Parkinson’s disease (PD) is a common and progressive neurodegenerative disorder with motor symptoms and a variety of non-motor symptoms. Experts regularly include handwriting as one of the Parkinsonian motor symptoms of PD and as a valuable tool that can aid in diagnosing and tracking the disease’s progression. PD patients have two periods. ‘On’ time is when levodopa is working well and your symptoms are controlled. ‘Off’ time is when levodopa is no longer working well and symptoms such as tremor, rigidity and slow movement re-emerge. To our knowledge, all existing publicly available datasets allow PD to be identified using only one period. No publicly available online handwriting datasets are dedicated to the analysis of PD using these two periods. Therefore, in this paper, we present our new online Arabic handwriting dataset for analysing PD, which we will make publicly available so that it could potentially be used for diagnosis, screening and monitoring the progression of PD. Our dataset was collected from 30 healthy controls and 30 PD patients in both “off” and “on” at the Neurology Department, Habib Bourguiba Hospital, Sfax, Tunisia. All participants performed five different handwriting tasks. The tasks included drawing repetitive ellipses, a spiral, repetitive digits and Arabic word writing. We hope that our new dataset will help researchers in the early detection of Parkinson’s disease, inpatient rehabilitation and quantification of therapeutic effects.