Starting yesterday, September 10, Mastodon instances with open or approval-based registration have been receiving automated sign-ups with a recognisable patt...

A special interest group of [CAA International](https://caa-international.org/) dedicated to scientific scripting languages in archaeology.

An international research team led by Dr. Gabriele Russo of the Senckenberg Centre for Human Evolution and Palaeoenvironment at the University of Tübingen has re-examined historical fossil collections from Lebanon using modern methodology. The results, published today in two complementary studies in the journal Communications Earth & Environment, show that the Levant served as an important corridor for animals and various human lineages during the Pleistocene and that environmental conditions in the central part of the region differed significantly from those in the south.

era v0.6.0 is now available on CRAN: install.packages("era") library(era) This minor release adds functions for chronological comparison of year vectors: x <- yr(c(200, 100, 300), "BCE") yr_earlier_than(x, yr(150, "BCE")) #> # BCE years : #> [1] FALSE FALSE TRUE #> # Era: Before Common Era (BCE): Gregorian years (365.2425 days), counted backwards from 1 yr_later_than(x, yr(150, "BCE")) #> # BCE years : #> [1] TRUE FALSE FALSE #> # Era: Before Common Era (BCE): Gregorian years (365.2425 days), counted backwards from 1 And for calculating chronological differences between year vectors: yr_difference(yr(300, "BCE"), yr(100, "BCE")) #> # years : #> [1] 200 #> # Era: Gregorian years (365.2425 days) These functions are aware of era directionality: “BCE” years count backwards, so 300 BCE is earlier than 100 BCE. They support work on tempo, a new package I am working on that represents chronological intervals and the temporal relations between them. Links era on CRAN era package documentation Introductory vignette Source code (GitHub)

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controller is an R package for working with controlled vocabularies. It’s first release (v0.1.0) is now available now on CRAN. The package addresses something I find myself doing very often in analysis code: tidying messy and inconsistent terminologies. For smaller datasets, dplyr::recode() is okay for this, but writing the mapping out as an R function call gets tedious fast when dealing with a long list of terms. It becomes very tedious when you have variants distinguished only by things like capitalisation (OxA- vs. oxa-), word boundaries (Çatalhöyük vs. Çatal Höyük) or character encoding (ʿAin Ghazal vs. ʽAyn Ghazal). controller instead defines preferred terms and their variants in a data frame. Its control() verb is the equivalent of dplyr::recode() but using this thesaurus and with a few extra bells and whistles for fuzzy matching and reporting what was (and wasn’t) changed: library(controller) data("colour_thesaurus") shades <- c("daffodil", "purple", "magenta", "azure", "navy", "violet") control(shades, colour_thesaurus) #> Replaced values: #> ℹ daffodil → yellow #> ℹ azure → blue #> ℹ navy → blue #> ℹ violet → purple #> Warning: Some values of `x` were not matched in `thesaurus`: #> ✖ magenta Fuzzy matching means we don’t need to exhaustively list those variants from things like differences in case, word boundaries, or character encoding: control_ci(toupper(shades), colour_thesaurus) #> Replaced values: #> ℹ DAFFODIL → yellow #> ℹ PURPLE → purple #> ℹ AZURE → blue #> ℹ NAVY → blue #> ℹ VIOLET → purple #> [1] "yellow" "purple" "MAGENTA" "blue" "blue" "purple" #> Warning message: #> Some values of `x` were not matched in `thesaurus`: #> ✖ MAGENTA This package has been hanging around for a while! It started off as a helper function I used for cleaning up site names from prehistoric sites in Southwest Asia. The basic idea was inspired by similar functions that used to exist in c14bazAAR for cleaning sample metadata for radiocarbon date, that I thought were quite neat. So when the maintainers of that package decided to deprecate those, I took over the thesauri as part of c14 and spun the supporting functions off into controller as a standalone package. Then over the years it acquired some more functionality for working with controlled vocabularies (a surprising gap in the R ecosystem), like reading heritage vocabularies in Historic England’s FISH format. Five years later, I am finally getting around to releasing it on CRAN because I need to release c14 on CRAN, because that’s used in analyses I’m now publishing. It’s the research software engineering of changing a lightbulb, basically. The first release of controller includes: control(), control_ci(), and control_fuzzy() for recoding values control_names(), control_names_ci(), and control_names_fuzzy() for recoding names control_matches() for inspecting how matches were made read_fish() for reading vocabularies in Historic England’s FISH format colour_thesaurus, an example dataset You can install it from CRAN: install.packages("controller") Or the development version from GitHub: remotes::install_github("joeroe/controller") You can find the full documentation at https://controller.joeroe.io.

Calculates the probability that a calibrated radiocarbon date falls within a given calendar age range or a single year.

An article dispelling myths about W Social, the new European platform that aims to rival X: it is a fork of Bluesky that shares many similarities with Eurosky and requires government ID to sign up.

hcommons.social is a microblogging network supporting scholars and practitioners across the humanities and around the world.