Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
The 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…
View
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…
View
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…
View
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…
View
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…
View
Adding quality comments to datasets
You are using a dataset and you come across some missing values, or unusual entries like NA or ND. What do these mean? Why is there data missing? Quality indicators of individual measurement or observation data can be included directly in your data and will help users of your data understand your data and use…
View
What do you mean you don’t use ISO 8601?
As researchers, we’re no strangers to the complexities of data management, especially when it comes to handling date and time information. Whether you’re conducting experiments, analyzing trends, or collaborating on projects, accurate temporal data is crucial. Like in many other fields, precision is key, and one powerful tool at our disposal for managing temporal data…
View
The R in FAIR
Findable Accessible (where possible) Interoperable Reusable I believe most of us are now familiar with this acronym? The FAIR principles published in 2016. I have to admit that part of me really wants to create a song around these 4 words – but I’ll save you all from that scary venture. Seriously though, how many…
View
Attributes and labels in OCA
When you document your data schema using the Semantic Engine, you are writing a schema in the schema language of Overlays Capture Architecture (OCA). Let’s do a deeper dive into one of the features of OCA. Attributes in OCA When you start using the Semantic Engine, you can either drag and drop your dataset, or…
View