1,720,985 research outputs found
sj-docx-4-jicm-10.1177_08850666211064844 - Supplemental material for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)
No full textSupplemental material, sj-docx-4-jicm-10.1177_08850666211064844 for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma) by Vincent I. Lau, Alexandra Binnie, John Basmaji, Nadia Baig, Dawn Opgenorth, Saoirse Cameron, Katie O’Hearn, Ellen McDonald, Janek Senaratne, Wendy Sligl, Danny J. Zuege, Oleksa Rewa, Sean M. Bagshaw and Jennifer Tsang in Journal of Intensive Care Medicine</p
sj-docx-2-jicm-10.1177_08850666211064844 - Supplemental material for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)
No full textSupplemental material, sj-docx-2-jicm-10.1177_08850666211064844 for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma) by Vincent I. Lau, Alexandra Binnie, John Basmaji, Nadia Baig, Dawn Opgenorth, Saoirse Cameron, Katie O’Hearn, Ellen McDonald, Janek Senaratne, Wendy Sligl, Danny J. Zuege, Oleksa Rewa, Sean M. Bagshaw and Jennifer Tsang in Journal of Intensive Care Medicine</p
sj-doc-1-jicm-10.1177_08850666211064844 - Supplemental material for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)
No full textSupplemental material, sj-doc-1-jicm-10.1177_08850666211064844 for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma) by Vincent I. Lau, Alexandra Binnie, John Basmaji, Nadia Baig, Dawn Opgenorth, Saoirse Cameron, Katie O’Hearn, Ellen McDonald, Janek Senaratne, Wendy Sligl, Danny J. Zuege, Oleksa Rewa, Sean M. Bagshaw and Jennifer Tsang in Journal of Intensive Care Medicine</p
sj-docx-3-jicm-10.1177_08850666211064844 - Supplemental material for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)
No full textSupplemental material, sj-docx-3-jicm-10.1177_08850666211064844 for Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma) by Vincent I. Lau, Alexandra Binnie, John Basmaji, Nadia Baig, Dawn Opgenorth, Saoirse Cameron, Katie O’Hearn, Ellen McDonald, Janek Senaratne, Wendy Sligl, Danny J. Zuege, Oleksa Rewa, Sean M. Bagshaw and Jennifer Tsang in Journal of Intensive Care Medicine</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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