Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+tags:
+- raman-spectroscopy
+- biotechnology
+- additive-determination
+- benchtop-spectroscopy
+- pls-regression
+license: cc-by-4.0
+language: en
+pretty_name: Raman Spectra of Bioprocess Analytes
+task_categories:
+- tabular-regression
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "train.parquet"
+  - split: validation
+    path: "val.parquet"
+  - split: test
+    path: "test.parquet"
+size_categories:
+- n<1K
+---
+
+## Dataset Overview
+
+This dataset contains  Raman spectra of mixtures of glucose, sodium acetate, and magnesium sulfate. It is part of a series of 8 datasets that use eight different spectrometers that measure nearly the same samples. Some datasets have a bit more samples than others. Each spectrum is paired with ground truth concentration labels verified by enzymatic assays, reflecting the concentration ranges typically found in E. coli fermentation processes. The data is specifically designed to support the training of non-linear models, such as Convolutional Neural Networks (CNNs), that can generalize across diverse hardware setups.
+
+## Target Parameters and Concentration Ranges
+
+The dataset contains measured Raman spectra of samples with different parameters from the following substances:
+
+* **Glucose**
+* **Acetate**
+* **Magnesium Sulfate**
+
+Reference values for the samples were measured using an HT analyzer (Cedex BioHT, Roche Diagnostics GmbH, Mannheim, Germany).
+
+## Data Acquisition
+
+Raman spectra were recorded using the follwing settings:
+
+* **Instrument:** Anton Paar Cora 5001
+* **Laser Wavelength:**  532 nm
+* **Exposure Time:** 5 s
+* **Laser Power:** 50 mW
+* **Scans per Sample:** 5
+* **Number of Samples:** 54
+* **Container Material:** Plastic
+
+
+## Citation
+
+Users should cite the original publication when using this dataset (Lange et. al. https://doi.org/10.1016/j.saa.2025.125861)
+
+or **BibTex:**
+
+```
+@article{
+  LANGE2025125861,
+  title = {Comparing machine learning methods on Raman spectra from eight different spectrometers},
+  journal = {Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy},
+  volume = {334},
+  pages = {125861},
+  year = {2025},
+  issn = {1386-1425},
+  doi = {https://doi.org/10.1016/j.saa.2025.125861},
+  url = {https://www.sciencedirect.com/science/article/pii/S1386142525001672},
+  author = {Christoph Lange and Maxim Borisyak and Martin Kögler and Stefan Born and Andreas Ziehe and Peter Neubauer and M. Nicolas Cruz Bournazou},
+  keywords = {Raman spectroscopy, Machine learning, Partial least squares, Convolutional neural network},
+}
+```
+
+
+