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Raman-Data: A Unified Python Library for Raman Spectroscopy Datasets

PyPI version GitHub

This project aims to create a unified Python package for accessing various Raman spectroscopy datasets. The goal is to provide a simple and consistent API to load data from different sources like Kaggle, Hugging Face, GitHub, and Zenodo. This will be beneficial for the Raman spectroscopy community, enabling easier evaluation of models, such as foundation models for Raman spectroscopy.

✨ Features

  • A single, easy-to-use Python package available on PyPI.
  • Automatic downloading and caching of datasets from their original sources.
  • A unified data format for all datasets.
  • A simple function to list available datasets, with filtering options.
  • Datasets are annotated with an application domain (APPLICATION_TYPE) for easy filtering:
    • MaterialScience -- mineral identification, pigment libraries
    • Biological -- bioprocess monitoring, fermentation, agricultural phenotyping
    • Medical -- clinical diagnostics, pathogen identification, disease screening
    • Chemical -- fuel analysis, chemical quantification, polymer characterisation

📦 Installation

Install directly from PyPI:

pip install raman-data

Or install from source:

# Clone the repository
git clone https://github.com/ml-lab-htw/raman_data.git
cd raman_data

# Install the package
pip install -e .

Note: For Kaggle datasets, you need to configure your Kaggle API credentials. See Kaggle API documentation for details.

🚀 Getting Started

The basic interface for the package is defined in raman_data/__init__.py. Here's a preview of how it works:

from raman_data import raman_data
# To specify a task type or application domain, import these enums as well
from raman_data import TASK_TYPE, APPLICATION_TYPE

# List all available datasets
print(raman_data())

# List only classification datasets
print(raman_data(task_type=TASK_TYPE.Classification))

# List only medical datasets
print(raman_data(application_type=APPLICATION_TYPE.Medical))

# Combine filters: only medical classification datasets
print(raman_data(task_type=TASK_TYPE.Classification, application_type=APPLICATION_TYPE.Medical))

# Load a dataset by name
dataset = raman_data(dataset_name="codina_diabetes_AGEs")

# Access the spectra (intensity data), raman_shifts (wavenumbers), targets, and metadata
spectra = dataset.spectra           # 2D array: (n_samples, n_wavenumbers)
raman_shifts = dataset.raman_shifts # 1D array: wavenumber values in cm⁻¹
targets = dataset.targets            # Target labels or values
metadata = dataset.metadata         # Dataset metadata (source, paper, description)

print(f"Number of spectra: {dataset.n_spectra}")
print(f"Raman shift range: {dataset.min_shift} - {dataset.max_shift} cm⁻¹")
print(metadata)

For more detailed examples see Demo Notebook or Demo Script.

Dataset Name Application Task Type Description
acetic_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
adenine_colloidal_gold Chemical Regression Quantitative SERS spectra of adenine measured using colloidal gold substrates across 15 different European laboratories. Benchmarks model reproducibility and inter-instrumental variability.
adenine_colloidal_silver Chemical Regression Quantitative SERS spectra of adenine measured using colloidal silver substrates across 15 different European laboratories. Benchmarks model reproducibility and inter-instrumental variability.
adenine_solid_gold Chemical Regression Quantitative SERS spectra of adenine measured using solid gold substrates across 15 different European laboratories. Benchmarks model reproducibility and inter-instrumental variability.
adenine_solid_silver Chemical Regression Quantitative SERS spectra of adenine measured using solid silver substrates across 15 different European laboratories. Benchmarks model reproducibility and inter-instrumental variability.
alzheimer Medical Classification Raman spectra from dried saliva drops targeting Alzheimer's Disease (PD) vs. healthy controls. Reveals hidden trends in proteins, lipids, and saccharides for early detection of cognitive and motor impairment.
amino_acids_glycine Chemical Regression Time-resolved (on-line) Raman spectra for Glycine elution using a vertical flow LC-Raman method. Features 785 nm excitation and 0.2s exposure frames to benchmark label-free analyte detection.
amino_acids_leucine Chemical Regression Time-resolved (on-line) Raman spectra for Leucine elution using a vertical flow LC-Raman method. Features 785 nm excitation and 0.2s exposure frames to benchmark label-free analyte detection.
amino_acids_phenylalanine Chemical Regression Time-resolved (on-line) Raman spectra for Phenylalanine elution using a vertical flow LC-Raman method. Features 785 nm excitation and 0.2s exposure frames to benchmark label-free analyte detection.
amino_acids_tryptophan Chemical Regression Time-resolved (on-line) Raman spectra for Tryptophan elution using a vertical flow LC-Raman method. Features 785 nm excitation and 0.2s exposure frames to benchmark label-free analyte detection.
bacteria_identification Medical Classification 60,000 spectra from 30 clinically relevant bacterial and yeast isolates (including an MRSA/MSSA isogenic pair). Acquired with 633 nm illumination on gold-coated silica substrates with low SNR to simulate rapid clinical acquisition times.
biomolecules_reference Biological Classification Reference Raman spectra (450–1800 cm⁻¹, 1 cm⁻¹ resolution) of ~140 pure biomolecules including amino acids, nucleotides, lipids, and sugars. Each spectrum is labelled by biomolecule name. Useful for spectral assignment and as a reference library for classification benchmarks.
bioprocess_analytes_anton_532 Biological Regression 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
bioprocess_analytes_anton_785 Biological Regression 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
bioprocess_analytes_kaiser Biological Regression 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
bioprocess_analytes_metrohm Biological Regression 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
bioprocess_analytes_mettler_toledo Biological Regression 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
bioprocess_analytes_tec5 Biological Regression 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
bioprocess_analytes_timegate Biological Regression 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
bioprocess_analytes_tornado Biological Regression 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
bioprocess_substrates Biological Regression A benchmark dataset of 6,960 spectra featuring eight key metabolites (glucose, glycerol, acetate, etc.) sampled via a statistically independent uniform distribution. Designed to evaluate regression robustness against common bioprocess correlations, including background effects from mineral salts and
cancer_cell_(cooh)2 Biological Classification SERS spectra of cancer cell metabolites collected on gold nanourchins functionalized with the (COOH)2 moiety. Designed to provide specificity toward specific proteins and lipids for cell line identification.
cancer_cell_cooh Biological Classification SERS spectra of cancer cell metabolites collected on gold nanourchins functionalized with the COOH moiety. Designed to provide specificity toward specific proteins and lipids for cell line identification.
cancer_cell_nh2 Biological Classification SERS spectra of cancer cell metabolites collected on gold nanourchins functionalized with the NH2 moiety. Designed to provide specificity toward specific proteins and lipids for cell line identification.
citric_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
covid19_salvia Medical Classification Curated for non-invasive SARS-CoV-2 screening. Includes ~25 spectral replicates per subject from 101 patients (positive, negative symptomatic, and healthy controls) acquired from dried saliva drops using a 785 nm spectrometer.
covid19_serum Medical Classification This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed.
deepr_denoising Unknown Denoising Raman spectral denoising dataset from DeepeR paper. Contains noisy input spectra and corresponding denoised target spectra for training deep learning denoising models.
deepr_super_resolution Unknown SuperResolution Hyperspectral super-resolution dataset from DeepeR paper. Contains low-resolution input spectra and high-resolution target spectra for training super-resolution models.
diabetes_skin_ages Medical Classification Part of the Diabetes Skin Raman Dataset. This subset focuses on Advanced Glycation End-products (AGEs) signatures in the skin. Data acquired in vivo using a portable 785 nm Raman spectrometer to discern between diabetic patients and healthy controls.
diabetes_skin_ear_lobe Medical Classification Part of the Diabetes Skin Raman Dataset. This subset focuses on Advanced Glycation End-products (Ear Lobe) signatures in the skin. Data acquired in vivo using a portable 785 nm Raman spectrometer to discern between diabetic patients and healthy controls.
diabetes_skin_inner_arm Medical Classification Part of the Diabetes Skin Raman Dataset. This subset focuses on Advanced Glycation End-products (Inner Arm) signatures in the skin. Data acquired in vivo using a portable 785 nm Raman spectrometer to discern between diabetic patients and healthy controls.
diabetes_skin_thumbnail Medical Classification Part of the Diabetes Skin Raman Dataset. This subset focuses on Advanced Glycation End-products (Thumbnail) signatures in the skin. Data acquired in vivo using a portable 785 nm Raman spectrometer to discern between diabetic patients and healthy controls.
diabetes_skin_vein Medical Classification Part of the Diabetes Skin Raman Dataset. This subset focuses on Advanced Glycation End-products (Vein) signatures in the skin. Data acquired in vivo using a portable 785 nm Raman spectrometer to discern between diabetic patients and healthy controls.
ecoli_fermentation Biological Regression Spectra captured during batch and fed-batch fermentation of E. coli. Measurements were performed on the supernatant using a 785 nm spectrometer to track glucose and acetate concentrations in a dynamic, high-throughput bioprocess environment.
ecoli_metabolites Biological Regression This dataset contains Raman spectra of mixtures of glucose and sodium acetate which are the most important metabolites during Ecoli Fermentations. The spectra were measured with a liquid handling station and a system for automatic Raman spectra measurements used in High-Throughput Experimentation
ecoli_metabolites_dig4bio Biological Regression This dataset contains Raman spectra of mixtures of glucose, sodium acetate, and magnesium sulfate. These components are important during E. Coli fermentation processes. The spectra were measured with a liquid handling station and a system for automatic Raman spectra measurements used in High-Throug
flow_microgel_synthesis Chemical Regression This data set contains in-line Raman spectroscopy measurements and predicted microgel sizes from Dynamic Light Scattering (DLS).The Raman spectroscopy measurements were conducted inside a customized measurement cell for monitoring in a tubular flow reactor.Inside the flow reactor, the microgel synth
formic_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
fuel_benchtop Chemical Regression Raman spectra from 179 commercial gasoline samples recorded using a benchtop 1064 nm FT-Raman system. Targets include Research Octane Number (RON), Motor Octane Number (MON), and oxygenated additive concentrations.
fuel_handheld Chemical Regression Counterpart to the benchtop fuel dataset, acquired from the same 179 samples using a handheld 785 nm spectrometer. Used for benchmarking model transferability across different hardware and wavelengths.
hair_dyes_sers Chemical Classification SERS spectra of commercial hair dye products acquired with a portable Raman spectrometer. Each spectrum is labelled by brand, permanence (permanent/semi-permanent/temporary), and colour. Target: brand identity (classification).
head_neck_cancer Medical Classification Raman spectra of blood plasma and saliva samples from head and neck cancer patients and healthy controls. Acquired for non-invasive liquid biopsy screening. Target: cancer vs. control (binary classification).
illicit_adulterants_ft_raman Medical Classification FT-Raman spectra (1064 nm, ~33–3600 cm⁻¹, 1851 points) of 11 SERS-active pharmaceutically active adulterants commonly found in adulterated dietary supplements. Acquired with a benchtop Bruker RAM II FT-IR Raman module. One spectrum per compound. Target: compound identity (classification).
illicit_adulterants_sers Medical Classification SERS spectra (785 nm, 400–2300 cm⁻¹, 1901 points) of 11 SERS-active illicit adulterants found in dietary supplements. Acquired with a portable Metrohm MISA analyzer using silver printed-SERS substrates. One spectrum per compound. Target: compound identity (classification).
itaconic_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
levulinic_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
microgel_size_lf_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Linear Fit, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_lf_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Linear Fit, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_mm_lf_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: MinMax + Linear Fit, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_mm_lf_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: MinMax + Linear Fit, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_mm_rb_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: MinMax + Rubber Band, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_mm_rb_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: MinMax + Rubber Band, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_raw_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Raw, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_raw_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Raw, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_rb_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Rubber Band, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_rb_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: Rubber Band, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_snv_lf_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: SNV + Linear Fit, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_snv_lf_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: SNV + Linear Fit, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_size_snv_rb_fingerprint Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: SNV + Rubber Band, spectral range: FingerPrint. Task: predict particle diameter from Raman spectrum.
microgel_size_snv_rb_global Chemical Regression Raman spectra of 235 microgel samples with DLS-measured particle diameters (208–483 nm). Pretreatment: SNV + Rubber Band, spectral range: Global. Task: predict particle diameter from Raman spectrum.
microgel_synthesis Chemical Regression This data set contains in-line Raman spectroscopy measurements inside a customized measurement cell for monitoring in a tubular flow reactor. The setup aims at monitoring the microgel synthesis in a flow reactor while aiming at a high measurement precision. The measurements include a systematic accu
microplastics_weathered MaterialScience Classification Raman spectra of 167 virgin and UV-weathered microplastic particles spanning multiple common polymer types (PE, PP, PS, PET, PVC, etc.). Files prefixed 'sta-' are unweathered standards; 'wea-' are UV-aged samples. Target: polymer type (classification).
mlrod MaterialScience Classification 500,000+ Raman spectra of common rock-forming silicate, carbonate, and sulfate minerals under low signal-to-noise-ratios, Mars-like conditions. No traditional spectral preprocessing such as cosmic ray or baseline removal was employed.
organic_compounds_preprocess Chemical Classification Preprocess Raman spectra of organic compounds collected with several different excitation sources. Designed to benchmark transfer learning and domain adaptation for chemical identification with limited data.
organic_compounds_raw Chemical Classification Raw Raman spectra of organic compounds collected with several different excitation sources. Designed to benchmark transfer learning and domain adaptation for chemical identification with limited data.
parkinson Medical Classification Raman spectra from dried saliva drops targeting Parkinson's Disease (PD) vs. healthy controls. Reveals hidden trends in proteins, lipids, and saccharides for early detection of cognitive and motor impairment.
pharmaceutical_ingredients Medical Classification A Raman spectral dataset comprising 3,510 spectra from 32 chemical substances. This dataset includes organic solvents and reagents commonly used in API development, along with information regarding the products in the XLSX, and code to visualise and perform technical validation on the data.
ralstonia_fermentations Biological Regression Monitoring of P(HB-co-HHx) copolymer synthesis in Ralstonia eutropha batch cultivations. Includes a hybrid mix of experimental and high-fidelity synthetic data to handle high multicollinearity between process variables.
rruff_mineral_preprocess MaterialScience Classification Comprehensive resource of raw Raman spectra for over 1,000 mineral species, representing a diverse array of crystallographic structures and chemical compositions measured under varying experimental conditions (e.g., 532 nm and 785 nm).
rruff_mineral_raw MaterialScience Classification Comprehensive resource of raw Raman spectra for over 1,000 mineral species, representing a diverse array of crystallographic structures and chemical compositions measured under varying experimental conditions (e.g., 532 nm and 785 nm).
succinic_acid_species Chemical Regression Raman spectra and composition data for titration experiments of various acids in aqueous solution. Includes acetic, citric, formic, itaconic, levulinic, oxalic, and succinic acids. Data for concentration monitoring and indirect hard modeling.
sugar_mixtures_high_snr Chemical Regression The high signal-to-noise ratio subset of the Sugar Mixtures benchmark (7,680 measurements at 0.5 s integration). Used for evaluating the noise-robustness of hyperspectral unmixing and quantification algorithms.
sugar_mixtures_low_snr Chemical Regression The low signal-to-noise ratio subset of the Sugar Mixtures benchmark (7,680 measurements at 0.5 s integration). Used for evaluating the noise-robustness of hyperspectral unmixing and quantification algorithms.
synthetic_organic_pigments_baseline_corrected MaterialScience Regression Baseline Corrected Raman spectral library comprising nearly 300 reference spectra of synthetic organic pigments (SOPs). Designed for spectral matching and identification of pigments in modern and contemporary art conservation.
synthetic_organic_pigments_raw MaterialScience Regression Raw Raman spectral library comprising nearly 300 reference spectra of synthetic organic pigments (SOPs). Designed for spectral matching and identification of pigments in modern and contemporary art conservation.
wheat_lines Biological Classification Raman spectra from the 7th generation of salt-stress-tolerant wheat mutant lines and their commercial cultivars. Features 785 nm excitation and tracks biochemical shifts in carotenoids and protein-related bands for agricultural phenotyping.
yeast_fermentation Biological Regression This dataset contains Raman spectra acquired during the continuous ethanolic fermentation of sucrose using Saccharomyces cerevisiae (Baker's yeast). To facilitate continuous processing and high-quality optical measurements, the yeast cells were immobilized in calcium alginate beads.

📊 RamanDataset Class

Each loaded dataset returns a RamanDataset object with the following attributes and properties:

Attribute/Property Type Description
spectra np.ndarray Raman spectra intensity data (2D: samples × wavenumbers, or 2D/object array if variable)
raman_shifts np.ndarray Wavenumber/Raman shift values in cm⁻¹ (1D array, or 2D/object array if variable)
targets np.ndarray Target labels (classification) or values (regression)
metadata dict Dataset metadata including source, paper, and description
name str Name of the dataset
task_type TASK_TYPE Classification or Regression
application_type APPLICATION_TYPE Application domain (MaterialScience, Biological, Medical, Chemical)
n_spectra int Number of spectra in the dataset
n_frequencies int Number of frequency points per spectrum
n_raman_shifts int Number of Raman shift values
n_classes int | None Number of classes (classification only)
class_names list | None Unique class names (classification only)
target_range tuple | None (min, max) targets values (regression only)
min_shift float Minimum Raman shift value
max_shift float Maximum Raman shift value

Support for Datasets with Multiple Raman Shifts:

  • If all spectra share identical raman_shifts, raman_shifts is a 1D array and spectra is a 2D array (n_samples × n_points).
  • If all spectra have the same number of points but different raman_shift values, both raman_shifts and spectra are 2D arrays (n_samples × n_points).
  • If spectra have different numbers of points, both raman_shifts and spectra are returned as 1D object arrays, where each entry is a 1D array for that sample.
  • This allows the library to support real-world datasets with variable or non-uniform spectral grids.

Note:

  • Downstream code should check the shape and dtype of raman_shifts and spectra to handle all cases robustly.
  • For machine learning, it is recommended to interpolate or pad spectra to a common grid if uniformity is required.

The dataset can also be converted to a pandas DataFrame:

# df = dataset.to_dataframe()

🎯 Milestones

  • View Datasets
  • Software architecture with dummy data
  • Software tests
  • Integration of Kaggle
  • Integration of Huggingface
  • Integration of Github
  • Integration of Zenodo
  • Code documentation (docstrings)
  • Publish to PyPi
  • Integration of other datasets
  • API documentation website

🚢 Publishing a New Version to PyPI

Releases are automated via GitHub Actions. Pushing a version tag triggers the CI pipeline: tests run first, then the package is built and published to PyPI automatically.

# 1. Ensure all changes are committed and pushed to main
git checkout main && git pull

# 2. Create and push a version tag (uses setuptools-scm for the version)
git tag v1.2.3
git push origin v1.2.3

The tag format must match v*.*.* (e.g., v1.2.3). Once pushed, the CI workflow will:

  1. Run the test suite across Python 3.10–3.13
  2. Build the distribution packages
  3. Publish to PyPI using the PYPI_API_TOKEN secret configured in the repository settings

Note: The PYPI_API_TOKEN secret must be set in GitHub → Settings → Secrets and variables → Actions before the first publish.

🤝 Contributing

Contributions are welcome! To add a new dataset:

  1. Choose the appropriate loader based on the data source:

    • KaggleLoader for Kaggle datasets
    • HuggingFaceLoader for Hugging Face datasets
    • ZenodoLoader for Zenodo datasets
    • ZipLoader for other URL-based sources
    • MiscLoader for datasets that do not fit into the above categories (e.g., DeepeR)

  2. Implement a loader function that returns a tuple of (spectra, raman_shifts, targets):

    • spectra: 2D numpy array of intensity values (samples × wavenumbers)
    • raman_shifts: 1D numpy array of wavenumber values in cm⁻¹
    • targets: numpy array of target labels or values

  3. Add the dataset to the loader's DATASETS dictionary with appropriate metadata.

  4. Add tests for the new dataset.

🔮 For Later (Future Datasets)

Remaining / For Later (still not integrated)

The following items remain to be added (suggested action: add a DATASETS entry under the appropriate loader, or add a loader placeholder with the source link):

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