The paper “Transforming the CIDOC-CRM model into a megalithic monument property graph” describes an interesting endeavour of developing and implementing a CIDOC-CRM based knowledge graph using a native graph database (Neo4J) to represent megalithic information (Câmara et al. 2023). While there are earlier examples of using native graph databases and CIDOC-CRM in diverse heritage contexts, the present paper is useful addition to the literature as a detailed description of an implementation in the context of megalithic heritage. The paper provides a demonstration of a working implementation, and guidance for future projects. The described project is also documented to an extent that the paper will open up interesting opportunities to compare the approach to previous and forthcoming implementations. The same applies to the knowledge graph and use of CIDOC-CRM in the project.

Readers interested in comparing available technologies and those who are developing their own knowledge graphs might have benefited of a more detailed description of the work in relation to the current state-of-the-art and what the use of a native graph database in the built-heritage contexts implies in practice for heritage documentation beyond that it is possible and it has potentially meaningful performance-related advantages. While also the reasons to rely on using plain CIDOC-CRM instead of extensions could have been discussed in more detail, the approach demonstrates how the plain CIDOC-CRM provides a good starting point to satisfy many heritage documentation needs.

As a whole, the shortcomings relating to positioning the work to the state-of-the-art and reflecting and discussing design choices do not reduce the value of the paper as a valuable case description for those interested in the use of native graph databases and CIDOC-CRM in heritage documentation in general and the documentation of megalithic heritage in particular.

References

Câmara, A., de Almeida, A. and Oliveira, J. (2023). Transforming the CIDOC-CRM model into a megalithic monument property graph, Zenodo, 7981230, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7981230

The paper entitled “From paper to byte: An interim report on the digital transformation of two thing editions” submitted by Frederic Auth and colleagues discusses how those rich and often meticulously illustrated catalogues of particular find classes that exist in many corners of archaeology can be brought to the cutting edge of contemporary research through digitisation. This paper was first developed for a special conference session convened at the EAA annual meeting in 2021 and is intended for an edited volume on the topic of typology, taxonomy, classification theory, and computational approaches in archaeology.

Auth et al. (2023) begin with outlining the useful notion of the ‘thing-edition’ originally coined by Kerstin Hofmann in the context of her work with the many massive corpora of finds that have characterised, in particular, earlier archaeological knowledge production in Germany (Hofmann et al. 2019; Hofmann 2018). This work critically examines changing trends in the typological characterisation and recording of various find categories, their theoretical foundations or lack thereof and their legacy on contemporary practice. The present contribution focuses on what happens with such corpora when they are integrated into digitisation projects, specifically the efforts by the German Archaeological Institute (DAI), the so-called iDAI.world and in regard to two Roman-era material culture groups, the Corpus der römischen Funde im europäischen Barbaricum (Roman finds from beyond the empire’s borders in eight printed volumes covering thousands of finds of various categories), and the Conspectus Formarum Terrae Sigillatae Modo Confectae (Roman plain ware).

Drawing on Bruno Latour’s (2005) actor-network theory (ANT), Auth et al. discuss and reflect on the challenges met and choices to be made when thing-editions are to be transformed into readily accessible data, that is as linked to open, usable data. The intellectual and infrastructural workload involved in such digitisation projects is not to be underestimated. Here, the contribution by Auth et al. excels in the manner that it does not present the finished product – the fully digitised corpora – but instead offers a glimpse ‘under the hood’ of the digitisation process as an interaction between analogue corpus, research team, and the technologies at hand. These aspects were rarely addressed in the literature, rooted in the 1970s early work (Borillo and Gardin 1974; Gaines 1981), on archaeological computerised databases, focused on technical dimensions (see Rösler 2016 for an exception). Their paper can so also be read in the broader context of heterogeneous computer-assisted knowledge ecologies and ‘mangles of practice’ (see Pickering and Guzik 2009) in which practitioners and technological structures respond to each other’s needs and attempt to cooperate in creative ways. As such, Auth et al.’s considerations not least offer valuable resources for Science and Technology Studies-inspired discussions on the cross-fertilization of archaeological theory, practice and currently emerging material and virtual research infrastructures and can be read in conjunction to Gavin Lucas’ (2022) paper on ‘machine epistemology’ due to appear in the same volume. 

Perhaps more importantly, however, the work by Auth and colleagues (2023) exemplifies the due diligence required in not merely turning a catalogue from paper to digital document but in transforming such catalogues into long-lasting and patently usable repositories of generations of scholars to come. Deploying the Latourian notions of trade-off and recursive reference, Auth et al. first examine the structure, strengths, and weakness of the two corpora before moving on to showing how the freshly digitised versions offer new and alternative ways of analysing the archaeological material at hand, notably through immediate visualisation opportunities, through ceramic form combinations, and relational network diagrams based on the data inherent in the respective thing-editions.

Catalogues including basic descriptions and artefact illustrations exist for most if not all archaeological periods. They constitute an essential backbone of archaeological work as repeated access to primary material is impractical if not impossible. The catalogues addressed by Auth et al. themselves reflect major efforts on behalf of archaeological experts to arrive at clear and operational classifications in a pre-computerised era. The continued and expanded efforts by Auth and colleagues build on these works and clearly demonstrate the enormous analytical potential to make such data not merely more accessible but also more flexibly interoperable. Their paper will therefore be an important reference for future work with similar ambitions facing similar challenges. 

References

Auth, Frederic, Katja Rösler, Wenke Domscheit, and Kerstin P. Hofmann. 2023. “From Paper to Byte: A Workshop Report on the Digital Transformation of Two Thing Editions.” Zenodo. https://doi.org/10.5281/zenodo.8214563

Borillo, Mario, and Jean-Claude Gardin. 1974. Les Banques de Données Archéologiques. Marseille: Éditions du CNRS.

Gaines, Sylvia W., ed. 1981. Data Bank Applications in Archaeology. Tucson, AZ: University of Arizona Press.

Hofmann, Kerstin P. 2018. “Dingidentitäten Und Objekttransformationen. Einige Überlegungen Zur Edition von Archäologischen Funden.” In Objektepistemologien. Zum Verhältnis von Dingen Und Wissen, edited by Markus Hilgert, Kerstin P. Hofmann, and Henrike Simon, 179–215. Berlin Studies of the Ancient World 59. Berlin: Edition Topoi. https://dx.doi.org/10.17171/3-59

Hofmann, Kerstin P., Susanne Grunwald, Franziska Lang, Ulrike Peter, Katja Rösler, Louise Rokohl, Stefan Schreiber, Karsten Tolle, and David Wigg-Wolf. 2019. “Ding-Editionen. Vom Archäologischen (Be-)Fund Übers Corpus Ins Netz.” E-Forschungsberichte des DAI 2019/2. E-Forschungsberichte Des DAI. Berlin: Deutsches Archäologisches Institut. https://publications.dainst.org/journals/efb/2236/6674

Latour, Bruno. 2005. Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press.

Lucas, Gavin. 2022. “Archaeology, Typology and Machine Epistemology.” Zenodo. https://doi.org/10.5281/zenodo.7622162

Pickering, Andrew, and Keith Guzik, eds. 2009. The Mangle in Practice: Science, Society, and Becoming. The Mangle in Practice. Science and Cultural Theory. Durham, NC: Duke University Press.

Rösler, Katja. 2016. “Mit Den Dingen Rechnen: ‚Kulturen‘-Forschung Und Ihr Geselle Computer.” In Massendinghaltung in Der Archäologie. Der Material Turn Und Die Ur- Und Frühgeschichte, edited by Kerstin P. Hofmann, Thomas Meier, Doreen Mölders, and Stefan Schreiber, 93–110. Leiden: Sidestone Press.

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/

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

The paper outlines the ClaReNet project's exploration of computer-based methods for classifying Celtic coin series, specifically focusing on a hoard from Jersey [1]. They collaborated with Jersey Heritage and numismatists, utilising a large dataset of coin images. The process involves stages such as pre-sorting, size-based sorting, class/type identification, and die studies. They employed IT methods, including object detection and unsupervised learning, followed by supervised learning for data refinement. Collaboration with numismatic experts ensured data quality. The study highlighted challenges in classifying coins, suggesting techniques like image matching alongside convolutional neural networks (CNNs). The results demonstrate the efficacy of semi-automatic processes in coin classification, emphasising the importance of human-computer collaboration for successful outcomes.

Overall, this is a good paper, showing how we as archaeologists and numismatics can use existing tools and finetune them for our purposes; without the need for huge domain specific datasets. This research and related papers show how we can more effectively deal with the increasingly bigger data we deal with, saving time on the monotonous and labour intensive tasks, leaving us more time to deal with the big picture. An important strength of the work is the provided public software repository and the dataset. The paper is well written, and a number of images illustrate the methodology as well as the objects used.

Reference

[1] Deligio, C., Tolle, K., and Wigg-Wolf, D. (2024). Supporting the analysis of a large coin hoard with AI-based methods. Zenodo, 8301464, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8301464