{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0324927,0.0008502031,0.001064491,0.006528228,0.002847694,0.007838904,0.003085661,0.002695731,0.005356018],"category_scores_gemma":[0.04189427,0.001194821,0.002467891,0.007990186,0.00365215,0.01705081,0.01026302,0.005186076,0.005627706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004747475,"about_ca_system_score_gemma":0.02281114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02011343,"about_ca_topic_score_gemma":0.0164376,"domain_scores_codex":[0.9806225,0.005221073,0.00485012,0.002407693,0.005917811,0.0009807098],"domain_scores_gemma":[0.9604248,0.01131549,0.002618528,0.01638601,0.007393816,0.001861249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002178011,0.0002442944,0.006863076,0.001402074,0.0001535917,0.0005326226,0.004171466,0.003109714,0.009442001,0.6650336,0.1644682,0.1443616],"study_design_scores_gemma":[0.000032546,0.00003038114,0.002306902,0.0006513876,0.00005656555,0.0003023441,0.0006537235,0.004965511,0.005024363,0.07676307,0.90913,0.00008325111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005642877,0.0005740579,0.8924962,0.009876907,0.0007911886,0.002075998,0.03530548,0.01436081,0.03887636],"genre_scores_gemma":[0.03410628,0.001376078,0.8616058,0.005050913,0.0003641104,0.002462005,0.07849028,0.004242786,0.01230172],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9969143,"threshold_uncertainty_score":0.1718398,"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."}}