Citrine | DataManager

Unlike legacy data management solutions, Citrine Data Manager (CDM) was specifically designed for product experts wanting to use AI on complex products where materials, chemicals or ingredients are a key part of their competitive advantage.

Product experts need

• An easy way to digitize data and check its quality
• A data structure that grows as they learn more about their project, or they want to join datasets along a value chain
• A common vocabulary, so that teams can speak different languages and use different units, but still work with each other’s data
• The ability to systematically specify all steps in a material’s processing history, including processing conditions and batch measurements.
• Intuitive visualizations to communicate their work, learn from others, and assess data quality
• A secure way to store and share data
• A place where experimental parameters, formulations and end product measured properties come together to power AI

CDM streamlines importing data from common instrument files, e.g. HCLP. Citrine Professional Services can help you get almost any data AI-ready. Our team of experts uses a combination of manual and programmatic techniques to clean and structure your data.

Increase and improve data automatically
CDM’s descriptor libraries convert information like chemical formulas and SMILES to relevant features automatically enhancing the dataset that can be used in AI.

Check data quality through visualizations
Researchers can inspect the data using visualizations to spot any outliers so they can clean and correct data if needed.

Data model that doesn’t turn data away
Our open-source materials data model (Graphical Expression of Materials Data, or GEMD) is at the core of CDM. It enables customers to add and remove properties, relationships and related data sets with ease. This means that unlike, standard relational database it never turns away data.

The platform ensures that data is comparable across a team. While each team member can use the name and units they are used to, a template can join comparable data together and convert units so that all the data can be easily analyzed and visualized and used to power AI. This is particularly useful for companies with business units in different countries.

Data model that fully captures the context
Each step from procurement to final product is visualized in the material history and made available to AI models. Product experts can learn from these diagrams and can click to uncover details.

In this example, each stage is recorded, including, both the specified processing parameters (e.g., 200°C) and the actual measured parameter in that run (e.g., 199°C). The color-coded graphical user interface is easy to use, and users can also use the Python client to add, review, and revise data. This rich data model enables AI access to all aspects of a material’s history that will affect its final properties.

Visualization and reporting
Finally, data can tell a story. Visualization tools and custom reports, enable teams to communicate their progress to managers and customers.

See when data is uploaded and where it is used