Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
Preparing data for a schema
Is your data ready to describe using a schema? How can you ensure the fewest hiccups when writing your schema (such as with the Semantic Engine)? What kind of data should you document in your schema and what kinds of data can be left out? Document data in chunks When you prepare to describe your…
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Organizing folders for a project
How should you organize your files and folders when you start on a research project? Or perhaps you have already started but can’t really find things. Did you know that there is a recommendation for that? The TIER protocol will help you organize data and associated analysis scripts as well as metadata documentation. The TIER…
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The F in FAIR
Findable Accessible (where possible) Interoperable Reusable Ah the last Blog post in the series of 4 regading the FAIR principles. The last or the first, depending on how you look at it :). F for Findable! Quick review from the FAIR website: F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are…
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Examples of Entry Codes
Entry codes can be very useful to ensure your data is high quality and to catch mistakes that might mess up with your analysis. For example, you might have taken multiple measurements of two samples (WH10 and WH20) collected during your research. You have a standardized sample name, a measurement (iron concentration) and a condition…
ViewThe Importance of Data Integration in the Dairy Industry
I recently attended the 46th ADSA Discover Conference, themed “Milking the Data – Value Driven Dairy Farming,” and the discussions there really drove home how crucial data integration is for the dairy industry. I want to share some insights and reflections, highlighting why this topic is so important, the challenges we face, and some specific…
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The A in FAIR
Findable Accessible (where possible) Interoperable Reusable Good day everyone! We’re back looking at the FAIR priniciples and have now moved to talk about A for Accessible (where possible). Let’s first review the 2 principles under Accessible: A1. (Meta)data are retrievable by their identifier using a standardised communications protocol A2. Metadata are accessible, even when the…
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Verification vs Validation of Data
Do we verify that the data is correct? Or do we validate it? These two similar terms have different meanings although the two are often conflated. For data, we can make the following distinction: Verification: “Did we collect the data right” Validation: “Did we collect the right data” For verification we are asking if the…
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The I in FAIR
Findable Accessible (where possible) Interoperable Reusable Oh my INTEROPERABILITY! What a HUGE word this is and let’s be honest are we all comfortable with what it means? Remember data – interoperability….. Ah but let’s start with a silly and very basic example first: a tool – more specifically a wrench. Tools, let’s think about a…
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Entry code importing
The Semantic Engine has gotten a recent upgrade for importing entry codes. If you don’t remember what entry codes are, they help with data standardization and quality by limiting what people can enter in a field for a specific attribute. You can read more about entry codes in our entry code blog post. Now, the…
ViewEnsuring Code Consistency and Reproducibility with R projects and renv in R Studio
Ensuring code consistency and reproducibility is paramount. Imagine collaborating on a project where each member uses different package versions, leading to inconsistencies in the results obtained. One of the fundamental steps in ensuring reproducibility is setting up an organized and self-contained R project and leveraging renv, an R package manager, providing a robust solution to…
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