In the Digital Vivarium, every cage is subject to automated daily evaluations to measure the moisture content in the bedding, guiding you towards a cage change schedule that aligns with the actual needs instead of a rigid calendar. Utilising a machine learning approach, the DVC® system enhances its daily moisture assessments of cage bedding by integrating your default configuration data with your continuous feedback. This method promotes a higher level of uniformity across your facility.

Embracing this innovative approach offers considerable advantages:

· Significant reduction in the frequency of cage changes, commonly between 40% and 60%, translating to lower operational costs through the conservation of resources like energy and supplies and a decrease in material needs and waste, such as bedding.

· Improved animal welfare by minimising stress related to bedding changes.

· A reduction in the workload for staff, which may alleviate ergonomic concerns associated with this repetitive task.

Many digital facilities have already adopted these advancements, witnessing substantial improvements in economic efficiency, standards of animal welfare, and human resource management. 

DVC® User interface primary examples:

Fig 1: In the DVC® workplace User Interface, the user sets the policy mode for changing cages in the Vivarium (partial or total).


Fig 2: In the DVC® workplace User Interface, the user can create unlimited custom changing protocols that overrule standard DVC® changing Protocol (fig. 1). This feature is ideal for specific experimental cages that will be changed all at the same time (weekly or bi-weekly) without introducing external bias.


Fig 3: In the DVC® workplace User Interface, there is a specific report called “Bedding Activities” recapping in full detail all the performed bedding change activities in a selected time interval for all the registered cages.


Fig 4: In the DVC® Operator User Interface, there is a section called “Bedding Change” in the electronic cage label, where the user can easily find (and filter) all the cage change activities performed on this cage.