Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
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…
ViewWhat 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…
ViewThe 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…
ViewAttributes 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…
ViewEnhancing Livestock Research with Seamless Data Integration
In the rapidly evolving landscape of livestock research, the ability to harness data from diverse sources is paramount. From sensors monitoring animal health to weather data influencing grazing patterns, the insights derived from integrated data can drive informed decisions and innovative solutions. However, integrating data into a centralized livestock research database presents a myriad of…
ViewWriting better file names
We first talked about writing filenames back in a post about Organizing your data: Research Data Management (RDM) To further improve your filenaming game, check out a naming convention worksheet from Caltech that helps researchers create a great filenaming system that works with their workflows. Example: My file naming convention is “SA-MPL-EID_YYYYMMDD_###_status.tif” Examples are “P1-MUS-023_20200229_051_raw.tif”…
ViewDocumenting your work- Statistical Analysis (RDM continued)
How many of you document your statistical analysis code? SAS and R users, do you add comments in your programs? Or do you fly by the seat of your pants, write, modify code, and know that you’ll remember what you did at a later time? I know we don’t have the time to add all…
ViewStreamlining Data Integrity: The Role of Entry Codes in Data Entry
Maintaining data quality can be a constant challenge. One effective solution is the use of entry codes. Let’s explore what entry codes entail, why they are crucial for clean data, and how they are seamlessly integrated into the Semantic Engine using Overlays Capture Architecture (OCA). Understanding Entry Codes in Data Entry Entry codes serve…
ViewAutomated Data Cleaning and Quality Assurance in Livestock Databases
Introduction Data is the backbone of informed decision-making in livestock management. However, the volume and complexity of data generated in modern livestock farms pose challenges to maintaining its quality. Inaccurate or unreliable data can have profound consequences on research programs and overall farm operations. In this technical exploration, we delve into the realm of automated…
ViewDocumenting your work- README: Research Data Management (RDM)
Happy New Year everyone!!! Welcome to 2024 – Leap year!! Oh wow! How time is really flying by! It’s so easy for us to say this and see it happen in our every day lives – BUT – yes it also happens at work and with our research. I remember as a graduate student, in…
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