Annotated datasets are essential for developing and evaluating argument mining systems, yet information about argument mining datasets remains scattered across papers, repositories, and task-specific surveys. To address this, we introduce AMResources (http://purl.archive.org/amresources), an online catalog that organizes argument mining datasets by task, and captures relationships among datasets, releases, and papers. We draw particular attention to relationships such as re-annotation and dataset extension. To curate dataset information into a consistent and provenance-aware structure, AMResources links datasets to canonical papers. For each dataset release, AMResources records standardized metadata such as language, genre, unit type and unit count, annotator characteristics, agreement reporting, and accessibility. We argue that such structured dataset documentation remains critical in the era of large language models, where annotated datasets increasingly serve as high-quality evaluation benchmarks and where tracing dataset provenance and annotation layers is necessary for systematic comparisons across tasks.