Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
Navigating the Research Landscape: The Crucial Role of Version Control in Data Analysis
In the dynamic realm of research, where data is the cornerstone of ground-breaking discoveries, ensuring the integrity, reproducibility, and collaboration of your work is paramount. One indispensable tool that researchers often overlook but should embrace wholeheartedly is version control. In this blog post, we’ll delve into the importance of version control for research data, explore…
ViewDocumenting your work: Variable Names – Research Data Management (RDM)
The next stop on our RDM travels is “Documenting your work”. Those 3 words can scare a lot of people – let’s face it that means spending time writing things down, or creating scripts, or it could be viewed as taking time away from conducting research and analysis. Yes, I know, I know – and…
ViewWhat is the value of classifying your schema
When you use the Semantic Engine to create a schema, one of the first things you are asked to do is to classify your schema. It might seem simple, but as you move further from your domain, what seems like an obvious classification to you may not be so obvious to people outside of your…
ViewA Peek Inside the Ontario Dairy Research Centre – Part 3
On our last blog post, we talked about events and feed data collected routinely at the Ontario Dairy Research Centre (ODRC). Today we’ll talk about milk and milk analysis data. If you missed the other parts of this series, check the links at the end of this post to learn more about other types of…
ViewOrganizing your data – Research Data Management (RDM)
Show of hands – how many people reading this blog know where their current research data can be found on their laptop or computer? No peaking and no searching! Can you tell me where your data is without looking? Let’s be honest now! I suspect a number do know where your data is, but I…
ViewGuidance for designing datasets
Recommendation: Long format of datasets is recommended for schema documentation. Long format is more flexible because it is more general. The schema is reusable for other experiments, either by the researcher or by others. It is also easier to reuse the data and combine it with similar experiments. Data must help answer specific questions or…
ViewA Peek Inside the Ontario Dairy Research Centre – Part 2
Let’s continue to peek into more of the types of data collected routinely at the Ontario Dairy Research Centre (ODRC). Today we’ll talk about health, reproductive, preventive, and research-specific events data, as well as feed analysis and intake data. If you missed part one of this series, read A Peek Inside the Ontario Dairy Research…
ViewResearch Data Life Cycle
Anyone who knows me or has sat in one of my classes, will know how much I LOVE the data life cycle. As researchers we have been taught and embraced the research life cycle and I’m sure many of you could recite how that works: Idea → Research proposal → Funding proposal → Data collection…
ViewThe different types of files used in Overlays Capture Architecture
Overlays Capture Architecture (OCA) is a structured way to describe data schemas. By making a schema a collection of separate functional parts bundled together, you can introduce the benefits of modular design. A modular schema means you can have multiple parties using their own expertise for separate features of a schema. For example, you could…
ViewA Peek Inside the Ontario Dairy Research Centre – Part 1
The Ontario Dairy Research Centre (ODRC) stands as an example to the importance of data-driven research in the dairy industry. With a dedicated commitment to several areas of dairy research, this research centre has been collecting and storing an extensive array of data through regular operations and exciting research projects. Today, we’re peeking into three…
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