{"id":"W2074846102","doi":"10.1038/ejhg.2013.131","title":"Data sharing in large research consortia: experiences and recommendations from ENGAGE","year":2013,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Research Data Management Practices","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Norwegian Institute of Public Health; Karolinska Institutet; University of Oxford; Vrije Universiteit Amsterdam; Wellcome Trust; Helsingin Yliopisto","keywords":"Data sharing; Genetic data; Survey data collection; Data science; Knowledge management; Business; Public relations; Computer science; Political science; Medicine; Environmental health; Alternative medicine","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":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3047959,0.0008038128,0.001158838,0.002854194,0.01149177,0.02543411,0.006877649,0.007979093,0.005037523],"category_scores_gemma":[0.3782289,0.001269082,0.002397051,0.005885506,0.009517171,0.03570305,0.0332084,0.008973492,0.001703332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006805562,"about_ca_system_score_gemma":0.03101825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006164088,"about_ca_topic_score_gemma":0.008165373,"domain_scores_codex":[0.6171491,0.3197164,0.02439197,0.005977142,0.01938456,0.01338097],"domain_scores_gemma":[0.4717298,0.3609784,0.02228082,0.0393683,0.04948648,0.05615621],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004049363,0.0007560271,0.06470356,0.003534289,0.0002362294,0.00484775,0.5947421,0.001075003,0.001509046,0.02769809,0.06785408,0.232639],"study_design_scores_gemma":[0.0001064324,0.0003654508,0.006583559,0.002743995,0.0000682231,0.002406853,0.5978739,0.0009947056,0.0007635565,0.02439215,0.3634612,0.0002398958],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1896515,0.009558266,0.04241798,0.7123324,0.002104858,0.002183181,0.0008481996,0.001053876,0.0398498],"genre_scores_gemma":[0.7679858,0.01598142,0.1244859,0.06998047,0.001204324,0.003852956,0.00257389,0.0008332189,0.01310204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9931223,"threshold_uncertainty_score":0.8573104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3909849470253792,"score_gpt":0.4525716711664441,"score_spread":0.06158672414106486,"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."}}