The modern computer-aided screening systems require a large amount of well-annotated training data to produce robust and consistent diagnostic performance. Furthermore, the public datasets designed to evaluate automated spinal disorders screening frameworks lack quantitative labels, which are marked by expert radiologists and clinically validated by spinal surgeons. This paper presents a dataset containing high-resolution (and well-labeled) mid-sagittal views of lumbar spine magnetic resonance imaging (MRI) scans. These scans also contain vertebral body masks along with clinically significant spinal measurements, including lumbar height, intervertebral body distances, vertebral body sidewall dimensions, vertebral body superior and inferior end-plates dimensions, lumbar lordotic angles, and lumbosacral angles. The mid-sagittal view MRI scans within the proposed dataset were first procured, and then they were manually marked by the expert radiologists and validated by the expert spinal surgeons. Afterward, different spinal measurements were recorded, which serves as a benchmark to evaluate the autonomous frameworks for predicting spinal misalignments. In addition to this, the proposed dataset is, to the best of our knowledge, the first composite database that contains lumbar spine mid-sagittal images along with spinal attributes and detailed markings of radiologists duly verified by the spinal surgeons. The proposed dataset, unlike its competitors, also introduces a quantitative vote to the clinicians and researchers in the assessment process of lumbar spine disorders.