{"id":"W4393594013","doi":"10.5281/zenodo.1187175","title":"Dataset for: Developing research data management services and support for researchers: a mixed methods study","year":2018,"lang":"en","type":"dataset","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":"University of Toronto","funders":"","keywords":"Research data; Data science; Data management; Computer science; Data mining; Data curation","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","open_science"],"consensus_categories":[],"category_scores_codex":[0.01277283,0.0007790041,0.0009525779,0.002841644,0.001379516,0.002589162,0.002338187,0.001373253,0.1622314],"category_scores_gemma":[0.07408646,0.0008386312,0.0009700721,0.006721451,0.0005298962,0.001916244,0.003297586,0.001913659,0.07109174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002961925,"about_ca_system_score_gemma":0.006788894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007969603,"about_ca_topic_score_gemma":0.01732743,"domain_scores_codex":[0.9929966,0.002787607,0.001617986,0.0007914816,0.001371374,0.0004349458],"domain_scores_gemma":[0.9541156,0.02204165,0.003640703,0.008104851,0.01064969,0.00144754],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001215505,0.00003260293,0.001017331,0.001125298,0.00002627232,0.00001511285,0.0001685411,0.0001201934,0.00007732594,0.00119261,0.9882165,0.0078866],"study_design_scores_gemma":[0.0005390389,0.00003917022,0.006913588,0.001309605,0.00005277402,0.0000453729,0.0004581815,0.0002058669,0.0003393048,0.002746301,0.9873037,0.0000472029],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004247533,0.00005794107,0.001253517,0.0004514751,0.0000500288,0.000921344,0.9945508,0.0003510828,0.001939061],"genre_scores_gemma":[0.003553569,0.0001824475,0.01134477,0.0005425989,0.00004013867,0.02544921,0.9535232,0.0006932022,0.00467094],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9976618,"threshold_uncertainty_score":0.5427181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.359032753417205,"score_gpt":0.4715062778177422,"score_spread":0.1124735244005372,"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."}}