CoreMeta4Cat — Overview

CoreMeta4Cat defines the minimum information that should be reported alongside catalysis research data, across four domains: Synthesis, Characterization, Reaction, and Simulation. This page gives you a structured overview of what the standard covers, how it is organized, and how the different parts connect.

What is CoreMeta4Cat?

CoreMeta4Cat is a metadata standard for catalysis research data, developed within the NFDI4Cat initiative. It is implemented as a LinkML schema — a format that allows the standard to automatically generate multiple useful outputs from a single source: an Excel reference workbook, a JSON Schema for validation, Python data classes, and a full RDF/OWL representation for semantic querying.

CoreMeta4Cat is built as a domain-specific application profile on top of DCAT-AP-PLUS, a provenance-aware extension of the DCAT Application Profile 3.0. This means every CoreMeta4Cat dataset is a valid dcat:Dataset, every activity is a valid prov:Activity, and all schema artefacts are generated automatically from the single LinkML source.

In practical terms: a dataset described with CoreMeta4Cat is not just a well-labelled spreadsheet. It is a structured, machine-readable record that can be validated, searched, and connected to other datasets across repositories — because every field links back to a shared scientific vocabulary.

Two-layer architecture

CoreMeta4Cat organises metadata in two layers.

Layer 1 — Global classification applies to every catalysis dataset, regardless of data class. It captures the two fields needed for the coarsest-possible filtering across a repository:

Field	Example values	Obligation
Catalysis research field	heterogeneous catalysis, electrocatalysis, biocatalysis	Recommended
Reaction type	CO oxidation, ammonia synthesis, hydrogenation	Recommended

Layer 2 — Data-class-specific metadata is structured around the four data classes: Synthesis, Characterization, Reaction, and Simulation. Each data class carries its own set of Mandatory, Recommended, and Optional fields.

CatalysisDataset
 ├── catalysis research field          [Layer 1 — applies to all]
 ├── reaction type                     [Layer 1 — applies to all]
 ├── was_generated_by → Synthesis      [Layer 2 — data-class specific]
 ├── was_generated_by → Characterization
 ├── was_generated_by → Simulation
 └── is_about_activity → Reaction

The four data classes

Synthesis

Reproducibility of catalyst synthesis is one of the most persistent challenges in catalysis research. The Synthesis data class defines the minimum metadata for twelve preparation methods — from common routes such as Impregnation and Co-Precipitation to more specialised techniques like Atomic Layer Deposition, Flame Spray Pyrolysis, and Exsolution Synthesis.

Method-specific parameter sets are organized into concrete preparation method types. Cross-cutting steps shared across methods (drying, calcination, precipitation) are defined once and reused, so the same parameter is never described differently depending on which method it appears in.

Preparation method	Key shared steps
Impregnation	Drying, Calcination
Co-Precipitation	Precipitation, Drying, Calcination
Deposition-Precipitation	Precipitation, Drying, Calcination
Solvothermal, Plasma-Assisted, Combustion, Microwave-Assisted, Mechanochemical, Sublimation	Thermal process
Sol-Gel, Flame Spray Pyrolysis, Atomic Layer Deposition, Exsolution	Method-specific only

Characterization

The Characterization data class covers twenty-eight analytical techniques currently used in catalysis. Each technique is modelled with slots for instrument parameters, sample state, and measurement conditions. Cross-cutting parameter groups are shared across related techniques:

X-ray source parameters — shared by Powder XRD, Single Crystal XRD, XPS, EDX
Electron microscopy parameters — shared by TEM, SEM
Temperature program parameters — shared by TPR, TPO, Thermogravimetry
Chromatography and mass range parameters — shared by GC, GC-MS, HPLC, HPLC-MS

Reaction

The Reaction data class represents the catalytic process being studied. An important design detail: for operando experiments — for example, in-situ XRD carried out while a reaction is running — the dataset carries both a Characterization record (the process that generated the data) and a Reaction record (the process the data is about). CoreMeta4Cat models both links explicitly.

Eight reactor design types are currently defined:

Electrochemical Reactor
CSTR (Continuous Stirred Tank Reactor)
Plug Flow Reactor
Autoclave
Slurry Reactor
Microreactor
Fixed Bed Reactor
Fluidized Bed Reactor

Simulation

The Simulation data class covers four major computational method classes: DFT, Molecular Dynamics, Microkinetics, and Monte Carlo. The simulation software used is recorded alongside the method. Twelve calculated property types — such as electronic structure, band gap, phonon dispersion, and thermodynamic stability — capture the computed output.

What does a CoreMeta4Cat record look like?

Here is a minimal example showing how a reaction dataset is described. Every class and property links to a controlled vocabulary term and can be validated and converted to RDF using standard tooling:

id: ex:dataset-001
title: "CO oxidation activity of 1wt% Pt/Al2O3 at 200–400°C"
catalysis_research_field: heterogeneous catalysis

was_generated_by:
  - type: Reaction
    catalyst_quantity: 100.0    # mg
    reactant:
      - "1 vol% CO in N2"
      - "2 vol% O2 in N2"
    reactor_temperature_range: "200–400 °C"
    experiment_pressure: 1.0    # bar
    carried_out_by:
      type: FixedBedReactor

is_about_entity:
  - type: CatalystSample
    nominal_composition: "1wt% Pt/Al2O3"

Page	What it covers
Design Patterns	How the four data classes map to DCAT-AP-PLUS, the mixin pattern, ontology alignment
How to Extend	Rules for adding new preparation methods, techniques, reactor types, and properties
Schema Reference	Auto-generated reference for all classes and slots
Intended Users	Projects and repositories that adopt CoreMeta4Cat