add csv version of dataset including description

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+## Dataset Overview
+
+This dataset, designed for biotechnological applications, provides a valuable resource for calibrating models used in high-throughput bioprocess development, particularly for bacterial fermentations. It features **Raman spectra** of samples containing varying, **statistically independent concentrations** of eight key metabolites, along with mineral salt medium and antifoam.
+
+The primary motivation behind this dataset is to offer a **calibration resource** with **uncorrelated metabolite concentrations**, addressing a common challenge in biotechnological applications. By ensuring the independence of substance concentrations, the dataset is ideal for training robust models and for integration with use-case specific labeled spectra.
+
+---
+## Substances and Concentration Ranges
+
+The dataset includes the following eight pure substances (metabolites) chosen for their relevance to fermentation processes:
+
+* **Glucose**
+* **Glycerol**
+* **Acetate**
+* **EnPump**
+* **Nitrate**
+* **Yeast Extract**
+* **Phosphate**
+* **Sulfate**
+
+Concentrations for each substance were randomly sampled from a uniform distribution within ranges relevant for bacterial fermentations. This approach ensures that all possible combinations of concentrations have an equal chance of appearing in the dataset, effectively preventing correlations among the substances. Lower concentrations were deliberately oversampled to enhance predictive accuracy for models trained on data within that range.
+
+---
+## Additional Components and Their Impact
+
+To better mimic real-world conditions encountered during bacterial fermentations, varying concentrations of **mineral salt medium** and **antifoam** were also included in the samples.
+
+* **Antifoam** significantly impacts signal intensity; increased amounts lead to greater turbidity and a decrease in overall signal. By incorporating varying antifoam concentrations, the dataset helps ensure that models trained on this data are **agnostic to changing concentrations of antifoam or mineral salt medium**, improving their applicability in diverse experimental setups.
+
+---
+## Data Acquisition
+
+Samples were prepared using a Liquid Handler (LiHa) system. The process involved:
+
+1.  **Determining exact concentrations** for each substance in a sample.
+2.  **Creating the sample** with the specified concentrations.
+3.  **Recording Raman spectra** of the prepared sample.
+
+
+More details on the creation process can be found in the paper: https://dx.doi.org/10.2139/ssrn.5239248
+As soon as this paper is published in a peer-reviewed journal, we will add the link.

test.csv

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+oid sha256:23e05069572b1d58283b7c1ca1353a3e46c04f34902d720d6d89f6abc4701b68
+size 9272282

train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:03c6e2205b9268e4e2063dbf3f967f5a4a53907a2da29c05199047a0707fdf0e
+size 63959528

val.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3c03d48b7efedd1c6f655c3d5e5e001b02b727d8dba326964c037a0859374a13
+size 18593889