Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
ADC’s Re-usable Data Explorer App
Let’s take a little jaunt back to my FAIR posts. Remember that first one? R is for Reusable? Now, it’s one thing to talk about data re-usability, but it’s an entirely different thing to put this into action. Well, here at Agri-food Data Canada or ADC we like to put things into action, or think…
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Understanding a schema text file
When you create a schema using the Semantic Engine you are documenting information that can make your dataset more FAIR, helping others use and understand your data. The schema created using the Semantic Engine is understood by machines and is written in JSON. At first glance, it is not so easy for people to read…
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Example video for writing a schema
At the Semantic Engine we have created a new video example where we walk through the process of describing a dataset with a schema. We are using a dataset with milking data that has been downloaded from the research dairy barn. You can watch the video on YouTube or follow along in the schema writing…
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Import entry codes from a schema
The Semantic Engine has a new upgrade for importing existing entry codes! If you don’t know what entry codes are, you can check out our blog post about how to use entry codes. We also walk through an example of entry codes in our video tutorial. While you can type your entry codes and labels…
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Array Datatypes for attributes
In Overlays Capture Architecture (OCA), when using the Semantic Engine you must assign data types to all of your attributes (aka variables). When do you use the array datatype? You use an array data type when a data record for that attribute would hold multiple values of a specific data type, arranged in a list-like…
ViewCultivating a Backup Culture: Safeguarding Research Data
In the world of academic research, data is the cornerstone of discovery and innovation. Graduate students and faculty members invest countless hours into gathering, analyzing, and interpreting data, making its protection crucial. Despite this, the importance of data backups often gets overlooked until disaster strikes. By cultivating a backup culture within research teams, we can…
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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…
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