{"id":"W2549065936","doi":"10.1371/journal.pcbi.1005128","title":"Ten Simple Rules for Developing Public Biological Databases","year":2016,"lang":"en","type":"editorial","venue":"PLoS Computational Biology","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute","keywords":"Biological database; Database; Computer science; Quality (philosophy); Set (abstract data type); Biological data; Data science; World Wide Web; Bioinformatics; Biology","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":[0.1702757,0.002441352,0.001946548,0.009752526,0.007954692,0.0263882,0.01508268,0.01291741,0.00780924],"category_scores_gemma":[0.2918105,0.003805653,0.002973223,0.008862392,0.01290715,0.03728283,0.02055669,0.01628424,0.01812047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006993688,"about_ca_system_score_gemma":0.01997391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0062281,"about_ca_topic_score_gemma":0.005446769,"domain_scores_codex":[0.8022225,0.06767,0.04198663,0.01413671,0.06877646,0.00520767],"domain_scores_gemma":[0.7082965,0.1454936,0.01797632,0.04568338,0.07092313,0.01162703],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002419854,0.0003880286,0.00551029,0.00144018,0.0001028412,0.0006811571,0.002174841,0.004125808,0.001228353,0.6411525,0.1563889,0.1865651],"study_design_scores_gemma":[0.0001947411,0.0001244621,0.0008523896,0.002766512,0.0001129248,0.001020527,0.00125694,0.01471893,0.003875053,0.4464121,0.5284113,0.0002541924],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"editorial","genre_scores_codex":[0.004879053,0.001888877,0.8667018,0.05203065,0.002341734,0.006371913,0.002862131,0.01022678,0.05269704],"genre_scores_gemma":[0.01686925,0.001048341,0.9610483,0.007848366,0.0005263114,0.002788976,0.004042575,0.0008023339,0.005025606],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.8297243,"threshold_uncertainty_score":0.9005149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3304295872358272,"score_gpt":0.4498763318757231,"score_spread":0.1194467446398959,"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."}}