{"id":"W4387007406","doi":"10.32942/x29025","title":"Don’t make genetic data disposable: Best practices for genetic and genomic data archiving","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Research Data Management Practices","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"U.S. Geological Survey; Biodiversa+; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Metadata; Data science; Repurposing; Genetic data; Best practice; Data management; Field (mathematics); Computer science; Genomics; World Wide Web; Biology; Ecology; Data mining; Genome; Political science; Population; Sociology","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"],"consensus_categories":[],"category_scores_codex":[0.1177328,0.001001906,0.001613105,0.009049208,0.004865027,0.02829684,0.01049335,0.007038143,0.003248144],"category_scores_gemma":[0.2441565,0.001630005,0.001936666,0.0163007,0.01502093,0.04365281,0.01079623,0.0123148,0.005295326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00668925,"about_ca_system_score_gemma":0.01884307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390297,"about_ca_topic_score_gemma":0.008732365,"domain_scores_codex":[0.9190338,0.03256603,0.01348047,0.008079787,0.02509805,0.001741829],"domain_scores_gemma":[0.6873397,0.1147121,0.01254151,0.113893,0.06663028,0.004883547],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001652096,0.0001715174,0.005028772,0.004443893,0.0002499497,0.0003829548,0.01714438,0.002565937,0.004135158,0.2133621,0.09680407,0.655546],"study_design_scores_gemma":[0.00004169713,0.00004592855,0.002081281,0.007432519,0.00009351598,0.0008369255,0.007358633,0.002168744,0.00576834,0.264611,0.7093009,0.0002604021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01179043,0.1104174,0.5462015,0.2912808,0.005570866,0.0006223599,0.001576898,0.004644536,0.02789526],"genre_scores_gemma":[0.05629974,0.09941017,0.806428,0.02172151,0.002202119,0.0008921403,0.003128037,0.003576554,0.006341804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8822672,"threshold_uncertainty_score":0.6226379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4256814650014928,"score_gpt":0.4444524972125066,"score_spread":0.01877103221101373,"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."}}