{"id":"W2620672465","doi":"10.1016/j.dib.2017.05.051","title":"Enhancing data visualisation to capture the simulator sickness phenomenon: On the usefulness of radar charts","year":2017,"lang":"en","type":"article","venue":"Data in Brief","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Essilor (Canada); Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simulator sickness; Motion sickness; Radar chart; Visualization; Computer science; Chart; Class (philosophy); Radar; Simulation; Psychology; Virtual reality; Human–computer interaction; Data mining; Artificial intelligence; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.008642935,0.000590135,0.0002123436,0.002344267,0.0004599493,0.002603424,0.0006833584,0.000840631,0.00359117],"category_scores_gemma":[0.04415889,0.0002387447,0.000404045,0.001209859,0.0008921055,0.002129648,0.001953302,0.0006321381,0.000702142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005354809,"about_ca_system_score_gemma":0.0006813331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001749887,"about_ca_topic_score_gemma":0.002750065,"domain_scores_codex":[0.9951456,0.003473165,0.0001380027,0.000201757,0.0009446252,0.00009681979],"domain_scores_gemma":[0.9688696,0.02523368,0.00108288,0.00116873,0.003341927,0.0003030573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007448226,0.0001562363,0.01949873,0.001488393,0.00006635531,0.0002416824,0.008790391,0.001259218,0.009284423,0.007057369,0.0159683,0.9354442],"study_design_scores_gemma":[0.0005926209,0.005970499,0.3386263,0.01109368,0.0008371915,0.006346834,0.02935493,0.08093206,0.04620403,0.02425053,0.4546693,0.001122079],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4172958,0.02972008,0.4720522,0.01564026,0.001407525,0.002536758,0.002155221,0.005687506,0.05350467],"genre_scores_gemma":[0.6162396,0.00981677,0.3670694,0.001123831,0.0004208326,0.0006994549,0.0005023299,0.0002549148,0.00387285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008642935,"threshold_uncertainty_score":0.04570878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567846482339614,"score_gpt":0.3522710851157832,"score_spread":0.1954864368818218,"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."}}