<jupyter_start><jupyter_text>String searches with dataset name to get back pub-dataset dyads<jupyter_code>dataset_string = "IRI infoscan data"
pub_dataset_dyads  = metadata_funs.return_string_search_dyads(exact_match= True,dataset_string = dataset_string, api_client = api_client)
pub_dataset_dyads[0]
# list(pub_dataset_dyads[0].keys())
print(pub_dataset_dyads[0]['FOR'])
pub_dataset_dyads[0]['title']
list(pub_dataset_dyads[0].keys())<jupyter_output>[{'id': '3292', 'name': '1402 Applied Economics'}, {'id': '3313', 'name': '1403 Econometrics'}]<jupyter_text>Doi searches to get back publication metadata<jupyter_code># doi = '10.1002/hec.3883'
doi = '10.1093/cid/cix668'
doi_metadata = metadata_funs.run_doi_search(doi_id = doi , api_client = api_client)<jupyter_output><empty_output><jupyter_text>Person metadata<jupyter_code>author_dimensions_id = 'ur.01356101567.65'
author_metadata = metadata_funs.run_author_search(author_dimensions_id,api_client)
# author_metadata<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12761714855433698, 'fraction_numerical': 0.05284147557328016, 'mean_word_length': 11.096385542168674, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '381806', 'license': 'CC0-1.0', 'n_tokens_mistral': 413, 'n_tokens_neox': 375, 'n_words': 54, 'path': 'OOD/scrap/example_searches.ipynb', 'repo_name': 'Coleridge-Initiative/RichContextMetadata', 'size': '1003'}