Datasets

Every dataset below is openly available, has a citable source, and has enough documentation that you can tell what the columns mean. They are grouped by flavor rather than by method, because the useful question is “what kind of data is this?” before it is “what model should I fit?”

Scale and license are listed wherever the source states them. Check both before you plan around a dataset — a benchmark licensed for evaluation only, or a 1.7 GB multi-format download, changes what the work looks like.

Note

None of these are used in the chapters of this book, and none are derived from it. That is deliberate: working on data you have not already seen analyzed is most of the point.

The scale and license details below are summarized from each source and have not all been independently verified — see how this section was compiled before relying on them.

Process data and time series

These are the datasets that look most like industrial practice: many correlated sensors, autocorrelation in time, batch or run structure, missing values, and faults. They are also the ones where a careless random train/test split will quietly invalidate your results.

Dataset	Good for	Scale and notes	Source
Combined Cycle Power Plant	Multivariate regression; a clean first target with real measurements	9,568 rows; 4 inputs (ambient temperature, ambient pressure, relative humidity, exhaust vacuum), 1 output (net hourly electrical energy output)	UCI 294 · 10.1016/j.ijepes.2014.02.027
Tennessee Eastman process simulation	Fault detection and classification, PCA, anomaly detection, time-aware validation	500 runs, 52 variables, 20 fault types. Simulated, so it is clean by the standards of real plant data; split by run and by time	10.7910/DVN/6C3JR1
PRONTO multiphase flow benchmark	Process monitoring and fault detection with heterogeneous data types	About 1.7 GB across several data types — meaningfully more setup than a CSV	Zenodo 1341583
Fluid catalytic cracking operating data	Data-driven modeling and control of a refinery unit	Released with the paper; see its data availability statement	10.1016/j.compchemeng.2022.107900
IndPenSim penicillin fed-batch	Bioprocess modeling, batch-wise validation, control and optimization studies	Industrial-scale simulated fed-batch fermentation, released as multiple batches	Mendeley Data · simulator paper 10.1016/j.jbiotec.2014.10.029
SECOM semiconductor manufacturing	Imbalanced classification, missing data, aggressive feature selection	1,567 rows × 591 sensor features. Many missing values and very few failure cases — the class imbalance is the problem. CC BY 4.0	UCI 179
NASA C-MAPSS turbofan degradation	Remaining useful life, predictive maintenance, distribution shift between train and test	708 training and 707 test engine trajectories across four sub-datasets (FD001–FD004) with different fault modes and operating conditions. The catalog does not state an explicit license	data.nasa.gov · Saxena et al. 2008

Materials and molecules

Structure-to-property problems, where the interesting question is usually how you represent the input rather than which regressor you use. These connect most directly to High-dimensional Data and Neural Network Architectures.

Dataset	Good for	Scale and notes	Source
QM9	Structure-to-property mapping; comparing molecular representations	About 134,000 small organic molecules with computed properties — atomization energy, internal energy, enthalpy, band gap, dipole moment	10.1038/sdata.2014.22 · quantum-machine.org
Matbench	Materials regression and classification, and fair model comparison	13 tasks spanning roughly 300 to 132,000 samples. Check the terms of each underlying source dataset	Matbench
Open Reaction Database	Reaction outcome prediction, experiment planning, working with a real schema	More than 2 million reactions. Data CC BY-SA 4.0, software Apache 2.0. Preparing it is more work than reading a CSV	open-reaction-database.org
Polymerization property data	Predictive models for polymer properties and polymerization processes	See the paper’s data availability statement	10.1039/D4PY00995A
Li-ion battery cycle life	Predicting long-run behavior from early-cycle features; high-dimensional regression at small sample size	Commercial cells cycled to failure, with early-cycle features used to predict eventual cycle life. Few samples, many candidate features	10.1038/s41560-019-0356-8 · related battery-parameter-spaces

Thermophysical properties

Dataset	Good for	Scale and notes	Source
NIST ThermoML Archive	Property prediction, and practice with structured XML, units, uncertainty, and source tracking	A large XML archive. Availability and reuse terms depend on the original publisher	ThermoML
UCI Energy Efficiency	Regression, model comparison, residual diagnostics	768 rows, 8 inputs, 2 targets. Small and simple by design — a good warm-up, not a process dataset. CC BY 4.0	UCI 242
Henry’s law constants	Regression against temperature across a very wide range of solutes	A large curated compilation drawn from the literature	10.5194/acp-23-10901-2023

Inverse problems and generative modeling

Dataset	Good for	Scale and notes	Source
Light-color inverse problem	Demonstrating and evaluating generative models on an inverse problem with a known forward map	Measured light intensity as a function of red, green, and blue input settings. Small, physically interpretable, and the forward direction is easy — which is what makes the inverse direction a fair test	10.1039/D5DD00137D

Benchmarks for evaluating models

Dataset	Good for	Scale and notes	Source
ChemBench	Testing what a language model actually knows about chemistry; comparing tool use against no tool use	More than 2,700 questions. Intended for evaluation, not for training	ChemBench

Finding your own data

The catalog above is a starting point, not a boundary. Two habits make independent searching far more productive.

Read the data availability statement first. Most journals now require one, and it is the fastest way to tell whether a paper’s data is genuinely obtainable. It will point you at a repository record, a supporting information file, or — often enough — a sentence saying the data are available on request, which in practice means budget for a delay. Check this before you get attached to the idea.

Prefer repositories that give you a stable identifier. A DOI or a numbered record means the data you download today is the data someone else downloads next year.

General-purpose and chemistry-specific places to look:

Source	What you will find
Awesome Industrial Datasets (browsable version)	The best single index for this domain: around 190 industrial datasets, each tagged with domain, modality, task, access route, size, year, and license. Roughly twenty are chemical and process datasets, and several of the entries above are catalogued there with more metadata than their original hosts provide
UCI Machine Learning Repository	Several hundred curated, well-documented datasets with stated licenses; several of the entries above live here
Zenodo	General-purpose research data with DOIs, including a great deal of supplementary data from papers
Harvard Dataverse	Institutional repository with DOIs; hosts the Tennessee Eastman record above
Materials Project	Computed properties for a very large number of inorganic materials, with a documented API
NIST Standard Reference Data and WebBook	Authoritative thermophysical and spectroscopic data, with uncertainties
PubChem	Chemical structures, properties, and bioassay data, with a REST API

Several of these are exercised directly in Online Data Access and Accessing Data with AI Tools, so the mechanics of pulling from an API and parsing the response are already covered.