The increasing reliance on digital and big data in archaeology is pushing the scientific community more and more to reconsider their storing and use [1, 2]. Furthermore, the openness and findability in the way these data are shared represent a key matter for the growth of the discipline, especially in the case of bioarchaeology and archaeological sciences [3]. 

In this paper, [4] the author presents the result of a survey targeted on UK bioarchaeologists and then extended worldwide. The paper maintains the structure of a report as it was intended for the conference it was part of (CAA 2023, Amsterdam) but it represents the first public outcome of an inquiry on the bioarchaeological scientific community. A reflection on ourselves and our own practices. Are all the disciplines adhering to the same policies? Do any bioarchaeologist use the same protocols and formats? Are there any differences in between the domains? Is the Needs Analysis fulfilling the questions?

The results, obtained through an accurate screening to avoid distortions, are creating an intriguing picture on the current state of "fairness" and highlighting how Institutions' rules and policies can and should indicate the correct workflow to follow. In the end, the wide application of the FAIR principles will contribute significantly to the growth of the disciplines and to create an environment where the users are not just contributors, but primary beneficiaries of the system. 

[1] Huggett j. (2020). Is Big Digital Data Different? Towards a New Archaeological Paradigm, Journal of Field Archaeology, 45:sup1, S8-S17. https://doi.org/10.1080/00934690.2020.1713281

[2] Nicholson C., Kansa S., Gupta N. and Fernandez R. (2023). Will It Ever Be FAIR?: Making Archaeological Data Findable, Accessible, Interoperable, and Reusable. Advances in Archaeological Practice 11 (1): 63-75. https://doi.org/10.1017/aap.2022.40

[3] Plomp E., Stantis C., James H.F., Cheung C., Snoeck C., Kootker L., Kharobi A., Borges C., Reynaga D.K.M., Pospieszny Ł., Fulminante, F., Stevens, R., Alaica, A. K., Becker, A., de Rochefort, X. and Salesse, K. (2022). The IsoArcH initiative: Working towards an open and collaborative isotope data culture in bioarchaeology. Data in brief, 45, p.108595. https://doi.org/10.1016/j.dib.2022.108595

[4] Lien-Talks, A. (2024). How FAIR is Bioarchaeological Data: with a particular emphasis on making archaeological science data Reusable. Zenodo, 8139910, ver. 6 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8139910

Data authenticity and approaches to data authentication are crucial issues in ancient protein research. The advent of modern mass spectrometry has enabled the detection of traces of ancient biomolecules contained in fossils, including protein sequences. However, detecting proteins in ancient samples does not equate to demonstrating their endogenous nature: instead, if the mechanisms that drive protein preservation and degradation are understood, then the extent of protein diagenesis can be used for evaluating preservational quality, which in turn may be related to the authenticity of the protein data. 

The post-mortem deamidation of asparaginyl and glutamyl residues is a key degradation reaction, which can be assessed effectively on the basis of mass spectrometry data, and which has accrued a long history of research, both in terms of describing the mechanisms governing the reactions and with regard to the best strategies for assessing and quantifying the extent of glutamine (Gln) and asparagine (Asn) deamidation in ancient samples (Pal Chowdhury et al., 2019; Ramsøe et al., 2021, 2020; Schroeter and Cleland, 2016; Simpson et al., 2016; Solazzo et al., 2014; Welker et al., 2016; Wilson et al., 2012). 

In their paper, Nair and colleagues (2022) build on this wealth of knowledge and present a tool for quantifying the extent of Gln deamidation in parchment. Parchment is a collagen-based material which can yield extraordinary insights into manuscript manufacturing practices in the past, as well as on the daily lives of the people who assembled and used them (“biocodicology”) (Fiddyment et al., 2021, 2019, 2015; Teasdale et al., 2017). Importantly, the extent of deamidation can be directly related to the quality of the parchment produced: rapid direct deamidation of Gln is induced by the liming process, therefore high extents of deamidation are linked to prolonged exposure to the high pH conditions which are typical of liming, thus implying lower-quality parchment.

Nair et al.’s approach focuses on collagen peptides which are typically detected during MALDI-TOF mass spectrometry analyses of parchment and build a simple three-step workflow able to yield an overall index of deamidation for a sample (the parchment glutamine index - PQI) 一 taking into account that different Gln residues degrade at different rates according to their micro-chemical environment. The first step involves pre-processing the MALDI spectra, since Nair et al. are specifically interested in maximising information which can be obtained by low-quality data. The second step builds on well-established methods for quantifying Q → E from MALDI-TOF data by modelling the convoluted isotope distributions (Wilson et al., 2012). Once relative rates of deamidation in selected peptides within a given sample are calculated, the third step uses a mixed effects model to combine the individual deamidation estimates and to obtain an overall estimate of the deamidation for a parchment sample (PQI). 

The PQI can be used effectively for assessing parchment quality, as the authors show for the dataset from Orval Abbey. However, PQI could also have wider applications to the study of processed collagen, which is widely used in the food and pharmaceutical industries. In general, the study by Nair et al. is a welcome addition to a growing body of research on protein diagenesis, which will ultimately improve models for the assessment of the authenticity of biomolecular data in archaeology. 

References

Chowdhury, P.M., Wogelius, R., Manning, P.L., Metz, L., Slimak, L., and Buckley, M. 2019. Collagen deamidation in archaeological bone as an assessment for relative decay rates. Archaeometry 61:1382–1398. https://doi.org/10.1111/arcm.12492

Fiddyment, S., Goodison, N.J., Brenner, E., Signorello, S., Price, K., and Collins, M.J.. 2021. Girding the loins? Direct evidence of the use of a medieval parchment birthing girdle from biomolecular analysis. bioRxiv. https://doi.org/10.1098/rsos.202055

Fiddyment,S., Holsinger, B., Ruzzier, C., Devine, A., Binois, A., Albarella, U., Fischer, R., Nichols, E., Curtis, A., Cheese, E., Teasdale, M.D., Checkley-Scott, C., Milner, S.J., Rudy, K.M., Johnson, E.J., Vnouček, J., Garrison, M., McGrory, S., Bradley, D.G., and Collins, M.J. 2015. Animal origin of 13th-century uterine vellum revealed using noninvasive peptide fingerprinting. Proc Natl Acad Sci U S A 112:15066–15071. https://doi.org/10.1073/pnas.1512264112

Fiddyment, S., Teasdale, M.D., Vnouček, J., Lévêque, É., Binois, A., and Collins, M.J. 2019. So you want to do biocodicology? A field guide to the biological analysis of parchment. Heritage Science 7:35. https://doi.org/10.1186/s40494-019-0278-6

Nair, B., Rodríguez Palomo, I., Markussen, B., Wiuf, C., Fiddyment, S., and Collins, M. Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data. BiorRxiv, 2022.03.13.483627, ver. 6 peer-reviewed and recommended by Peer community in Archaeology. https://doi.org/10.1101/2022.03.13.483627 

Ramsøe, A., Crispin, M., Mackie, M., McGrath, K., Fischer, R., Demarchi, B., Collins, M.J., Hendy, J., and Speller, C. 2021. Assessing the degradation of ancient milk proteins through site-specific deamidation patterns. Sci Rep 11:7795. https://doi.org/10.1038/s41598-021-87125-x

Ramsøe, A., van Heekeren, V., Ponce, P., Fischer, R., Barnes, I., Speller, C., and Collins, M.J. 2020. DeamiDATE 1.0: Site-specific deamidation as a tool to assess authenticity of members of ancient proteomes. J Archaeol Sci 115:105080. https://doi.org/10.1016/j.jas.2020.105080

Schroeter, E.R., and Cleland, T.P. 2016. Glutamine deamidation: an indicator of antiquity, or preservational quality? Rapid Commun Mass Spectrom 30:251–255. https://doi.org/10.1002/rcm.7445

Simpson, J.P., Penkman, K.E.H., and Demarchi, B. 2016. The effects of demineralisation and sampling point variability on the measurement of glutamine deamidation in type I collagen extracted from bone. J Archaeol Sci 69: 29-38. https://doi.org/10.1016/j.jas.2016.02.002

Solazzo, C., Wilson, J., Dyer, J.M., Clerens, S., Plowman, J.E., von Holstein, I., Walton Rogers, P., Peacock, E.E., and Collins, M.J. 2014. Modeling deamidation in sheep α-keratin peptides and application to archeological wool textiles. Anal Chem 86:567–575. https://doi.org/10.1021/ac4026362

Teasdale, M.D., Fiddyment, S., Vnouček, J., Mattiangeli, V., Speller, C., Binois, A., Carver, M., Dand, C., Newfield, T.P., Webb, C.C., Bradley, D.G., and Collins M.J. 2017. The York Gospels: a 1000-year biological palimpsest. R Soc Open Sci 4:170988. https://doi.org/10.1098/rsos.170988

Welker, F., Soressi, M.A., Roussel, M., van Riemsdijk, I., Hublin, J.-J., and Collins, M.J. 2016. Variations in glutamine deamidation for a Châtelperronian bone assemblage as measured by peptide mass fingerprinting of collagen. STAR: Science & Technology of Archaeological Research 3:15–27. https://doi.org/10.1080/20548923.2016.1258825

Wilson, J., van Doorn, N.L., and Collins, M.J. 2012. Assessing the extent of bone degradation using glutamine deamidation in collagen. Anal Chem 84:9041–9048. https://doi.org/10.1021/ac301333t

It currently seems obvious that the Mediterranean basin is a place of great exchanges of cultures, populations and goods. Although studies have focused quite extensively on the archaeology of maritime exchanges [1–3], it is challenging to assess archaeologically to what extent these networks had an influence on the biology of domesticated animals in the past, or even if animals were part of the trip. The question of past populations establishing extensive connections in the Mediterranean basin during the Iron Age and Persian period and especially, if these population exchange livestock and how these connections contributed to the animals phenotype or morphotype diversity is still difficult to document in the (zoo)archaeological record. These are amongst the questions Harding et al. [4] are making an attempt at documenting. Focusing on archaeological deposits from Cyprus and Israel in the eastern part of the Mediterranean basin, they use sheep astragali as a proxy to explore the potential connections that might have existed between evolution of the animals’ morphology and exchanges through sea travel. Postulating that animals from inland sites should have variant morphology from those of coastal sites due to more intensive exchanges for the latter, mainly because of the incorporation of non-native animals, they conducted geometric morphometrics analyses to make an attempt at documenting the phenomenon. Observing changes in size and shape, but also an increased morphological variability within the assemblages from coastal sites, they nicely discuss and put their results in perspectives with the archaeological record and literature [5,6]. Although, as they acknowledge, their sample size is rather limited to draw any general conclusion, this paper sheds new lights on the influence of maritime transport and its influence over domesticated sheep diversity between Cyprus and the southern Levant, paving the way for future studies.

References
1.     Leidwanger, J. and Knappett, C. (2018). Maritime Networks in the Ancient Mediterranean World. (Cambridge University Press). https://doi.org/10.1017/9781108555685
2.     Leidwanger, J. (2020). Roman Seas: A Maritime Archaeology of Eastern Mediterranean Economies. (Oxford University Press). https://doi.org/10.1093/oso/9780190083656.001.0001
3.     Bernard Knapp, A., Russell, A. and van Dommelen, P. (2022). Cyprus, Sardinia and Sicily: A Maritime Perspective on Interaction, Connectivity and Imagination in Mediterranean Prehistory. Cambridge Archaeological Journal 32, 79–97. https://doi.org/10.1017/S0959774321000330
4.     Harding, S. A., Hadjikoumis, A., Vermeersch, S., Shafir, R. and Marom, N. (2024) The contribution of Mediterranean connectivity to morphological variability in Iron Age sheep of the Eastern Mediterranean. bioRxiv2022.12.24.521859 https://doi.org/10.1101/2022.12.24.521859.
5.     Price, M. D., Perry-Gal, L. and Reshef, H. (2023). The Southern Levantine pig from domestication to Romanization: A biometrical approach. J. Archaeol. Sci. 157, 105828. https://doi.org/10.1016/j.jas.2023.105828
6.     Vigne, J., Zazzo, A., Cucchi, T., Briois, F. and Guilaine, J. (2014). The transportation of mammals to Cyprus shed light on early voyaging and boats in the mediterranean sea. Eurasian Prehistory 10, 157–176.

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/

In this paper [1], the authors describe how they apply machine learning with YOLOv5 to classify visual data, aiming to enhance understanding of archaeological phenomena before conducting destructive fieldwork. Despite challenges, the integration of machine learning with remote sensing technology was seen as transformative, enabling precise recording of areas of interest and assessment of environmental risk factors. The paper discusses successes, failures, and future directions in machine learning research, emphasising the need for standardisation and integration of streamlined methods. The application of machine learning techniques facilitates non-destructive analysis of material culture records, improving conservation efforts and offering insights into both past and contemporary phenomena. While the initial use of YOLOv5 showed potential for consistent detection of archaeological features, further refinement and dataset enlargement are deemed necessary for broader application in non-destructive archaeological surveying. The authors advocate for the integration of machine learning tools in archaeological research to save time, resources, and promote ethical digital recording practices. They highlight the importance of standardised methodologies to enhance credibility and reproducibility, aiming to contribute to the ongoing dialogue in computational archaeology.

Overall, I think this paper is a good step forward in detecting small objects in UAV data, and contains useful information for similar studies. The aim towards greater reproducibility and standardisation is of course shared more widely in the machine learning community, and this study is a good example of how to approach this.

References

[1] Sharp, K., Christofis, B., Eslamiat, H., Nepal, U. and Osores Mendives, C. (2024). Machine Learning for UAV and Ground-Captured Imagery: Toward Standard Practices. Zenodo, 8307612, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8307612