{"id":"W2134038087","doi":"10.1007/s12021-007-0012-5","title":"Sharing and Reusing Gene Expression Profiling Data in Neuroscience","year":2007,"lang":"en","type":"review","venue":"Neuroinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital; Canada's Michael Smith Genome Sciences Centre","funders":"National Institute of General Medical Sciences; National Institutes of Health; Michael Smith Health Research BC","keywords":"Profiling (computer programming); Reuse; Neuroinformatics; Computer science; Data science; Data sharing; Data integration; Open data; Gene expression profiling; Data management; Expression (computer science); Open science; Neuroscience; Gene expression; World Wide Web; Data mining; Psychology; Biology; Gene; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004247096,0.0002897765,0.0004735529,0.0001408891,0.00009578103,0.0001065398,0.000836396,0.0002534295,7.354409e-7],"category_scores_gemma":[0.0001991861,0.0002562341,0.00006237678,0.0001855996,0.00007304372,0.00002599959,0.0009181553,0.0003489942,0.000002302881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001271852,"about_ca_system_score_gemma":0.0001103278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003759831,"about_ca_topic_score_gemma":0.000004951358,"domain_scores_codex":[0.9983127,0.00003053652,0.0006705608,0.0004836661,0.0001788315,0.0003236645],"domain_scores_gemma":[0.9985972,0.00002089313,0.0002331929,0.001041232,0.00002047059,0.00008702992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003270443,0.0001151694,0.0008130759,0.02220149,0.00001556573,0.00006758658,0.0001442516,0.0000596409,0.03683729,0.00003038051,0.0001781608,0.9395047],"study_design_scores_gemma":[0.0002968995,0.00009821026,0.0000132296,0.003141161,0.00006754218,0.000241869,0.0000160665,0.00515496,0.002884411,0.000005977031,0.9875135,0.0005662124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005620799,0.9900997,0.002562049,0.000003175402,0.0004826149,0.0005747138,0.00006527366,0.00002330822,0.0005683325],"genre_scores_gemma":[0.0001481259,0.9940094,0.005046005,0.0001167027,0.0001281603,0.00000414636,0.000466421,0.00004072609,0.00004035582],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9873353,"threshold_uncertainty_score":0.999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.159060467601182,"score_gpt":0.3638754436301231,"score_spread":0.2048149760289411,"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."}}