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  license: mit
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- # Pretraining Foundation Models: Unleashing the Power of Forgotten Spectra for Advanced Geological Applications
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- The dataset for masked autoencoder for X-ray fluorescence (XRF) is a following development after the dataset [(Chao et al., 2022)](https://doi.org/10.1594/PANGAEA.949225).
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  Besides the published XRF spectra-target measurements (CaCO3 and TOC) pairs of data, we further upload the XRF spectra in that project but without alignments of the target measurements here.
 
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  They are compiled in a machine learning ready format, which we expect for convenient implementation of other studies.
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- The investigated cores, which form the datast, are mostly retrieved across the high- to mid-latitude Northwest Pacific (37°N-52°N) and the Pacific sector of the Southern Ocean (53°S-63°S), with a water depth coverage from 1211 to 4853 m:
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- 1. Cruise SO264 in the subarctic Northwest Pacific with R/V SONNE in 2018
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- 2. Cruise PS97 in the central Drake Passage with RV Polarstern in 2016
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- 3. Cruise PS75 in the Pacific sector of the Southern Ocean in 2009/2010.
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- 4. Cruise KOMEX I and KOMEX II with R/V Akademik Lavrentyev in 1998 and cruise SO178 in 2004 in the Okhotsk Sea.
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-
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- For more information, please checkout the published paper [(Lee et al., 2022)](https://doi.org/10.1038/s41598-022-25377-x) and previous dataset [(Chao et al., 2022)](https://doi.org/10.1594/PANGAEA.949225).
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- The direct use of this dataset is documented in the GitHub [repo](https://github.com/dispink/xpt).
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-
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- **Data structure**
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- - **raw**: Raw spectra in the Avaatech XRF Core Scanner format. Each subfolder contains the raw data for a core series.
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- - **legacy**: Previously compiled and raw data in [(Lee et al., 2022)](https://doi.org/10.1038/s41598-022-25377-x).
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- - **pretrain**: Data used for pre-training and is built from the previously compiled spectra data `legacy/spe_dataset_20220629.csv`. The `train` subfolder has the training and validation sets. The `test` subfolder contains the data selected during fine-tuning as the zero-shot test, i.e., case study in the published paper.
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-
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- ```
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- +- train
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- +- spe (all spetra)
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- +- info.csv (training set spectrum list)
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- +- val.csv (validation set spectrum list)
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- +- test
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- +- spe
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- +- info.csv (case study spectrum list)
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- ```
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- - **fine-tune**: Data used for fine-tuning. The `train` subfolder has the training and validation sets. The `test` subfolder is the data for zero-shot test, i.e., case study in the published paper.
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- ```
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- +- CaCO3%
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- +- train
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- +- spe (all spetra)
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- +- target (all target measurements)
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- +- info.csv (training set spectrum-target pair list)
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- +- info_#.csv (splits from the info.csv in different data amounts)
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- +- val.csv (validation set spectrum-target pair list)
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- +- test
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- (same as in train)
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- +- TOC%
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- (same as in CaCO3%)
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- ```
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-
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- The target data (CaCO3 and TOC) distribution:
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- ![Data distribution](https://raw.githubusercontent.com/dispink/xpt/dev/files/data_hist.png)
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-
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  The case study (i.e., test set) is composed of three cores ('PS75-056-1', 'LV28-44-3', 'SO264-69-2') isolated from the beginning and not used in both the pre-training and fine-tuning process.
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  The rest of data are randomly split in to the trainging and validation sets wtih 4:1 ratio.
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  The script is `src/datas/build_data.py` in the GitHub [repo](https://github.com/dispink/xpt).
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  **Acknowledgements**
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- We thank the crew and the science parties of different cruises for their contributions to core and sample acquisition on the respective expeditions.
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- We are very grateful to Dr. Weng‐Si Chao, Dr. Lester Lembke‐Jene, and Dr. Frank Lamy for providing these data.
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  We also sincerely thank Valéa Schumacher, Susanne Wiebe, and Rita Fröhlking and student assistants at the AWI Marine Geology Laboratory in Bremerhaven for technical assistance with XRF-scanning, CaCO3 and TOC measurements.
 
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  license: mit
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+ # High-latitude Pacific Ocean Sediment Geochemistry and XRF Data for Geoscientific Foundation Models
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+ This dataset is a following development after the dataset [(Chao et al., 2022)](https://doi.org/10.1594/PANGAEA.949225), which inculdes the geochemical records from the high-latitude sectors of Pacific Ocean.
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  Besides the published XRF spectra-target measurements (CaCO3 and TOC) pairs of data, we further upload the XRF spectra in that project but without alignments of the target measurements here.
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+ In total, it has 59,828 XRF spectra, 2,254 CaCO3 measurements, and 2,363 TOC measurements.
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  They are compiled in a machine learning ready format, which we expect for convenient implementation of other studies.
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+ This dataset is used for training and validating the first foundation model for X-ray Fluorescence [(lee et al., 2025)](https://doi.org/10.1029/2025JH000754).
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+ It's also published on Zenodo. For more details, please refer to the [Zenodo dataset](https://doi.org/10.5281/zenodo.16354050).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  The case study (i.e., test set) is composed of three cores ('PS75-056-1', 'LV28-44-3', 'SO264-69-2') isolated from the beginning and not used in both the pre-training and fine-tuning process.
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  The rest of data are randomly split in to the trainging and validation sets wtih 4:1 ratio.
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  The script is `src/datas/build_data.py` in the GitHub [repo](https://github.com/dispink/xpt).
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+ If you use the data, please cite the paper and dataset properly. The citations are:
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+
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+ ```
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+ @article{https://doi.org/10.1029/2025JH000754,
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+ author = {Lee, An-Sheng and Pao, Yu-Wen and Lin, Hsuan-Tien and Liou, Sofia Ya Hsuan},
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+ title = {Cross-Project Deep-Sea Sediment Geochemistry From XRF Spectra: A Self-Supervised Foundation Model (MAX)},
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+ journal = {Journal of Geophysical Research: Machine Learning and Computation},
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+ volume = {2},
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+ number = {3},
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+ pages = {e2025JH000754},
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+ keywords = {X-ray fluorescence, geochemistry, deep learning, self-supervised learning, foundation model, sediment cores},
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+ doi = {https://doi.org/10.1029/2025JH000754},
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+ note = {e2025JH000754 2025JH000754},
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+ year = {2025}
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+ }
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+
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+ @dataset{lee_2025_16354051,
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+ author = {Lee, An-Sheng and
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+ Pao, Yu-Wen},
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+ title = {High-latitude Pacific Ocean Sediment Geochemistry
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+ and XRF Data for Geoscientific Foundation Models
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+ },
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+ month = jul,
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+ year = 2025,
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+ publisher = {Zenodo},
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+ version = {v1.0.0},
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+ doi = {10.5281/zenodo.16354051},
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+ url = {https://doi.org/10.5281/zenodo.16354051},
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+ }
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+ ```
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+
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+
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+ The target data (CaCO3 and TOC) distribution:
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+ ![Data distribution](https://raw.githubusercontent.com/dispink/xpt/dev/files/data_hist.png)
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+
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  **Acknowledgements**
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+ We thank the crew and the science parties of different cruises for their contributions to core and sample acquisition on the respective expeditions.
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+ We are very grateful to Dr. Weng‐Si Chao, Prof. Dr. Ralf Tiedemann, Dr. Lester Lembke‐Jene, and Dr. Frank Lamy for providing these data.
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  We also sincerely thank Valéa Schumacher, Susanne Wiebe, and Rita Fröhlking and student assistants at the AWI Marine Geology Laboratory in Bremerhaven for technical assistance with XRF-scanning, CaCO3 and TOC measurements.