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In addition to being highly prevalent and variable in its presentation, chronic low back pain (cLBP) has the unique challenge of having multi-faceted barriers to recovery [1-3]. This has resulted in high costs of care for people with cLBP, largely without associated improvements in pain, disability, and quality of life [4]. In fact, LBP is the 4th leading cause of global disability adjusted life years in individuals 25–49 years of age and 8th in those 50–74 years [5, 6]. While previous research has aimed to collect large datasets to identify key characteristics of persons with cLBP [7-10], most focused only on a few data domains. None have comprehensively compiled aspects from various research domains within the same individual. Given the multidimensional nature of cLBP, it is imperative to collect data on all of the features of cLBP and understand the key co-contributors to the experience of cLBP and potential barriers to recovery to optimize treatment approaches. Together, these measurements, led by domain experts in their respective fields, have yielded an unprecedented dataset to facilitate understanding of the diverse characteristics contributing to the experience of cLBP. Moreover, by collecting this broad set of data from different scientific perspectives (biological, biomechanical, and behavioral) within the same participants, this work will facilitate examination of the interactions of these various components of cLBP to allow a more comprehensive assessment of cLBP. Acknowledging the value to the community of this unique set of comprehensive characteristics collected for our cohort of persons with cLBP, herein we present the baseline features of our participants, and report data from the enrollment visits in domains including demographics and clinical characteristics, patient-reported outcomes, quantitative sensory testing, behaviors and activity, physical exam and performance, kinematics, and circulating inflammatory mediators. Future work will follow this valuable cohort longitudinally, collecting data on treatments experienced by our participants and patient-reported outcomes, and proposing advanced modeling to identify distinct cLBP subgroups or “phenotypes,” to be evaluated in future studies for their ability to predict response to treatment. The data will also be made publicly available through the HEAL data repository [12] to facilitate additional hypothesis testing and future study designs. It is hoped that sharing these data with the research, clinical, and stakeholder community will be catalytic in multi-disciplinary efforts to improve care for this important and widespread condition. This work was supported by the National Institutes of Health, U19AR076725-01. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.