def METHOD_NAME(db, dataset_name):
    match_d = {"name": dataset_name}
    dataset_dict = db.datasets.find_one(match_d)
    sample_collection_name = dataset_dict.get("sample_collection_name", None)
    frame_collection_name = dataset_dict.get("frame_collection_name", None)
    if sample_collection_name and not frame_collection_name:
        dataset_dict["frame_collection_name"] = (
            "frames." + sample_collection_name
        )
    app_config = dataset_dict.get("app_config", None)
    if app_config is not None:
        sidebar_groups = app_config.get("sidebar_groups", None)
        if sidebar_groups is not None:
            tags_idx = None
            found_label_tags = False
            for idx, sidebar_group in enumerate(sidebar_groups):
                name = sidebar_group.get("name", None)
                if name == "tags":
                    sidebar_group["paths"] = []
                    tags_idx = idx
                if name == "label tags":
                    sidebar_group["paths"] = []
                    found_label_tags = True
            if tags_idx is not None and not found_label_tags:
                sidebar_groups.insert(
                    tags_idx + 1,
                    {"name": "label tags", "paths": []},
                )
    _update_runs(
        db,
        dataset_dict,
        "brain_methods",
        {"cls": _NEW_SKLEARN_CONFIG_CLS},
        {"cls": _OLD_SKLEARN_CONFIG_CLS, "method": "similarity"},
        {"cls": _OLD_SKLEARN_RESULTS_CLS},
    )
    db.datasets.replace_one(match_d, dataset_dict)