What’s new with geoChronR

It’s been a busy couple of years for geoChronR. In 2021 our paper, “geoChronR–an R package to model, analyze, and visualize age-uncertain data”, was published in Geochronology following an open review process. The paper lays out the motivation and underlying theory for geoChronR, and describes four common analytical use cases enabled by the package: correlation, regression, principal components analysis and spectral analysis.

You can read much more in detail, but here’s one example from the paper — time-uncertain principal components analysis.

The spatial loading pattern (a, b) and time series (c, d, e) for the first principal component shown in panels (a, c). The results for the second PC are shown in panels (b, d). The data density through time is shown in panel (e). For the maps, the median loadings of the ensemble are shown by the color scale, and the standard deviation of the loadings across ensemble members is depicted by the size of the markers, with larger markers showing smaller uncertainties. For the time series plots, the median of the ensemble is shown in black, and the 50 % and 95 % highest-probability density regions are shown in dark and light gray, respectively.

This multi-panel figure produced by geoChronR visualizes a complex analysis that propagates age uncertainty through principal components analysis. In this case we’re looking at a multiple proxies from the North Atlantic, to get a sense of the predominant spatiotemporal patterns in the region for the past 2000 years.

In February 2022 we hosted our third geoChronR training workshop, which we hosted online only for the first time. We had 45 participants in17 countries represented!

The 2022 workshop also marked the initial release of our online textbook “Time-Uncertain Data Analysis in R”. The textbook is designed to walk readers through the philosophy and practice of time-uncertain data analysis, and relies heavily on LinkedEarth products. In addition to background and tutorials, TUDAR also includes exercises designed to get users hands on with geoChronR.

Among other topics, TUDAR includes modules that walk users through learning to work with Linked PaleoData (LiPD) in R:

Creating an age model using the various options in geoChronR:

Time uncertain correlation:

And time uncertain spectral analysis:

If you’re interested in learning more about how to use LiPD data and geoChronR, Time Uncertain Data Analysis in R is a great place to start, and we plan to continue growing and developing this resource as time goes on. Check it out!

Finally, it is exciting to see the number of users of geoChronR continue to grow, and more and more new studies published that use geoChronR and other LinkedEarth products to do exciting science. For example, Devon Gorbey and colleagues used geoChronR as part of their investigation of leaf wax δD at Lake Qaupat on southern Baffin Island. By creating age models in geoChronR and propagating those uncertainties through their analyses, they were able to find a robust relation between leaf wax δD at the lake and Labrador Sea surface temperatures through the late Holocene. This gave insight to the role of local moisture sources on summer precipitation isotopes, and late Holocene climate evolution in the region.

Enabling scientists to conduct robust, efficient and reproducible time-uncertain analysis is the big idea behind geoChronR, and it’s been great to see the community of scientists working with geoChronR really expand and mature over these past couple of years. We’re continuing to add features, fix bugs, and develop more training materials for geoChronR. You can learn more, see new features and report bugs on Github. We look forward to hearing from you!