Industry & research
Materials science and industrial research data
Organize research data around material systems, formulas, properties or literature lists. Work can include crystal-structure CIFs, material properties, phase diagrams and calculations, checking identifiers, units, conditions and linked publications, with samples and structured delivery.
Request a sample ↗What to confirm before requesting a quote
- Coverage
- Regions, languages, date range and available record count
- Fields and attachments
- Field definitions, example records and whether original documents or media are required
- Quality and updates
- Deduplication and missing-value rules, sample-based acceptance criteria and update frequency
- Permitted uses
- Internal research, model training, customer-facing products or redistribution
These are project scoping questions, not a statement that every data type includes all coverage or permissions.
01 / WHAT’S INCLUDED
What’s in the dataset
- Material names, formulas and source identifiers
- Crystal-structure CIFs, properties, conditions and units
- Phase diagrams and calculation data
- Linked publications, field definitions and QA notes
02 / USE CASES
Where it can help
- Materials R&D and specialist libraries
- Cross-source field preparation and linking
- Materials-informatics data preparation
03 / DELIVERY
Choose the data you need
Receive agreed CIF, JSON/JSONL, CSV and supporting source documents with fields, units and relationship notes.
Review representative materials and their supporting files before agreeing volume, sourcing and acceptance criteria.
See a sample format ↗Assess the fit for your project
Check data types, units, conditions and supporting literature on representative materials first.
Can CIFs and structured tables be delivered together?
Structure files, property tables, phase diagrams and literature links can be organized to the source and project requirements, preserving material IDs and file mappings.
How are experimental and calculated results separated?
Retain source-defined result types, conditions, units and methods, normalizing as agreed and identifying fields that need review.
Sources and usage
Confirm fields, supporting files, scientific completeness and use against sources and samples. Experimental and calculated results are identified separately.
If you plan to train a model, use the data in a customer-facing product or share it with third parties, mention this when requesting a quote so we can check source permissions and restrictions.
Public access does not establish permission for collection, training or redistribution. Naming a platform does not imply affiliation, agency or endorsement.
Read about data sources and rights ↗Want to review a sample first?
Tell us your project objectives and we’ll help you assess and find a relevant sample.