of the archaeological record through secrecy, elitism, and exclusiveness. The harm in this is twofold. First, that pseudoarchaeology typically erases Indigenous heritage by claiming that their past achievements were due to an ancient, extinct advanced civilization, not Indigenous people. These claims are often adopted by white supremacists to support racist and antisemitic conspiracy theories (Turner and Turner 2021), which sometimes leads to prejudice, physical violence, radicalization and extremism. A second type of harm that can come from claims of secrecy and elitism is it drains public trust in experts, leading to science denial. Not only trust in archaeologists, but trust in many kinds of experts, including those working on urgent contemporary issues such as public health and climate change. Karoune and Plomp's work is important here because it provides a practical and affordable pathway for archaeologists to fight claims of secrecy and elitism by sharing their work in ways that make it possible for non-academics to inspect the analyses and logic in detail. Claims of secrecy and elitism can be easily countered by openness, transparently and reproducibility by archaeologists. This is not only useful for tackling pseudoarchaeologists, but also in enacting an ethic of care, framing members of the public as people that not only care about archaeology as part of humanity's shared heritage, but also care for the construction of reliable interpretations of the archaeological record to provide secure and authentic foundations for their social identities and relationships (Wylie et al 2018; de la Bellacasa 2011). By striving for reproducible research in the way described by Karoune and Plomp, we are practicing a kind of reciprocal care among ourselves as archaeologists, and between archaeologists and members of the public as two communities who care about the human past. 

References

Karoune, E., and Plomp, E. (2022). Removing Barriers to Reproducible Research in Archaeology. Zenodo, 7320029, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7320029

de la Bellacasa, M. P. (2011). Matters of care in technoscience: Assembling neglected things. Social Studies of Science, 41(1), 85–106. https://doi.org/10.1177/0306312710380301

Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., and Hudson, M. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1), Article 1. https://doi.org/10.5334/dsj-2020-043​

Elman, C., and Kapiszewski, D. (2017). Benefits and Challenges of Making Qualitative Research More Transparent. Inside Higher Ed 2017,  http://web.archive.org/web/20220407064134/https://www.insidehighered.com/blogs/rethinking-research/benefits-and-challenges-making-qualitative-research-more-transparent (accessed 21 Oct, 2022). 

Galison, P. (1997). Image and logic: a material culture of microphysics. Chicago (IL): University of Chicago Press.

Leonelli, S. (2018). Re-Thinking Reproducibility as a Criterion for Research Quality [preprint]. Available online: http://philsci-archive.pitt.edu/id/eprint/14352 (Accessed 21 Oct 2022).

Massimi, M. (2022). Perspectival realism. Oxford University Press.

Monnier, G. F., and Kele M.. "Another Mousterian debate? Bordian facies, chaîne opératoire technocomplexes, and patterns of lithic variability in the western European Middle and Upper Pleistocene." Quaternary International 350 (2014): 59-83. https://doi.org/10.1016/j.quaint.2014.06.053

Turner, D. D., and Turner, M. I. (2021). “I’m Not Saying It Was Aliens”: An Archaeological and Philosophical Analysis of a Conspiracy Theory. In A. Killin and S. Allen-Hermanson (Eds.), Explorations in Archaeology and Philosophy (pp. 7–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-61052-4_2

​Wylie, C., Neeley, K., and Ferguson, S. (2018). Beyond Technological Literacy: Open Data as Active Democratic Engagement? Digital Culture & Society, 4(2), 157–182. https://doi.org/10.14361/dcs-2018-0209​​​​

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​The paper “Percolation Package – From Script Sharing to Package Publication” by Sophie C. Schmidt and Simon Maddison (2023) describes the development of an R package designed to apply Percolation Analysis to archaeological spatial data. In an earlier publication, Maddison and Schmidt (2020) describe Percolation Analysis and provide case studies that demonstrate its usefulness at different spatial scales. In the current paper, the authors use their experience collaborating to develop the R package as part of a broader argument for the importance of code sharing to the research process. 

The paper begins by describing the development process of the R package, beginning with borrowing code from a geographer, refining it to fit archaeological case studies, and then collaborating to further refine and systematize the code into an R package that is more easily reusable by other researchers. As the review by Joe Roe noted, a strength of the paper is “presenting the development process as it actually happens rather than in an idealized form.” The authors also include a section about the lessons learned from their experience. 

Moving on from the anecdotal data of their own experience, the authors also explore code sharing practices in archaeology by briefly examining two datasets. One dataset comes from “open-archaeo” (https://open-archaeo.info/), an on-line list of open-source archaeological software maintained by Zack Batist. The other dataset includes articles published between 2018 and 2023 in the Journal of Computer Applications in Archaeology. Schmidt and Maddison find that these two datasets provide contrasting views of code sharing in archaeology: many of the resources in the open-archaeo list are housed on Github, lack persistent object identifiers, and many are not easily findable (other than through the open-archaeo list). Research software attached to the published articles, on the other hand, is more easily findable either as a supplement to the published article, or in a repository with a DOI.

The examination of code sharing in archaeology through these two datasets is preliminary and incomplete, but it does show that further research into archaeologists’ code-writing and code-sharing practices could be useful. Archaeologists often create software tools to facilitate their research, but how often?  How often is research software shared with published articles? How much attention is given to documentation or making the software usable for other researchers? What are best (or good) practices for sharing code to make it findable and usable? Schmidt and Maddison’s paper provides partial answers to these questions, but a more thorough study of code sharing in archaeology would be useful. Differences among journals in how often they publish articles with shared code, or the effects of age, gender, nationality, or context of employment on attitudes toward code sharing seem like obvious factors for a future study to consider.

Shared code that is easy to find and easy to use benefits the researchers who adopt code written by others, but code authors also have much to gain by sharing. Properly shared code becomes a citable research product, and the act of code sharing can lead to productive research collaborations, as Schmidt and Maddison describe from their own experience. The strength of this paper is the attention it brings to current code-sharing practices in archaeology. I hope the paper will also help improve code sharing in archaeology by inspiring more archaeologists to share their research code so other researchers can find and use (and cite) it. 

References

Maddison, M.S. and Schmidt, S.C. (2020). Percolation Analysis – Archaeological Applications at Widely Different Spatial Scales. Journal of Computer Applications in Archaeology, 3(1), p.269–287. https://doi.org/10.5334/jcaa.54 

Schmidt, S. C., and Maddison, M. S. (2023). Percolation Package - From script sharing to package publication, Zenodo, 7966497, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7966497

The community of archaeologists, bioanthropologist and paleontologists relying on tools use-wear and dental microwear has grown in the recent years, mainly driven by the spread of confocal microscopes in the laboratories. If the diversity of microscopes is quite high, the main software used for 3D surface texture data analysis are mostly different versions of the same Mountains Map core. In addition to this software, since the beginning of 3D surface texture analysis in dental microwear, surface sensitive fractal analysis (SSFA) initially developed for industrial research (Brown & Savary, 1991) have been performed in our disciplines with the Sfrax/Toothfrax software for two decades (Ungar et al., 2003). This software being discontinued, these calculations have been integrated to the new versions of Mountains Map, with multi-core computing, full integration in the software and an update of the calculation itself.

New research based on these standard parameters of surface texture analysis will be, from now on, mainly calculated with this new add-on of Mountains Map, and will be directly compared with the important literature based on the previous software. The question addressed by Calandra et al. (2022), gathering several prominent researchers in this domain including the Mountains Map developer F. Blateyron, is key for the future research: can we directly compare SSFA results from both software?

Thanks to a Bayesian approach to this question, and comparing results calculated with both software on three different datasets (two on dental microwear, one on lithic raw materials), the authors show that the two software gives statistically different results for all surface texture parameters tested in the paper. Nevertheless, applying the new calculation to the datasets, they also show that the results published in original studies with these datasets would have been similar. Authors also claim that in the future, researchers will need to re-calculate the fractal parameters of previously published 3D surfaces and cannot simply integrate ancient and new data together.

We also want to emphasize the openness of the work published here. All datasets have been published online and will be probably very useful for future methodological works. Authors also published their code for statistical comparison of datasets, and proposed a fully reproducible article that allowed the reviewers to check the content of the paper, which can also make this article of high interest for student training.

This article is therefore a very important methodological work for the community, as noted by all three reviewers. It will certainly support the current transition between the two software packages and it is necessary that all surface texture specialists take these results and the recommendation of authors into account: calculate again data from ancient measurements, and share the 3D surface measurements on open access repositories to secure their access in the future.

References

Brown CA, and Savary G (1991) Describing ground surface texture using contact profilometry and fractal analysis. Wear, 141, 211–226. https://doi.org/10.1016/0043-1648(91)90269-Z

Calandra I, Bob K, Merceron G, Blateyron F, Hildebrandt A, Schulz-Kornas E, Souron A, and Winkler DE (2022) Surface texture analysis in Toothfrax and MountainsMap® SSFA module: Different software packages, different results? Zenodo, 7219877, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7219877

Ungar PS, Brown CA, Bergstrom TS, and Walker A (2003) Quantification of dental microwear by tandem scanning confocal microscopy and scale-sensitive fractal analyses. Scanning: The Journal of Scanning Microscopies, 25, 185–193. https://doi.org/10.1002/sca.4950250405

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.