{"id":"W4380789031","doi":"10.1038/s41431-023-01403-y","title":"Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory","year":2023,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; McGill University Health Centre","funders":"Medical Research Council; Canadian Institutes of Health Research; European Commission; Horizon 2020 Framework Programme; Government of Canada","keywords":"Custodians; General Data Protection Regulation; Collaboratory; Data Protection Act 1998; Data governance; Data sharing; Downstream (manufacturing); Business; Internet privacy; Political science; Computer science; Law; Data quality; World Wide Web","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":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0241513,0.000134949,0.0003114792,0.00005943875,0.0006591963,0.0001554344,0.002145855,0.00006906452,0.00007768031],"category_scores_gemma":[0.01025846,0.00005895581,0.00009066636,0.0003912536,0.002561524,0.00003357007,0.001407366,0.002975091,0.00006108948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002711258,"about_ca_system_score_gemma":0.0005426009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451222,"about_ca_topic_score_gemma":0.0006039092,"domain_scores_codex":[0.9955612,0.001826999,0.0008435044,0.000244613,0.001235465,0.0002881924],"domain_scores_gemma":[0.982906,0.01425223,0.0003576764,0.001703841,0.000519554,0.0002607022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001667046,0.0005473164,0.07595986,0.0002064191,0.003024812,0.004103414,0.02205691,0.00008325264,0.008970771,0.01817554,0.7506047,0.1145999],"study_design_scores_gemma":[0.01134989,0.001814932,0.5248417,0.001279819,0.001063421,0.0003357163,0.01010998,0.002438109,0.0001873594,0.03367146,0.4125271,0.0003804813],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8343711,0.006102162,0.0001703744,0.1553813,0.0005501093,0.000313538,0.0001508767,0.00002464222,0.00293589],"genre_scores_gemma":[0.9898537,0.004612184,0.0004498883,0.002442982,0.002146527,0.00000102181,0.0000322253,0.00005715966,0.0004043583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4488819,"threshold_uncertainty_score":0.9993251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6327371977236931,"score_gpt":0.5444516071713719,"score_spread":0.08828559055232121,"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."}}