raman-data 1.2.7

A unified Python package for accessing various Raman spectroscopy datasets.

raman-data

A unified Python library for accessing public Raman spectroscopy datasets. raman-data is the dataset layer of RamanBench, a large-scale benchmark for machine learning on Raman spectroscopy data. It provides a single API to discover, download, and load 90 datasets — covering classification, regression, denoising, and super-resolution tasks — from diverse sources (Kaggle, HuggingFace, Zenodo, Figshare, GitHub) in a standardized, ML-ready format. Of these, 75 meet the inclusion criteria of RamanBench and are used for benchmarking.

Installation

pip install raman-data

Kaggle datasets require API credentials. Follow the Kaggle API setup guide and place your kaggle.json in ~/.kaggle/.

Quick Start

from raman_data import raman_data, TASK_TYPE, APPLICATION_TYPE

# List all available datasets
raman_data()

# Filter by task type or application domain
raman_data(task_type=TASK_TYPE.Classification)
raman_data(application_type=APPLICATION_TYPE.Medical)
raman_data(task_type=TASK_TYPE.Regression, application_type=APPLICATION_TYPE.Biological)

# Load a dataset by ID
dataset = raman_data("bioprocess_substrates")

# Access data
X = dataset.spectra          # np.ndarray (n_samples × n_wavenumbers)
w = dataset.raman_shifts     # np.ndarray of wavenumber values in cm⁻¹
y = dataset.targets          # np.ndarray of labels or values
print(dataset.metadata)

# Convert to pandas DataFrame
df = dataset.to_dataframe()

See examples/demo.ipynb for a full walkthrough.

RamanDataset API

Every raman_data(dataset_id) call returns a RamanDataset object:

Attribute	Type	Description
spectra	np.ndarray	Intensity matrix (samples × wavenumbers)
raman_shifts	np.ndarray	Wavenumber axis in cm⁻¹
targets	np.ndarray	Labels (classification) or values (regression)
target_names	list	Target column names (regression) or class names (classification)
metadata	dict	Source, paper reference, description
name	str	Dataset identifier
task_type	TASK_TYPE	Classification, Regression, Denoising, or SuperResolution
application_type	APPLICATION_TYPE	Medical, Biological, Chemical, or MaterialScience
n_spectra	int	Number of spectra
n_frequencies	int	Number of wavenumber points
n_classes	int | None	Number of classes (classification only)
target_range	tuple | None	(min, max) of target values (regression only)
min_shift / max_shift	float	Spectral range in cm⁻¹

Variable-length spectra: If spectra share the same wavenumber axis, spectra and raman_shifts are standard 2D/1D arrays. If the axis differs per sample, both are returned as object arrays of 1D arrays. For machine learning, interpolate or pad to a common grid as needed.

Available Datasets

89 datasets across Material Science, Biological & Biotechnological, Medical & Clinical, and Chemical & Industrial domains (click to expand)

Dataset Name	Application	Task Type	Description
acetic_acid_species	Chemical & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Clinical	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 & Industrial	Regression	Time-resolved LC-Raman spectra for Glycine elution using the vertical flow method (Lo, Hiramatsu lab, NCTU Taiwan). Glycine injected at 100 mM into an HPLC system (hydrophobic resin column, H₂O→ACE gradient, 7 mL/min, 50 µL injection). Time-resolved acquisition at 0.2 s/frame. Preprocessing: solvent
amino_acids_leucine	Chemical & Industrial	Regression	Time-resolved LC-Raman spectra for Leucine elution using the vertical flow method (Lo, Hiramatsu lab, NCTU Taiwan). Leucine injected at 100 mM into an HPLC system (hydrophobic resin column, H₂O→ACE gradient, 7 mL/min, 50 µL injection). Time-resolved acquisition at 0.2 s/frame. Preprocessing: solvent
amino_acids_phenylalanine	Chemical & Industrial	Regression	Time-resolved LC-Raman spectra for Phenylalanine elution using the vertical flow method (Lo, Hiramatsu lab, NCTU Taiwan). Phenylalanine injected at 55 mM into an HPLC system (hydrophobic resin column, H₂O→ACE gradient, 7 mL/min, 50 µL injection). Time-resolved acquisition at 0.2 s/frame. Preprocessi
amino_acids_tryptophan	Chemical & Industrial	Regression	Time-resolved LC-Raman spectra for Tryptophan elution using the vertical flow method (Lo, Hiramatsu lab, NCTU Taiwan). Tryptophan injected at 55 mM into an HPLC system (hydrophobic resin column, H₂O→ACE gradient, 7 mL/min, 50 µL injection). Time-resolved acquisition at 0.2 s/frame. Preprocessing: so
bacteria_identification	Medical & Clinical	Classification	60,000 SERS spectra (2,000 per isolate across three measurement time-points) from 30 clinically relevant bacterial and yeast isolates, including an MRSA/MSSA isogenic pair. Acquired on a Horiba LabRAM HR Evolution spectrometer (633 nm, 13.17 mW, 300 l/mm grating, 1.2 cm⁻¹ dispersion, Olympus MPLAN 1
biomolecules_reference	Biological & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Biotechnological	Classification	SERS spectra of conditioned cell culture media metabolites entrapped on gold multibranched nanoparticles (AuMs, ~50 nm) functionalized with (COOH)2. 12 sample categories from Table 1 (Erzina et al. 2020): A2058/G361 melanoma cells, HPM melanocytes, HF skin fibroblasts, ZAM tumour-associated fibrobla
cancer_cell_cooh	Biological & Biotechnological	Classification	SERS spectra of conditioned cell culture media metabolites entrapped on gold multibranched nanoparticles (AuMs, ~50 nm) functionalized with COOH. 12 sample categories from Table 1 (Erzina et al. 2020): A2058/G361 melanoma cells, HPM melanocytes, HF skin fibroblasts, ZAM tumour-associated fibroblasts
cancer_cell_nh2	Biological & Biotechnological	Classification	SERS spectra of conditioned cell culture media metabolites entrapped on gold multibranched nanoparticles (AuMs, ~50 nm) functionalized with NH2. 12 sample categories from Table 1 (Erzina et al. 2020): A2058/G361 melanoma cells, HPM melanocytes, HF skin fibroblasts, ZAM tumour-associated fibroblasts,
chembl_molecules	Chemical & Industrial	Regression	140k DFT-computed Raman spectra for ChEMBL drug-like molecules. Targets: HOMO-LUMO gap, HOMO/LUMO energies, isotropic polarizability, heat capacity, dipole moment.
chlorinated_samples	Chemical & Industrial	Classification	Binary Raman classification task: detect the presence of chloroform in a sample. 230 spectra across 2473 wavenumbers (350–3500 cm⁻¹). Class balance {0: 76, 1: 154}. Data provided by Analyze IQ Limited; predefined 3-fold splits ship with the source repository.
citric_acid_species	Chemical & Industrial	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.
comfile_stroke	Medical & Clinical	Classification	SERS serum spectra for binary stroke vs. healthy-control classification. 20 tab-separated files (10 stroke, 10 healthy control), each containing ~201 spectra across 723 wavenumber points (202.985–1999.92 cm⁻¹). ~4,020 spectra total.
covid19_salvia	Medical & Clinical	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 & Clinical	Classification	Raman spectra of blood serum from 20 subjects (10 RT-PCR/ELISA-confirmed COVID-19 patients and 10 healthy controls) collected under Ethics Committee protocol 26691419.6.0000.5492 (Universidade Anhembi Morumbi). Acquired with a Dimension P1 dispersive Raman spectrometer (Lambda Solutions), 830 nm exc
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 & Clinical	Classification	In vivo portable Raman spectra of human skin at the AGEs site for DM2 screening. 11 type 2 diabetes patients (7F, 49.5±6.7 y) and 9 healthy controls (7F, 33.2±4.9 y), University of Guanajuato, Mexico, IRB approved. PEK-785 spectrometer (Agiltron), 785 nm, 90 mW, 200 µm spot, 12 cm⁻¹ resolution, 5 sc
diabetes_skin_ear_lobe	Medical & Clinical	Classification	In vivo portable Raman spectra of human skin at the Ear Lobe site for DM2 screening. 11 type 2 diabetes patients (7F, 49.5±6.7 y) and 9 healthy controls (7F, 33.2±4.9 y), University of Guanajuato, Mexico, IRB approved. PEK-785 spectrometer (Agiltron), 785 nm, 90 mW, 200 µm spot, 12 cm⁻¹ resolution,
diabetes_skin_inner_arm	Medical & Clinical	Classification	In vivo portable Raman spectra of human skin at the Inner Arm site for DM2 screening. 11 type 2 diabetes patients (7F, 49.5±6.7 y) and 9 healthy controls (7F, 33.2±4.9 y), University of Guanajuato, Mexico, IRB approved. PEK-785 spectrometer (Agiltron), 785 nm, 90 mW, 200 µm spot, 12 cm⁻¹ resolution,
diabetes_skin_thumbnail	Medical & Clinical	Classification	In vivo portable Raman spectra of human skin at the Thumbnail site for DM2 screening. 11 type 2 diabetes patients (7F, 49.5±6.7 y) and 9 healthy controls (7F, 33.2±4.9 y), University of Guanajuato, Mexico, IRB approved. PEK-785 spectrometer (Agiltron), 785 nm, 90 mW, 200 µm spot, 12 cm⁻¹ resolution,
diabetes_skin_vein	Medical & Clinical	Classification	In vivo portable Raman spectra of human skin at the Vein site for DM2 screening. 11 type 2 diabetes patients (7F, 49.5±6.7 y) and 9 healthy controls (7F, 33.2±4.9 y), University of Guanajuato, Mexico, IRB approved. PEK-785 spectrometer (Agiltron), 785 nm, 90 mW, 200 µm spot, 12 cm⁻¹ resolution, 5 sc
ecoli_fermentation	Biological & Biotechnological	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 & Biotechnological	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 & Biotechnological	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Clinical	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).
ht_raman_bio_catalysis_axp	Biological & Biotechnological	Regression	This dataset consists of Raman spectra tailored for the real-time monitoring of biocatalytic reactions. A key feature of this data is the use of Deep Eutectic Solvents (DES) as the reaction medium.
illicit_adulterants_ft_raman	Medical & Clinical	Classification	FT-Raman reference spectra (1064 nm, 50–3600 cm⁻¹, 4 cm⁻¹ resolution, 32 scans) of 11 SERS-active pharmaceutically active adulterants found in dietary supplements, acquired with a benchtop Bruker RAM II FT-IR Raman module (OPUS v7.2, 300 mW). Compounds selected from RASFF portal EU alerts. Library s
illicit_adulterants_sers	Medical & Clinical	Classification	Portable SERS spectra (785 nm, 400–2300 cm⁻¹, 8 cm⁻¹ FWHM, 3 scans) of 11 SERS-active illicit adulterants in dietary supplements. Acquired with a Metrohm Instant SERS analyzer (MISA) using silver printed-SERS (p-SERS) substrates and Orbital Raster Scan (ORS™) technology (30 µm spot, ~2 mm raster, 10
itaconic_acid_species	Chemical & Industrial	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.
kaiser_ecoli_fermentation	Biological & Biotechnological	Regression	Raman spectra collected during E. coli fermentation using a Kaiser spectrometer.
kaiser_ecoli_fermentation_supernatant	Biological & Biotechnological	Regression	Raman spectra collected during E. coli fermentation, measured on the supernatant using a Kaiser spectrometer.
levulinic_acid_species	Chemical & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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 & Industrial	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	Material Science	Classification	Raman spectra of field-collected weathered microplastic debris (155 samples) and unweathered standard plastics (18 samples), acquired from sediments near waste plastics recycling industries in Laizhou City, Shandong Province, China (August 2018). WITec alpha300-R confocal Raman (532 nm, 5 mW typical
mlrod	Material Science	Classification	500,000+ point-mapping Raman spectra of Mars-analogue geological samples acquired on a Horiba LabRAM HR Evolution (532 nm, 100 mW, 1800 l/mm grating, ~1 cm⁻¹ resolution, open-electrode CCD at −60 °C, 50× LWD NA 0.5 objective, 0.1 µm XY step, 100–1800 cm⁻¹). Samples include gabbro (Madagascar), grani
organic_compounds_preprocess	Chemical & Industrial	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 & Industrial	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 & Clinical	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 & Clinical	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 & Biotechnological	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	Material Science	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	Material Science	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).
serum_alzheimer_disease	Medical & Clinical	Classification	SERS serum metabolite spectra for classifying Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls. 139 serum samples (57 male, 82 female) collected at Rui Jin Hospital, Shanghai Jiao Tong University. Organized as SERSomes (200 spectra per sample, 638 nm laser, quartz capi
serum_prostate_cancer	Medical & Clinical	Classification	SERS serum metabolite spectra for classifying prostate cancer (PCa), benign prostatic hyperplasia (BPH), and healthy controls. 424 serum samples from male participants (ages 41–89) collected at Ren Ji Hospital, Shanghai Jiao Tong University. Organized as SERSomes (200 spectra per sample, 638 nm lase
streptococcus_thermophilus_fermentation_kaiser	Biological & Biotechnological	Regression	This dataset contains offline Raman spectra collected during batch cultivations of Streptococcus thermophilus. The spectra were measured using a Kaiser RXN1. The dataset includes two distinct fermentation runs conducted in shake flasks over a 24-hour period.
streptococcus_thermophilus_fermentation_timegate	Biological & Biotechnological	Regression	This dataset contains offline Raman spectra collected during batch cultivations of Streptococcus thermophilus. Unlike conventional continuous-wave Raman, these measurements were captured using Time-Gated Raman Spectroscopy. The dataset includes two distinct fermentation runs conducted in shake flask
succinic_acid_species	Chemical & Industrial	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 & Industrial	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 & Industrial	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	Material Science	Regression	Baseline Corrected Raman spectral library of approximately 270 synthetic organic pigments (~300 reference spectra, nearly 400 samples total) compiled at the Royal Institute for Cultural Heritage (KIK/IRPA), Brussels. Acquired on a Renishaw inVia dispersive Raman spectrometer (785 nm, Peltier-cooled
synthetic_organic_pigments_raw	Material Science	Regression	Raw Raman spectral library of approximately 270 synthetic organic pigments (~300 reference spectra, nearly 400 samples total) compiled at the Royal Institute for Cultural Heritage (KIK/IRPA), Brussels. Acquired on a Renishaw inVia dispersive Raman spectrometer (785 nm, Peltier-cooled CCD 203 K, 1200
tg_ecoli_fermentation	Biological & Biotechnological	Regression	Raman spectra collected during E. coli fermentation using Time-Gated Raman Spectroscopy.
tg_ecoli_fermentation_supernatant	Biological & Biotechnological	Regression	Raman spectra collected during E. coli fermentation, measured on the supernatant using Time-Gated Raman Spectroscopy.
wheat_lines	Biological & Biotechnological	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 & Biotechnological	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.

Contributing a Dataset

Just want to suggest a dataset without implementing it? Add it to the community proposals list: PROPOSED_DATASETS.md (in the RamanBench repo). It's the running queue we draw from for the next benchmark release. The full implementation steps below are only needed if you want to add a loader yourself.

We welcome contributions of new Raman datasets, especially from underrepresented domains, novel instrumentation, or larger sample sizes. A dataset is eligible for inclusion if it:

• Contains real, experimentally acquired 1D Raman spectra (not synthetic or simulated)
• Is publicly available without access restrictions or upon-request-only policies
• Provides ground-truth labels (class labels for classification; continuous values for regression)
• Is accompanied by a citable reference (paper, report, or dataset DOI)

Step-by-step

1. Host your data publicly. Upload your dataset to HuggingFace Datasets, Zenodo, Figshare, or Kaggle. HuggingFace is preferred for its versioning and direct streaming support — see dataset_to_huggingface.md for a step-by-step guide.

2. Choose the right loader. Add your dataset entry to the corresponding loader file in raman_data/loaders/:

Source	Loader file
HuggingFace	HuggingFaceLoader.py
Zenodo	ZenodoLoader.py
Figshare	FigshareLoader.py
Kaggle	KaggleLoader.py
Other URL (ZIP/file)	MiscLoader.py

3. Add a dataset entry. Each loader contains a DATASETS dict mapping a unique string ID to a DatasetInfo object. Add an entry following the existing pattern:

from raman_data.types import DatasetInfo, TASK_TYPE, APPLICATION_TYPE

"your_dataset_id": DatasetInfo(
    id="your_dataset_id",               # unique snake_case identifier
    name="Your Dataset Name",           # human-readable name
    short_name="Short Name",            # for tables and figures (≤ 30 chars)
    task_type=TASK_TYPE.Regression,     # or TASK_TYPE.Classification
    application_type=APPLICATION_TYPE.Biological,
    license="CC BY 4.0",
    loader=lambda df: _load_your_dataset(df),
    metadata={
        "source": "https://doi.org/...",    # DOI or URL of the dataset
        "paper": "https://doi.org/...",     # DOI of the associated paper
        "description": "One-sentence description of the dataset and task.",
    },
)

4. Implement the loader function. The loader receives the raw data (e.g. a HuggingFace dataset or a file path) and must return a RamanDataset:

def _load_your_dataset(raw_data) -> RamanDataset:
    spectra      = ...  # np.ndarray, shape (n_samples, n_wavenumbers)
    raman_shifts = ...  # np.ndarray, shape (n_wavenumbers,), values in cm⁻¹
    targets      = ...  # np.ndarray, shape (n_samples,) or (n_samples, n_targets)
    return RamanDataset(spectra=spectra, raman_shifts=raman_shifts, targets=targets)

5. Add tests and open a pull request. Add a test in tests/ that loads your dataset and checks basic properties (shape, dtype, value ranges). Then open a pull request — we will review it and, if it meets the inclusion criteria, merge it and include it in the next RamanBench release.

Releasing a New Version

Releases are automated via GitHub Actions. A v*.*.* git tag triggers tests, build, and PyPI publish.

See RELEASING.md for the full step-by-step checklist (README regeneration, smoke tests, and common gotchas).

Short version:

git checkout main && git pull
PYTHONPATH=. python3 scripts/generate_readme_datasets.py  # refresh table
git add -A && git commit -m "Release vX.Y.Z"
git push origin main
git tag vX.Y.Z && git push origin vX.Y.Z

The CI workflow runs at github.com/ml-lab-htw/raman_data/actions and publishes to PyPI.

Citation

If you use raman-data in your research, please cite the RamanBench paper:

@misc{koddenbrock2026ramanbench,
  title         = {{RamanBench}: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy},
  author        = {Koddenbrock, Mario and Lange, Christoph and Legner, Robin and Jaeger, Martin
                   and K{\"o}gler, Martin and Cruz Bournazou, Mariano N. and Neubauer, Peter
                   and Bie{\ss}mann, Felix and Rodner, Erik},
  year          = {2026},
  eprint        = {2605.02003},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2605.02003}
}

License

This project is licensed under the MIT License — see LICENSE for details. The individual datasets retain their original licenses as specified in each dataset's metadata.