Radiocarbon and aoristic modelling

MOSAIC Summer School, Bibracte, 2–6 September 2024

Joe Roe

University of Bern

This morning:

Radiocarbon and aoristic dating
Modelling calendar probability distributions
Radiocarbon and aoristic modelling in R

This afternoon (practical):

Acquiring radiocarbon data with xronos: https://r.xronos.ch/articles/xronos.html
Preparing aoristic data with datplot: https://lsteinmann.github.io/datplot/articles/data_preparation.html
Calibrating radiocarbon dates: https://www.martinhinz.info/jekyll/update/blog/2016/06/03/simple_calibration.html
Summarising data with c14: https://github.com/joeroe/c14
Aggregating dates with rcarbon: https://github.com/ahb108/rcarbon/

Radiocarbon and aoristic dating

Radiocarbon dating¹

Ambers 2005, https://doi.org/10.1016/B0-12-369397-7/00019-4

OxA-1234 11200 ± 20 (uncal) BP

Sample metadata

Lab identifier (lab code + number)
Site and location
Coordinates
Depth
Material
Taxon
Pretreatment and measurement method

Calibration

ORAU, https://c14.arch.ox.ac.uk/explanation.php

Martin Hinz, https://www.martinhinz.info/blog/

Publication and compilation

Conventions for radiocarbon reporting

measurement (CRA or F14C)
laboratory identifier
sample material, pretreatment method, quality control measurements
calibration curve and any reservoir offset
details of software used for calibration
calibrated range(s) with associated probability

Aoristic dating

…not so straightforward?

Not all periods are equal
Duration ≠ uncertainty
Not actually a population statistic

Crema (2024, https://doi.org/10.1111/arcm.12984) proposes a new Bayesian alternative.

Modelling calendar probability distributions

Radiocarbon & aoristic dates are:

Coordinates in time
Modelled as probability functions of an event
Measured relative to an epoch

Summarise
Aggregate
Fit
Correlate
Map

Summarising

Aggregating

Fitting

Correlating

Martin et al. 2017, https://doi.org/10.1016/j.yqres.2016.07.001

Martin et al. in prep.

Mapping

Radiocarbon and aoristic modelling in R

Sources of radiocarbon data

Primary: the lab
Secondary: literature
Tertiary: compilations

Radiocarbon compilations

Roe et al. in prep, ‘XRONOS: an open data infrastructure for archaeological chronology’

Global compilations and metadatabases

c14bazAAR
https://docs.ropensci.org/c14bazAAR/

IntChron
https://intchron.org/

p3k14c
https://www.p3k14c.org/

XRONOS
https://xronos.ch

Sources of aoristic data

???

Cleaning data

Dates missing measurement data
Dates missing metadata
Duplicates
Incorrect or inconsistent (meta)data

Radiocarbon packages

Representing time: era, aion
Acquiring data: c14bazAAR, rIntChron, p3k14c, xronos
Calibration: c14, rcarbon, IntCal, BChron, oxcAAR, IntCal
Modelling: rcarbon (SPDs, cKDEs, spatiotemporal modelling), coffee, nimbleCarbon (Bayesian modelling), ArchaeoPhases (Bayesian post-processing), stratigraphr (stratigraphic modelling)

Aoristic packages

aoristic, aoristAAR, datplot, archSeries, kairos, baorista

Other options

https://c14.arch.ox.ac.uk/oxcal

Of course you don’t have to do this in R. The major alternative is OxCal, which offers a full suite of modelling tools for radiocarbon and other chronological data. It is very advanced, has both a well-documented scripting language and an easy-to-use GUI interface, and is available online for free. It is not open source, which raises some issues, but it can produce partially reproducible analyses if you share your scripts.

There is also ChronoModel and some libraries for chronological modelling in Python and Haskell.

I prefer to use R for a number of reasons, but primarily because I do a lot of other types of work in R, and I want the chronological modelling to be integrated alongside it. If you want to do chronological modelling alone, then you might find one of the other options better.