Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+tags:
+- raman-spectroscopy
+- biotechnology
+- benchtop-spectroscopy
+- pls-regression
+- tabular-regression
+license: cc-by-4.0
+language: en
+pretty_name: Raman Spectra of Bioprocess Analytes Anton 532
+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.
+
+## 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:** Timegate Pico-Raman M2
+* **Laser Wavelength:** 532 nm
+* **Exposure Time:** 90 s (Pulsed/Timegated)
+* **Laser Power:** Not specified (–)
+* **Scans per Sample:** 5
+* **Number of Samples:** 133
+* **Container Material:** Aluminum
+
+
+## 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},
+}
+```
+
+
+