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license: mit
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#
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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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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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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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**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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+- 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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The target data (CaCO3 and TOC) distribution:
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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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---
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license: mit
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---
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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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@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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@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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The target data (CaCO3 and TOC) distribution:
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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.
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