{"id":"W2106994273","doi":"10.1093/ije/dyq111","title":"DataSHIELD: resolving a conflict in contemporary bioscience--performing a pooled analysis of individual-level data without sharing the data","year":2010,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Statistics Canada; The Quebec Population Health Research Network; McGill Genome Centre; University of Ottawa; McGill University and Génome Québec Innovation Centre","funders":"Medical Research Council; University of Leicester; Genome Canada; National Institute for Health and Care Research; Leverhulme Trust; British Heart Foundation; Wellcome Trust","keywords":"Data science; Sample (material); Computer science; Flexibility (engineering); Data sharing; Legislation; Set (abstract data type); Perspective (graphical); Sample size determination; Management science; Data mining; Medicine; Artificial intelligence; Engineering; Political science; Statistics; Mathematics; Law","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":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3688061,0.001617169,0.00417735,0.0144807,0.004002726,0.01953448,0.007446398,0.007287438,0.0160191],"category_scores_gemma":[0.6579453,0.003575549,0.003384,0.02914518,0.01987655,0.02336539,0.02545791,0.01209996,0.007954801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005930796,"about_ca_system_score_gemma":0.03330341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003071227,"about_ca_topic_score_gemma":0.002245534,"domain_scores_codex":[0.5185969,0.3557851,0.06932601,0.016353,0.03786853,0.002070433],"domain_scores_gemma":[0.2297863,0.5106528,0.03293251,0.1733924,0.04300544,0.01023047],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001154194,0.0001179371,0.005299338,0.008203623,0.001037624,0.0006124415,0.01322741,0.002531365,0.001512056,0.5910797,0.1795043,0.19572],"study_design_scores_gemma":[0.0003258845,0.0002446852,0.002624547,0.009605727,0.0002705113,0.0007844673,0.002477607,0.002223779,0.00181709,0.310866,0.6684681,0.0002917098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008840885,0.009453009,0.7754983,0.1263729,0.0100203,0.004471347,0.01687797,0.006469132,0.04199612],"genre_scores_gemma":[0.08609783,0.005792622,0.8328503,0.02688316,0.003775582,0.01827982,0.01514178,0.005303966,0.005874896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9925536,"threshold_uncertainty_score":0.7783745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.334215534565908,"score_gpt":0.4337591458730157,"score_spread":0.09954361130710776,"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."}}