  Lameness is one of the costliest pathological problems affecting dairy
animals. It is usually assessed by trained veterinary clinicians who observe
features such as gait symmetry or gait parameters as step counts in real-time.
With the development of artificial intelligence, various modular systems have
been proposed to minimize subjectivity in lameness assessment. However, the
major limitation in their development is the unavailability of a public dataset
which is currently either commercial or privately held. To tackle this
limitation, we have introduced CowScreeningDB which was created using sensory
data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria,
Spain. It consists of a multi-sensor dataset built on data collected using an
Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the
collection environment, sampling technique, information regarding the sensors,
the applications used for data conversion and storage make the dataset a
transparent one. This transparency of data can thus be used for further
development of techniques for lameness detection for dairy cows which can be
objectively compared. Aside from the public sharing of the dataset, we have
also shared a machine-learning technique which classifies the caws in healthy
and lame by using the raw sensory data. Hence validating the major objective
which is to establish the relationship between sensor data and lameness.
