Humans and our long extinct evolutionary relatives have been using stone to fashion tools for well over two million years. While they only are the hardest and most durable parts of ancient tools that almost always included organic materials, they are the ubiquitous remnants of prehistoric technologies and adaptations – they form the backbone of most studies in Palaeolithic archaeology. Yet, the study of stone artefacts (lithics) has developed organically and with different methodological emphases in different countries, not least in Europe. What gets published and how, what we call these ancient artefacts, and what inferences we draw from them has varied over time and research historical fashion, and it still varies today between, for example, Anglophone and Francophone traditions but also between western and eastern Europe. The result of this beautiful but also messy development is a lack of a current terminology and taxonomy for lithic studies. This, in turn, makes it hard to conduct large-scale interregional comparisons and to consistently compare and understand past human adaptations. Can new cross-European collaborations and modern computational possibilities improve the situation?

In 2018, one of us (FR) was awarded an ERC Consolidator Grant for the project CLIOARCH (https://cas.au.dk/en/erc-clioarch) whose focus is to better understand the cultural transmission and adaptation of ancient hunter-gatherer populations that roamed the chilly environs of Europe during the final phases of the last ice age between ca. 15,000 and 11,000 years ago (Riede et al. 2020). The seeds of this project were sown much earlier though, in the context of some of our previous studies that showed how at least some of the ‘cultures’ that were said to populate Europe at this time turned out to be problematic when subject to closer scrutiny: many were defined on very limited archaeological material, some were defined on entirely different criteria, and the names given to these cultures followed little in the way of systematic procedure (e.g. Ivanovaitė et al. 2020; Riede 2017; Sauer and Riede 2019). It’s also often unclear what an archaeological culture is meant to reflect – a language group? A population? A community of practice? A forager band or tribal group? In sum, it can and has been argued that Palaeolithic archaeology – the discipline central in our understanding of human behavioural and cultural evolution – is in a sort of taxonomic crisis, where the analytical units we use to analyse and describe change and adaptation  are simply not robust enough (Reynolds and Riede 2019).

 House of cards? Archaeological interpretations of migration, adaptation, and other aspects of past lives during the Palaeolithic build on a pyramid of data that begins with the retrieval of artefacts from the ground. Through various transformations and with many in-between inferences, these objects are turned into data, including the definition of taxonomic groups – cultures – which serve a role much like species or genera in evolutionary biology; they are the units said to change and adapt. The problem is that of cards are pulled out at the base – if certain groupings turn out to be unfounded, the upper stories of this house may tumble down. From Reynolds & Riede (2019), based on an image by Lluisa Iborra licensed under CC BY 3.0; the figure is available at https://doi.org/10.6084/m9.figshare.8293784 and is licensed under CC BY 4.0).

To assess the current state of the art in European Late Glacial hunter-gatherer archaeology and to test the robustness of existing cultural taxonomies quantitatively, we had to collect a sufficiently large sample from across the continent. When CLIOARCH kicked off, we wanted to re-examine, measure, weigh, and code many of the stone tools from key sites with good dates, robust stratigraphies and sufficiently large samples of artefacts – in addition to working with published archaeological sources. Then came the COVID-19 pandemic and travel and access to sites and museum archives became impossible and entirely impractical. Therefore, we diverted all our efforts to working with published and legacy sources and improving our grasp on them. Knowledge progress does not only depend on the collection of new data and the improvement of site chronologies but is equally premised on better characterising and understanding what we already know. In fact, working with legacy data should, we believe, be of high priority in archaeology. In this spirit, we invited several colleagues – each a specialist in a particular region of Europe – to an online workshop. Using the skills and tricks we had all quickly learned during the pandemic, we really made an effort to make this workshop stimulating and fun to attend; we sent out a box of goodies, prepared guidelines for the presentations and discussions, set up a gamified online meeting space. During the workshop, we talked and exchanged information about the cultural taxonomies – the different ways of classifying and sorting stone tools into analytically useful categories – used across these different regions, compared, contrasted, and probed what the observed cultural patterns and changes might mean in terms of culture history, migration, and adaptation (Hussain et al. 2021). Most importantly, however, this workshop was just one step in a collaborative data collection protocol we developed such that extensive stone tool data could be collated into a single database. The database now published is the result of our collaborative efforts and is well suited for quantitatively testing the robustness of the existing cultural taxonomies, but also diachronically analysing macro-scale cultural evolutionary processes.

But getting to this point was quite the journey. To get a solid geographic coverage of Europe, we realised that additional colleagues needed to be invited into the fold. Our templates for data entry also needed to be re-assessed and modified in light of the author collective’s input. And the depth of differences in perspectives and priorities among the involved group of colleagues also became quite apparent. We were facing the challenge of negotiating and somehow overcoming – at least to the point of consensual agreement – the sorts of epistemic and empirical differences that our previous research historical research had pointed to, but this time in vivo and face-to-face. The resulting database – a truly collective effort – contains many thousands of artefact outlines, typological, and technological traits for all archaeological cultures included in it. We can hardly claim that it is without fault or truly comprehensive, but it is massive and rich. Putting it together has been ‘slow science’ (Alleva 2006) even if not always by design. It took some time from initial workshop to the publication of the database and our own initial analyses are underway (Riede et al. 2023), and more forthcoming we hope as we and others mine the resource that this database represents.

Collective effort Seemingly simple, our workflow demanded much work from both the core team in terms of preparing for data collection, guidance, and interaction, as well as from our expert contributors and co-authors whose input to the database was invaluable. The database now consists of many thousands of artefact images, typological and technological trait data for all cultural units and an extensive literature list that can be directly imported by users into their reference management software of choice.

Digital data and methods are said to revolutionize many aspects of archaeology (Bevan 2015; Gattaglia 2015), especially as Open Science methods are being embraced by many in the discipline (e.g. Lodwick 2019; Marwick 2017; Marwick et al. 2017), including lithic analysts (Maier et al. 2023; Matzig et al. 2021; Pargeter et al. 2023; Timbrell et al. 2022). Studies that make use of massive, collaborative, and expert-sourced data are being published with increasing frequency these days (Ellis et al. 2021) but in contrast to closely allied disciplines such as ecology and epidemiology (Blettner and Schlattmann 2005; Culina et al. 2018), archaeology does not have established protocols for meta-analysis and synthesis. This approach is still in a pioneer phase and faces many challenges related to digitization, representativity, and benchmarking – as well as with regard to finding appropriate analytical techniques to present and interrogate large swaths of ancient stone artefact data. We hope that our database can be inspiration and source for many more such works in the future.

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