Dear Editor, We are pleased to submit our manuscript, titled "NSORN: Designing a Benchmark Dataset for Neurosymbolic Ontology Reasoning with Noise", for consideration in the Journal of Neurosymbolic Artificial Intelligence as part of your Special Issue on Neurosymbolic AI and Ontologies. Given the special issue's focus on the intersection of ontologies and neural systems, we believe our work aligns well with its scope and would make a valuable contribution. In our study, we propose a mechanism for introducing noise into ontologies, particularly in the ABox, and evaluate the performance of existing neurosymbolic reasoners across varying noise levels. We introduce three distinct techniques to generate noise: logical, statistical, and random noise. These methods were applied to the OWL2Bench and Family ontologies, creating benchmark datasets with diverse noise types and levels. Subsequently, we assessed the performance of two state-of-the-art neurosymbolic reasoners, Box2EL and OWL2Vec*, using these benchmarks. We believe that the developed benchmark datasets provide valuable insights for a broad audience of researchers working at the intersection of ontologies and neural-symbolic reasoning. We would like to inform you that we have received an extension from Cogan to finalize and submit this manuscript. Thank you for considering our work. We look forward to your feedback and are happy to provide any additional information if needed. With kind regards, On behalf of all co-authors, Julie Loesch