Catalogue

I’m sure by now you’ve heard of the AAFC news – seven research facilities closing with many job cuts.  Research facilities with over a century of research, data, reports….  Oh you all know where I’m going with this!!!  Yup!  Where is all that data?  Gone?  Hidden?  Maybe in some repository?  I don’t know!

What I do know is that we, as an industry and as data researchers and archivists, need to seriously think about that data!   Lacombe Research Centre – 119 years of research – many of these in the field of meat science!  If anyone works in that area – you are well aware of the changes we’ve made over time in the quality of our meats – how we evaluate and grade – a lot of that research was developed at Lacombe!   As a beef geneticist who worked in the meat science field, I am crying if that data is not saved or at least documented!  Uh-oh I said that magic word “document”.

I’m trying to stay optimistic and hopeful – but when I attend industry related meetings and the primary question that arises is “What data?” followed by “Where is the data?” I get scared!  The only reason I am familiar with the type of research and data that was collected at Lacombe is because of my research background.  If I was to run a search today for pork grade data – ok – let’s try it for giggles.

Hmmm…  ok I should add Canada and see if that changes anything….

Yup!  As I suspected nothing but reports – no data!  So – that initial question of “What data?” followed by “Where is the data?” is not being answered!

Two points I want to make here:

1. Data is NOT easy to find – nothing showing up for Lacombe information?   If you didn’t know this data existed you wouldn’t know to ask about it.  The classic “If you don’t know you don’t know!”  So – if we don’t know it exists then it’s ok to let it go?  Maybe I shouldn’t worry about the data that’s been collected for the past 119 years?
2. This is the MAIN problem that we are trying to solve with both ADC and the CS-DCC!   A catalogue of data sources to search across.  A place to visit to determine IF the data exists – followed by where the data exists.  BUT if we don’t know it exists or if it disappears then….

Let’s wake up and acknowledge that our data is VALUABLE and needs to be preserved!

Let’s hope I am wrong and the data collected at the seven AAFC facilities slated for closure will be preserved and FAIR!

At Agri-food Data Canada (ADC), we are developing tools to help researchers create high-quality, machine-readable metadata. But what exactly is metadata, and what types does ADC work with?

What Is Metadata?

Metadata is essentially “data about data.” It provides context and meaning to data, making it easier to understand, interpret, and reuse. While the data itself doesn’t change, metadata describes its structure, content, and usage. Different organizations may define metadata slightly differently, depending on how they use it, but the core idea remains the same: metadata adds value by enhancing data context and improving the FAIRness of data.

Key Types of Metadata at ADC

At ADC, we focus on several types of metadata that are especially relevant to research outputs:

1. Catalogue Metadata

Catalogue metadata describes the general characteristics of a published work—such as the title, author(s), publication date, and publisher. If you’ve ever used a library card catalogue, you’ve interacted with this type of metadata. Similarly, when you cite a paper in your research, the citation includes catalogue metadata to help others locate the source.

2. Schema Metadata

Schema metadata provides detailed information about the structure and content of a dataset. It includes descriptions of variables, data formats, measurement units, and other relevant attributes. At ADC, we’ve developed a tool called the Semantic Engine to assist researchers in creating robust data schemas.

3. License Metadata

This type of metadata outlines the terms of use for a dataset, including permissions and restrictions. It ensures that users understand how the data can be legally accessed, shared, and reused.

These three types of metadata play a crucial role in supporting data discovery, interpretation, and responsible reuse.

Combining Metadata Types

Metadata types are not isolated—they often work together. For example, catalogue metadata typically follows a structured schema, such as Darwin Core, which itself has licensing terms (license metadata). Interestingly, Darwin Core is also catalogued: the Darwin Core schema specification has a title, authors, and a publication date.

– written by Carly Huitema

Oh WOW!  Back in October I talked about possible places to store and search for data schemas.   For a quick reminder check out Searching for Data Schemas and Searching for variables within Data Schemas.   I think I also stated somewhere or rather sometime, that as we continue to add to the Semantic Engine tools we want to take advantage of processes and resources that already exist – like Borealis, Lunaris, and odesi. In my opinion, by creating data schemas and storing them in these national platforms, we have successfully made our Ontario Agricultural College research data and Research Centre data FAIR.

But, there’s still a little piece to the puzzle that is missing – and that my dear friends is a catalogue.  Oh I think I heard my librarian colleagues sigh :).    Searching across National platforms is fabulous!  It allows users to “stumble” across data that they may not have known existed and this is what we want.  But, remember those days when you searched for a book in the library – found the catalogue number – walked up a few flights of stairs or across the room to find the shelf and then your book?  Do you remember what else you did when you found that shelf and book?   Maybe looked at the titles of the books that were found on the same shelf as the book you sought out?  The titles were not the same, they may have contained different words – but the topic was related to the book you wanted.  Today, when you perform a search – the results come back with the “word” you searched for.   Great! Fabulous!  But it doesn’t provide you with the opportunity to browse other related results.   How these results are related will depend on how the catalogue was created.

I want to share with you the beginnings of a catalogue or rather catalogues.   Now, let’s be clear, when I say catalogue, I am using the following definition: “a complete list of items, typically one in alphabetical or other systematic order.” (from the Oxford English Dictionary).  We have started to create 2 catalogues at ADC – one is the Agri-food research centre schema library listing all the data schemas currently available at the Ontario Dairy Research Centre and the Ontario Beef Research Centre and the second is a listing of data schemas being used in a selection of Food from Thought research projects.

As we continue to develop these catalogues, keep an eye out for more study level information and a more complete list of data schemas.

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