In the realm of data analysis, having well-structured and clear data is fundamental for meaningful insights. Data schemas provide a blueprint for understanding and organizing datasets. In today’s digital landscape, where automation and machine-readability are vital, machine-actionable schemas offer enhanced utility. This blog post will guide you through the process of creating a machine-actionable schema using, a user-friendly platform. We’ll explore how to develop such schemas step by step and the benefits of utilizing the Overlays Capture Architecture (OCA) for better data management.

The Practicality of Machine-Actionable Schemas

Think of a schema as a roadmap that navigates you through your data landscape. Machine-actionable schemas take this up a notch by making the schema easily understandable for computers. The advantages range from ensuring data accuracy to streamlining data integration and analysis.

Getting Started: Crafting Your Machine-Actionable Schema simplifies schema creation through a tutorial on crafting an Excel Template schema. Before you begin, have a clear picture of your dataset or the data you intend to collect. Decide on concise attribute names, avoiding spaces and complex characters to maintain clarity and consistency.

Overlays Capture Architecture (OCA) is the language that uses to express your schema. It allows flexibility to schema design, such as having descriptive labels for your schema attributes. These labels provide context and descriptive information, ensuring accessibility for diverse users. This feature enriches the schema’s comprehensibility. OCA also has features such as adding units to your schema attributes, descriptions to help users understand attributes and more.

A Gradual Approach to Schema Improvement

The flexibility of OCA enables you to start with a basic schema and add details as your project progresses. This adaptable approach accommodates evolving project needs without overwhelming you with upfront complexity.

Creating the Machine-Actionable Version‘s parser transforms your Excel Template schema into an official OCA Bundle. This bundle compiles all schema features into machine-readable JSON format, packaged as a .zip file.

A pathway for working with schemas. First write your schema template in Excel, then parse this template to your .zip machine-readable bundle at You can save this schema and the Excel template together with your data and share it with your data. You can also deposit the .zip and Excel template schemas in Borealis through the library process. You will then have a published schema with a DOI.

Future Developments and the OCA Standard aims to expand beyond Excel Templates and introduce additional functionalities. The OCA Bundle adheres to the OCA Open Standard hosted by the Human Colossus Foundation. This standardization ensures compatibility and interoperability within the data ecosystem.

Contributing to the OCA Standard

For those interested in shaping OCA’s future, participating in OCA Standard meetings offers an avenue for contribution. By sharing insights, you can contribute to refining this open standard. empowers researchers with practical tools to create machine-actionable schemas. By simplifying schema creation and incorporating OCA, facilitates efficient, accurate, and collaborative data-driven research. Delve into the world of machine-actionable schemas and optimize your data management with

Written by Carly Huitema

In the world of research and data analysis, understanding and structuring data effectively is crucial. The way data is documented and organized can greatly impact its usability and value. This is where data schemas come into play. A data schema is like a roadmap that guides researchers through the intricacies of their datasets, ensuring clarity, consistency, and accurate interpretation. In this blog post, we’ll explore the challenges posed by working with poorly described data, the benefits of well-documented data schemas, and how the University of Guelph’s Semantic Engine is revolutionizing the way researchers create and utilize these schemas.

The Challenge of Poor Data Descriptions

Imagine receiving a dataset with columns of numbers and labels that are cryptic at best. Without proper documentation, interpreting the data becomes a daunting task. Researchers may struggle to understand what each column represents, the units of measurement, and the data types involved. This lack of clarity not only hinders individual research efforts but also makes it challenging to collaborate, share, and replicate findings accurately.

The Need for Well-Documented Data Schemas

To make sense of data, researchers rely on data schemas – structured descriptions that outline the composition and meaning of the dataset. A robust schema provides insights into column labels, data types, units, and relationships between different data elements. By offering this comprehensive view, a well-documented schema ensures that researchers can quickly grasp the essence of the data, minimizing misinterpretations and errors.

Furthermore, data schemas play a pivotal role in data sharing. Researchers often collaborate across disciplines, and clear documentation ensures that the context of the data is communicated effectively. It’s especially valuable when researchers from different domains come together, as they might not be familiar with the conventions and nuances of each other’s fields.

Enter the Semantic Engine: Simplifying Schema Creation

Recognizing the importance of data schemas, the University of Guelph has developed the Semantic Engine – a powerful set of tools designed to help researchers generate machine-accessible meaning for their data. One of the standout features of this engine is its ability to simplify the creation and utilization of data schemas, making it easier for researchers to harness the full potential of their data.

Developed in collaboration with researchers, the Semantic Engine takes a user-friendly approach to schema creation. It provides an intuitive interface that allows researchers to craft data schemas with minimal effort, even if they lack specialized knowledge in schema design.

Machine-Actionable Schemas and Beyond

One of the key advantages of the Semantic Engine is its ability to generate machine-actionable schemas. This means that the schemas created using the engine can be easily understood and processed by computers. This machine-readability comes in handy when validating data – the schema enforces formatting rules, ensuring that the data aligns with the intended structure.

Moreover, having a well-structured schema becomes invaluable when researchers aim to combine datasets. Whether merging data from different sources or conducting meta-analyses, a clear schema facilitates seamless integration, leading to more robust and comprehensive insights.

The Overlays Capture Architecture (OCA) Advantage

At the heart of the Semantic Engine’s schema standard lies the Overlays Capture Architecture (OCA), developed by the Human Colossus Foundation. OCA is an open, international standard that organizes data schemas into layers or overlays. This layered approach provides a detailed and organized representation of the schema, making it easier to comprehend and use.

Each layer of the OCA schema corresponds to a specific feature of the data, and these layers can be added or modified independently. This modularity enhances flexibility and ensures that researchers can adapt their schemas as their research evolves.

Storing and Sharing Schemas

The Semantic Engine not only helps in creating effective data schemas but also facilitates their storage and sharing. Researchers can store the schemas alongside their data, ensuring that the context and structure are preserved. Researchers can also share their schemas when they share their data or deposit their schemas in repositories, assigning them citable identifiers like Digital Object Identifiers (DOIs). This makes the schemas citable and shareable, promoting transparency and reproducibility in research.

In the world of research, the importance of well-documented data cannot be overstated. Clear and structured data schemas empower researchers to unlock the true potential of their datasets, facilitating understanding, collaboration, and meaningful insights. The University of Guelph’s Semantic Engine, with its focus on user-friendly schema creation, machine-actionable designs, and utilization of the Overlays Capture Architecture, is a game-changer in the realm of data schemas. By simplifying schema creation and enhancing data clarity, the Semantic Engine is paving the way for more efficient, accurate, and impactful research across disciplines.


Written by Carly Huitema