This study examines dataset annotation practices in influential NeurIPS research. Datasets employed in highly cited NeurIPS papers were assessed based on criteria concerning their item population, labelling schema, and annotation process. While high-level information, such as the presence of human labellers and item population, is present in most cases, procedural details of the annotation process are poorly reported. Notably, 48% of datasets lack details on annotator training, 43% omit inter-rater reliability, and 28% are not publicly accessible. Temporal comparisons show minor improvements, but no substantial progress in reporting annotation methodology. A complementary analysis of 49 NeurIPS papers published since 2020 shows that researchers often discuss the broader impact of their work, yet do not include datasets or their annotations in these assessments. These findings highlight a lack of standardisation in annotation reporting and call for more robust practices that ensure transparency, auditability, and reproducibility in machine learning research.