Anneke H. van Heteren based on reviews by Stefan Schlager and 1 anonymous reviewer

Phenomics is the analysis of high-dimensional phenotypic data [1]. Phenomics research strategies are capable of linking genetic variation to phenotypic variation [2], but a genetic component is not absolutely necessary. The paper “Archaeophenomics of ancient domestic plants and animals using geometric morphometrics: a review” by Evin and colleagues [3] examines the use of geometric morphometrics in bioarchaeology and coins the term archaeophenomics. Archaeophenomics can be described as the large-scale phenotyping of ancient remains, and both addresses taxonomic identification, as well as infers spatio-temporal agrobiodiversity dynamics. It is a relatively new field in bioarchaeology with the first paper using this approach stemming from 2004. This study by Evin et al. [3] presents an excellent review and unquestionably demonstrates the potential of archaeophenomics.

The authors provide an exhaustive review specifically of bioarchaeological studies in international journals using geometric morphometrics to study archaeological remains of domestic species. Although geometric morphometrics lends itself well for archaeophenomics, readers should keep in mind that this is not the only method and other approaches might equally fall under archaeophenomics as long as high-dimensional phenotypic archaeological data are involved.

Distinguishing archaeophenomics from phenomics is important because of a critical difference. Archaeological remains are often altered by taphonomical processes. As such data may not be as complete as when working with modern specimens. Although this poses difficulties, morphometric analyses can usually still be performed as long as the structures presenting the relevant geometrical features are present. Even fragmented remains can be studied with a restricted version of the original landmarking/measurement protocol.

Evin et al. [3] define archaeophenomics as “phenomics of the past”. This is only partly correct. It can be deduced from their review that they really mean phenomics of our (human) past. This leaves a gap for phenomics of the non-human past, for which I suggest the term palaeophenomics. 

[1] Jin, L. (2021). Welcome to the Phenomics Journal. Phenomics, 1, 1–2. https://doi.org/10.1007/s43657-020-00009-4.
[2] Bilder, R.M., Sabb, F.W., Cannon, T.D., London, E.D., Jentsch, J.D., Stott Parker, D., Poldrack, R.A., Evans, C., Freimer, N.B. (2009). Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience, 164(1), 30-42. https://doi.org/10.1016/j.neuroscience.2009.01.027
[3] Evin, A., Bouby, L., Bonhomme, V., Jeanty, A., Jeanjean, M., Terral., J.-F. (2022). Archaeophenomics of ancient domestic plants and animals using geometric morphometrics: a review. Peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.31219/osf.io/skeu5

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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