  Magnetic resonance imaging (MRI) is a crucial tool to identify brain
abnormalities in a wide range of neurological disorders. In focal epilepsy MRI
is used to identify structural cerebral abnormalities. For covert lesions,
machine learning and artificial intelligence algorithms may improve lesion
detection if abnormalities are not evident on visual inspection. The success of
this approach depends on the volume and quality of training data.
  Herein, we release an open-source dataset of preprocessed MRI scans from 442
individuals with drug-refractory focal epilepsy who had neurosurgical
resections, and detailed demographic information. The MRI scan data includes
the preoperative 3D T1 and where available 3D FLAIR, as well as a manually
inspected complete surface reconstruction and volumetric parcellations.
Demographic information includes age, sex, age of onset of epilepsy, location
of surgery, histopathology of resected specimen, occurrence and frequency of
focal seizures with and without impairment of awareness, focal to bilateral
tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of
surgery, and a total of 1764 patient years of post-surgical follow up.
Crucially, we also include resection masks delineated from post-surgical
imaging.
  To demonstrate the veracity of our data, we successfully replicated previous
studies showing long-term outcomes of seizure freedom in the range of around
50%. Our imaging data replicates findings of group level atrophy in patients
compared to controls. Resection locations in the cohort were predominantly in
the temporal and frontal lobes.
  We envisage our dataset, shared openly with the community, will catalyse the
development and application of computational methods in clinical neurology.
