{"id":"W6949408907","doi":"10.5281/zenodo.1745373","title":"Operationalizing and evaluating the FAIRness concept for a good quality of data sharing in Research: the RDA-SHARC-IG (SHAring Rewards and Credit Interest Group","year":2018,"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":"McGill University","funders":"","keywords":"Data sharing; Operationalization; Interoperability; Process (computing); Data quality; Quality (philosophy); Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.02513705,0.0001235668,0.0001587912,0.0002490244,0.002741782,0.007695206,0.008103328,0.00003849838,0.0001770668],"category_scores_gemma":[0.01141707,0.0000923196,0.00001883751,0.0009210777,0.0007278211,0.009534475,0.02589148,0.0004397173,0.00003529028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000832331,"about_ca_system_score_gemma":0.0000128614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002321484,"about_ca_topic_score_gemma":0.00004678181,"domain_scores_codex":[0.9958454,0.001466066,0.0004565709,0.0009824135,0.0007892787,0.0004603065],"domain_scores_gemma":[0.9958333,0.0008306784,0.0001976718,0.002113293,0.0009226612,0.0001024247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001784635,0.0001276601,0.0002042492,0.0002838163,0.00008846929,0.000004266316,0.006990334,0.00002832824,0.005675365,0.8570938,0.006235126,0.1230902],"study_design_scores_gemma":[0.002878462,0.001894118,0.02039531,0.0004799504,0.00003123909,0.00007422249,0.008064409,0.4371823,0.0007278436,0.01469943,0.512996,0.0005767122],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2959212,0.001161394,0.6490114,0.02454025,0.0003159142,0.005924857,0.0007328321,0.0005476681,0.02184443],"genre_scores_gemma":[0.9940704,0.0001863983,0.004426986,0.0001107312,0.0002160557,0.000001086825,0.0004529686,0.0002240099,0.0003113451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8423943,"threshold_uncertainty_score":0.9985565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6546675037793055,"score_gpt":0.4858193358773532,"score_spread":0.1688481679019523,"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."}}