{"id":"W3060958463","doi":"10.1093/jlb/lsaa065","title":"Policy-aware data lakes: a flexible approach to achieve legal interoperability for global research collaborations","year":2020,"lang":"en","type":"article","venue":"Journal of Law and the Biosciences","topic":"Research Data Management Practices","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Wellcome Trust","keywords":"Interoperability; Metadata; Data sharing; Computer science; Data governance; Flexibility (engineering); Data science; Corporate governance; Reuse; World Wide Web; Business; Engineering; Data quality","routes":{"ca_aff":true,"ca_fund":false,"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":[0.1121971,0.001071081,0.001279479,0.006862319,0.009097757,0.02030406,0.009925316,0.008864679,0.005830293],"category_scores_gemma":[0.1123304,0.002328393,0.003531893,0.006885454,0.02023898,0.06879313,0.04236112,0.01199012,0.002815376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01240309,"about_ca_system_score_gemma":0.03254378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006285924,"about_ca_topic_score_gemma":0.005731521,"domain_scores_codex":[0.9239482,0.03943686,0.006760761,0.008416458,0.01752856,0.003909132],"domain_scores_gemma":[0.8694956,0.0409631,0.009117898,0.06273967,0.01202232,0.005661494],"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":[0.0000204284,0.00006184731,0.0007504748,0.00006452672,0.0000297252,0.0001421617,0.002318894,0.004786675,0.0007972145,0.9591149,0.003271912,0.02864128],"study_design_scores_gemma":[0.00003860983,0.0000380605,0.0002564018,0.0001836149,0.00003077038,0.0001353942,0.001273759,0.02094363,0.001870267,0.8881156,0.08702953,0.00008429946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005958713,0.0001991524,0.9545873,0.01895677,0.0001281855,0.0005718005,0.0001431274,0.001705515,0.01774944],"genre_scores_gemma":[0.170347,0.0003607148,0.8151032,0.002869709,0.0002336669,0.001461037,0.0004612932,0.0007101239,0.008453278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9900747,"threshold_uncertainty_score":0.5933619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3881764793933642,"score_gpt":0.4733123496311253,"score_spread":0.08513587023776109,"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."}}