The National COVID Cohort Collaborative (N3C) Data Enclave is an analytics platform containing clinical data from electronic health records from over 70 sites around the country.  This data from SARS-CoV-2 positive patients (and matched controls) helps scientists further understand the disease, including potential risk factors, preventative factors and long-term health consequences. N3C code workbooks allow users to access, aggregate, visualize and analyze the available data for their approved project.  Coding in N3C requires that researchers learn novel software interfaces and workflows, and often even new languages that support distributed computation (i.e. PySpark or RSpark).  These challenges make it all the more critical for researchers to share new best-practices across the community and to create and share code templates.

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N3C Domain Teams enable all interested researchers with shared interests to analyze data within the N3C Data Enclave and collaborate more efficiently in a team science environment.  The N3C Logic Liaisons are a group of experienced Domain Team coders from across the country who have been actively mining the billions of rows of N3C data to extract relevant variables for their project analyses. The Logic Liaison Team is led by Johanna Loomba, Director of Informatics at the Integrated Translational Health Research Institute of Virginia (iTHRIV) CTSA and Stony Brook University's Richard Moffitt. Andrea Zhou, iTHRIV data scientist, is the principal template developer for the Logic Liaison team. This team developed a generic code workbooks with structure that is agnostic to a research domain and easy to reuse across data use requests (DURs). The workbooks was refined in the Logic Liaisons meetings where community best practice tips were added, and are now published for general use in N3C. (Instructions for access are provided below.)  “The Logic Liaisons have become critical to making the connection between clinician scientists, informaticians, and the massive dataset within N3C - they are at the heart of team science AND translational science.” - Melissa Haendel, co-founder of N3C

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The generic N3C code workbooks are helpful for new N3C research teams as they create a project-specific data frame of facts. The sample code automatically imports the appropriate level 3 (limited dataset) or level 2 (de-identified) patient data tables and generates commonly derived variables. The variables include patient BMI, death data, intermittent mandatory ventilation (IMV) and extracorporeal membrane oxygenation (ECMO) procedures, inpatient visits, and common comorbidities from the N3C Cohort Paper. Additional variables related to the DUR can be added by modifying this sample code (i.e. incorporating new clinical concept sets, adding new logic regarding relationships between concepts, etc.).