Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
The “Elephant in the Room” – Data Ownership
Alrighty let’s address that proverbial Elephant in the Room – WHO owns the data? This question goes round and round and round – but does it ever land comfortably for everyone? We have been discussing this topic for quite a while here at Agri-food Data Canada, and it keeps cropping up in all the projects…
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From Siloed Systems to Shared Success: The Problem with Doing It All Yourself
Part of the blog series on Collaborative Research IT Infrastructure In our first post, we discussed the challenges universities face with fragmented IT systems and the need for unified solutions. Here, we explore the specific issues that arise when research teams independently manage their IT infrastructure. Research IT infrastructure often develops in response to immediate…
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Data – So What?
I have been attending industry-focused meetings over the past month and I’m finding the different perspectives regarding agri-food data very interesting and want to bring some of my thoughts to light. I first want to talk about my interpretation of the academic views. In research and academia we focus on Research Data Management, how and…
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Ontologies for agriculture
Using an ontology in agri-food research provides a structured and standardized way to manage the complex data that is common in this field. Ontologies are an important tool to improve data FAIRness. Ontologies define relationships between concepts, allowing researchers to organize information about crops, livestock, environmental conditions, agricultural practices, and food systems in a consistent…
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From Siloed Systems to Shared Success: A Blog Series on Collaborative Research IT Infrastructure
In today’s data-driven research environment, universities face a growing challenge: while researchers excel at pushing the boundaries of knowledge, they often face challenges managing the technology that supports their work. Many university research teams still operate on isolated, improvised systems for computing and data storage—servers tucked in closets or offices, ad-hoc storage solutions, and no…
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Searching for variables within Data Schemas
My last post was all about where to store your data schemas and how to search for them. Now let’s take it to the next step – how do I search for what’s INSIDE a data schema – in other words how do I search for the variables or attributes that someone has described in…
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Understanding the format overlay
With the introduction of using OCA schemas for data verification let’s dig a bit more into the format overlay which is an important piece for data verification. When you are writing a data schema using the Semantic Engine you can build up your schema documentation by adding features. One of the features that you can…
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Using the Data Entry Excel tool
When data entry into an Excel spreadsheet is not standardized, it can lead to inconsistencies in formats, units, and terminology, making it difficult to interpret and integrate research data. For instance, dates entered in various formats, inconsistent use of abbreviations, or missing values can give problems during analysis leading leading to errors. Organizing data according…
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Searching for ADC Data Schemas
Alrighty – so you have been learning about the Semantic Engine and how important documentation is when it comes to research data – ok, ok, yes documentation is important to any and all data, but we’ll stay in our lanes here and keep our conversation to research data. We’ve talked about Research Data Management and…
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Data Quality Annotations
What do you do when you’ve collected data but you need to also include notes in the data. Do you mix the data together with the notes? Here we build on our previous blog post describing data quality comments with worked examples. An example of quality comments embedded into numeric data is if you include…
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