In a previous study we presented a new method that used summed probability distributions (SPD) of radiocarbon dates as a proxy for population levels, and Monte-Carlo simulation to test the significance of the observed fluctuations in the context of uncertainty in the calibration curve and archaeological sampling. The method allowed us to identify periods of significant short-term population change, caveated with the fact that around 5% of these periods were false positives. In this study we present an improvement to the method by applying a criterion to remove these false positives from both the simulated and observed distributions, resulting in a substantial improvement to both its sensitivity and specificity. We also demonstrate that the method is extremely robust in the face of small sample sizes. Finally we apply this improved method to radiocarbon datasets from 12 European regions, covering the period 8000–4000 BP. As in our previous study, the results reveal a boom-bust pattern for most regions, with population levels rising rapidly after the local arrival of farming, followed by a crash to levels much lower than the peak. The prevalence of this phenomenon, combined with the dissimilarity and lack of synchronicity in the general shapes of the regional SPDs, supports the hypothesis of endogenous causes

Large anthropogenic ¹⁴ C datasets are widely used to generate summed probability distributions (SPDs) as a proxy for past human population levels. However, SPDs are a poor proxy when datasets are small, bearing little relationship to true population dynamics. Instead, more robust inferences can be achieved by directly modelling the population and assessing the model likelihood given the data. We introduce the R package ADMUR which uses a continuous piecewise linear (CPL) model of population change, calculates the model likelihood given a ¹⁴ C dataset, estimates credible intervals using Markov chain Monte Carlo, applies a goodness-of-fit test, and uses the Schwarz Criterion to compare CPL models. We demonstrate the efficacy of this method using toy data, showing that spurious dynamics are avoided when sample sizes are small, and true population dynamics are recovered as sample sizes increase. Finally, we use an improved ¹⁴ C dataset for the South American Arid Diagonal to compare CPL modelling to current simulation methods, and identify three Holocene phases when population trajectory estimates changed from rapid initial growth of 4.15% per generation to a decline of 0.05% per generation between 10 821 and 7055 yr BP, then gently grew at 0.58% per generation until 2500 yr BP. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

The datasets described in this paper comprise the core spatial and temporal structure of the Cultural Evolution of Neolithic Europe project (EUROEVOL), led by Professor Stephen Shennan, UCL. This is one of three datasets resulting from the EUROEVOL project, the other two comprising the faunal (EUROEVOL Dataset 2) and archaeobotanical (EUROEVOL Dataset 3) data (http://discovery.ucl.ac.uk/1469811/). The dataset offers the largest repository of archaeological site and radiocarbon data from Neolithic Europe (4,757 sites and 14,053 radiocarbon samples), dating between the late Mesolithic and Early Bronze Age (Figure 1). It also offers the core archaeological information structured at the site and phase level, which provide the primary links between the datasets

This article presents a series of recommendations for the publication of archaeological data, to improve their usability. These 12 recommendations were formulated by archaeological data experts who mined thousands of publications for different data types (including funerary practices, accelerator mass spectrometry dating, stable isotopes, zooarchaeology, archaeobotany and pathologies) during the initial construction of the Big Interdisciplinary Archaeological Database (BIAD). We also include data harmonisation vocabularies utilised for the integration of data from different recording systems. The case studies we cite to illustrate the recommendations are grounded in examples from the published literature and are presented in a problem/solution format. Though practically oriented towards the facilitation of efficient databasing, these recommendations – which we refer to as the BIAD Standards – are broadly applicable by those who want to extract scientific data from archaeological information, those who work with a specific region or theoretical focus and journal editors and manuscript authors. We anticipate that the use of the BIAD Standards will increase the usability, visibility, interoperability and longevity of published data and also increase the citations of those publications from which data were mined. The Standards will also help frame a unified foundation to support the continued integration of the natural sciences with archaeological research in the future.