Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."

The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.

The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.

The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.

For info on running Jupyter Notebook, please visit (https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.

Our Model can be compared to similar previously published works (e.g. https://doi.org/10.1111/ter.12574). Model was trained using skikit-learn v1.41.

The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.

Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)

Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.

Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.

References for the dataset compilation are provided in File S3.

If you have any queries contact:Gary O'Sullivan, Trinity College Dublin