{"id":"W6912810661","doi":"10.5281/zenodo.6564423","title":"Accessing, analyzing and visualizing research data metadata using DataCite and Jupyter Notebooks","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Metadata; Toolbox; Data sharing; Data element; Subject (documents); Presentation (obstetrics); Reuse; Metadata modeling; Citation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.01039726,0.0001552481,0.0001783533,0.0008970245,0.008019759,0.04002411,0.009023028,0.00002970267,0.0004224924],"category_scores_gemma":[0.001964049,0.0001755749,0.00001640542,0.001465575,0.0003089494,0.0520838,0.08254908,0.0007879835,0.000066111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001452462,"about_ca_system_score_gemma":0.00001577996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001473818,"about_ca_topic_score_gemma":0.000001445799,"domain_scores_codex":[0.9940692,0.002284984,0.0003145473,0.001354356,0.001309572,0.0006673312],"domain_scores_gemma":[0.9962847,0.0001958516,0.000161529,0.002775892,0.0003297545,0.0002522604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001822662,0.0004227952,0.0001468794,0.0004768406,0.0004417335,0.0004204194,0.003197169,0.0002107298,0.02578185,0.3208644,0.07204767,0.5758072],"study_design_scores_gemma":[0.0003320912,0.0001228093,0.000419568,0.00002224855,0.00001885539,0.0001513184,0.0005365944,0.04828193,0.00009472694,0.0002657183,0.9495445,0.0002096589],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05453075,0.001289964,0.9191229,0.004043695,0.0002029191,0.00141768,0.00136314,0.000994494,0.01703444],"genre_scores_gemma":[0.934068,0.001922637,0.04659531,0.0007117628,0.0003772464,5.914289e-7,0.008943904,0.00214,0.005240543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8795373,"threshold_uncertainty_score":0.9963386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3790066674937626,"score_gpt":0.4144793096524885,"score_spread":0.03547264215872592,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}