{"id":"W6931465724","doi":"10.5281/zenodo.6473034","title":"Practicing Open Data Governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Open data; Open science; Stewardship (theology); Citizen science; Corporate governance; Open source","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.05257863,0.0008022548,0.0006911169,0.004801397,0.02693058,0.02481707,0.004407824,0.004027008,0.02101642],"category_scores_gemma":[0.1103042,0.001217317,0.000700593,0.01088409,0.01669326,0.01592621,0.01652344,0.007011371,0.005906461],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07306349,"about_ca_system_score_gemma":0.3746027,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.880854,"about_ca_topic_score_gemma":0.9301744,"domain_scores_codex":[0.9162085,0.01240163,0.002641405,0.006262058,0.05195744,0.010529],"domain_scores_gemma":[0.7394311,0.02682832,0.01022688,0.02677044,0.1311101,0.06563314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000846957,0.0001421127,0.01069304,0.0002609334,0.00005983551,0.0004373047,0.02336681,0.0007600401,0.00284434,0.1371819,0.574655,0.249514],"study_design_scores_gemma":[0.0000106279,0.00001908854,0.005558856,0.0002753485,0.00001171949,0.0001237646,0.009689036,0.0008487349,0.0009054143,0.0153516,0.9670805,0.0001253748],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03809216,0.005535956,0.08264026,0.4740061,0.003869363,0.001175303,0.003138478,0.005682567,0.3858597],"genre_scores_gemma":[0.4848541,0.01316577,0.135153,0.05800961,0.001523708,0.0006202223,0.003609041,0.007192458,0.295872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9955922,"threshold_uncertainty_score":0.5301152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2268128249131546,"score_gpt":0.3433129699670948,"score_spread":0.1165001450539402,"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."}}