{"id":"W3214176644","doi":"10.1016/j.xgen.2021.100028","title":"The Data Use Ontology to streamline responsible access to human biomedical datasets","year":2021,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); Montreal Neurological Institute and Hospital; University Health Network; McGill University; Canada's Michael Smith Genome Sciences Centre; McGill Genome Centre","funders":"National Health and Medical Research Council; Horizon 2020 Framework Programme; National Institutes of Health; ZonMw; FP7 Coherent Development of Research Policies; University of Michigan; Government of the United Kingdom; European Bioinformatics Institute; McGill University; Horizon 2020; EOSC-Life; Bayer; Japan Agency for Medical Research and Development; Novartis; European Commission; Broad Institute; International Business Machines Corporation; National Human Genome Research Institute; Wellcome Trust; Intel Corporation","keywords":"Computer science; Ontology; Data science; Data access; Data sharing; Data management; Data discovery; Metadata; Information retrieval; World Wide Web; Data mining; Database","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003895411,0.0001515178,0.0001695794,0.00004032026,0.0002353349,0.0001623648,0.00151691,0.0002022315,0.00002812214],"category_scores_gemma":[0.0009646722,0.0001181786,0.0000358906,0.0001824089,0.0001739141,0.000005306974,0.002859559,0.0001309758,0.0000540189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002135162,"about_ca_system_score_gemma":0.0003434534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005778716,"about_ca_topic_score_gemma":0.00124542,"domain_scores_codex":[0.9984339,0.0001200271,0.0002670227,0.0006551287,0.0001298083,0.0003940913],"domain_scores_gemma":[0.9975784,0.0001224712,0.00005036406,0.001894506,0.00006297785,0.0002912433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001374216,0.0001112086,0.0004250991,0.000008894491,0.00004169991,0.00005641972,0.00004186272,0.00001116003,0.5727733,0.00003962511,0.4036154,0.02273799],"study_design_scores_gemma":[0.0002566792,0.0001839795,0.00133809,0.000005217347,0.00001652276,0.00002378997,0.0001036954,0.00002305589,0.07710853,0.0000399975,0.9207439,0.0001565429],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812144,0.001346957,0.005856836,0.008132207,0.0006850046,0.0002520433,0.002146001,0.0000338434,0.0003327111],"genre_scores_gemma":[0.7466834,0.002611712,0.1283542,0.03127062,0.003499909,0.0001302859,0.06787135,0.0002262223,0.01935236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5171285,"threshold_uncertainty_score":0.4819184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08713909626843905,"score_gpt":0.3653044782949046,"score_spread":0.2781653820264656,"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."}}