Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
All the Data, One Place — Now What?
At the Ontario Dairy and Beef Research Centres, we’ve reached an exciting milestone: all of our core livestock data — from milk yield and feed intake to activity, body weight, veterinary treatments, health events, and breeding values — is now centralized in a single database. And thanks to our dairy and beef data portals, it’s…
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Cardinality
In our ongoing exploration of using the Semantic Engine to describe your data, there’s one concept we haven’t yet discussed—but it’s an important one: cardinality. Cardinality refers to the number of values that a data field (specifically an array) can contain. It’s a way of describing how many items you’re expecting to appear in a…
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To keep or let go? Historical data sources
I’ve been talking about data ownership in a few different ways and I have also been digging into these wonderful historical data sources. BUT! Where do we draw the line? Let’s be honest that’s another really tough question to answer. I have proclaimed many times, to many different people/audiences, and in different fora – that…
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Can You Use an LLM to Write an OCA Schema?
Short answer: Not really — but also, kind of. Why you can’t just use an LLM to write a schema At first glance, writing an OCA (Overlays Capture Architecture) schema might seem simple. After all, it’s just JSON, and tools like ChatGPT or Microsoft Copilot are great at generating structured text. But when it comes…
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Data Ownership – is it a thing?
Alrighty folks – this topic is getting hot and I love it! Time to move out of my lane and talk a bit about producer data – I’m referring to the wealth of data collected on dairy farms, sheep operations, swine, poultry, and the list goes on. A conversation I’ve sat in on a couple…
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Unit framing to standardize units
In research and data-intensive environments, precision and clarity are critical. Yet one of the most common sources of confusion—often overlooked—is how units of measure are written and interpreted. Take the unit micromolar, for example. Depending on the source, it might be written as uM, μM, umol/L, μmol/l, or umol-1. Each of these notations attempts to…
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Custom format rules
When designing a data schema, you’re not only choosing what data to collect but also how that data should be structured. Format rules help ensure consistency by defining the expected structure for specific types of data and are especially useful for data verification. For example, a date might follow the YYYY-MM-DD format, an email address…
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Architecture of OCA Schema Language
Data schemas Schemas are a type of metadata that provide context to your data, making it more FAIR (Findable, Accessible, Interoperable, and Reusable). At their core, schemas describe your data, giving data better context. There are several ways to create a schema, ranging from simple to more complex. The simplest approach is to document what…
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From Siloed Systems to Shared Success: Debunking the Myths of Research Autonomy in a Shared System
Part of the blog series on Collaborative Research IT Infrastructure In our last post, we explored why a shared infrastructure makes sense—highlighting how collaborative systems reduce costs, improve security, simplify compliance, and offer scalable, cutting-edge tools for research. But even with all these benefits, one hesitation often remains: the fear of losing control. Many researchers…
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Format rules for data
When you’re building a data schema you’re making decisions not only about what data to collect, but also how it should be structured. One of the most useful tools you have is format restrictions. What Are Format Entries? A format entry in a schema defines a specific pattern or structure that a piece of data…
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