{"id":"W2145874439","doi":"10.1080/03632415.2013.757975","title":"How to Manage Data to Enhance Their Potential for Synthesis, Preservation, Sharing, and Reuse—A Great Lakes Case Study","year":2013,"lang":"en","type":"article","venue":"Fisheries","topic":"Research Data Management Practices","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Fisheries and Oceans Canada","funders":"Environment Canada; Department of Forestry and Natural Resources, Purdue University; National Oceanic and Atmospheric Administration; National Institutes of Health; Great Lakes Fishery Trust","keywords":"Computer science; Documentation; Data management; Data sharing; Data curation; Data science; Data collection; Process (computing); Reuse; Relational database; Data management plan; Data dictionary; Metadata; Database; World Wide Web; Engineering","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.0346217,0.0004718133,0.0003782648,0.003571369,0.005655086,0.01031642,0.002967966,0.002317955,0.002766766],"category_scores_gemma":[0.03827117,0.000642865,0.0009456465,0.006860207,0.004633382,0.01232646,0.007212181,0.001608434,0.0006369973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00526193,"about_ca_system_score_gemma":0.008996318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04066955,"about_ca_topic_score_gemma":0.05656771,"domain_scores_codex":[0.9730393,0.01950699,0.001742118,0.001059187,0.003713718,0.000938701],"domain_scores_gemma":[0.9565041,0.02442911,0.002175594,0.008501585,0.007119345,0.001270335],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003260113,0.0007225713,0.08471054,0.002116611,0.0003040273,0.01056243,0.1475873,0.01351213,0.01239868,0.1613256,0.05197893,0.5144552],"study_design_scores_gemma":[0.0002112974,0.0006051242,0.02632264,0.002234436,0.0002817197,0.005286632,0.1107875,0.0259599,0.01904969,0.06962839,0.7392932,0.0003392829],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5218064,0.004978157,0.2720285,0.1108608,0.0004775342,0.002996365,0.002241748,0.001805342,0.08280507],"genre_scores_gemma":[0.5205926,0.001979337,0.4615744,0.001724968,0.0001846157,0.0008807934,0.001114068,0.0003793375,0.01156993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.997032,"threshold_uncertainty_score":0.1830993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1380317445195678,"score_gpt":0.3470446287243834,"score_spread":0.2090128842048156,"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."}}