The main error propagation algorithm in iolite was first published by Paton et al. (2010). Its purpose is to estimate the additional uncertainty to be added to the internal uncertainty calculated by iolite and is most often used in U-Pb studies.

NOTE: The following is a guest post by Graham Hagen-Peter (even though I -Bence- am listed as the author, it is Graham’s work; I am just posting it here).

The iolite GitHub repository contains many examples of additional modules for iolite written in python. It’s not always obvious how to download and use these files, so here’s a quick rundown of the process.

Laser logs and mass spec sample metadata speed up the process of creating selections in iolite considerably. However, sometimes we want to further refine those selections for a variety of reasons.

To import mass spec data into iolite, a few key pieces of information are required. The most basic of these are the time the data were collected and what masses were measured.