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Data Management and Access Control in Soil Health Research Projects

This document outlines the hierarchy and access control protocols for managing files and data within Soil Health research projects conducted by the project team members, including Group Leaders and Members. It details the three levels of access control (Full Control, Modify, Read, No Access) assigned to various roles. It also covers the disciplined use of spreadsheets and databases such as MS Excel and MS Access for research data management, along with protocols for data quality, analysis, and publication standards. Essential tools for transforming, visualizing, and managing research data, including R-statistics, Genstat, and CropStat, are discussed.

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Data Management and Access Control in Soil Health Research Projects

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  1. General Data and Information Management

  2. File Management

  3. OU File Repository: Hierarchy and Access Control OU Repository Group Leader Project Group Member V: ACastaneda K6K7 BBouman CKreye SoilHealth TCorrea RBuresh LTFE Three levels of access control

  4. Full control Modify Read No access Role: Group Leader (RBuresh) V: ACastaneda K6K7 BBouman CKreye SoilHealth TCorrea RBuresh LTFE

  5. Full control Modify Read No access Role: Group Member (ACastaneda) V: ACastaneda K6K7 BBouman CKreye SoilHealth TCorrea RBuresh LTFE

  6. Project Branch Structure Protocols, procedures, definitions, standards Documentation K6K7 MS Access database with tables and queries Database Data quality reports, procedures, standards Data_quality R / SAS scripts and outputs Data_analysis Raw data in Excel spreadsheets Excel_IN Derived data in Excel spreadsheets Excel_OUT Scientific publications, reports Publications

  7. Viewing and Managing Permissions in Windows Explorer

  8. Lack of Permission

  9. Synchronizing Directories and Files

  10. Backing Up by Group, Member, Project

  11. Managing Repository Disk Space

  12. Data Management

  13. General Data Pipeline • Disciplined use of spreadsheets (MS Excel) for research data capture • Minimal use of databases (MS Access) for research data management • Use of R-statistics, Genstat, CropStat for research data analysis and visualization

  14. Research Data Pipeline MS Excel MS Access MS Excel raw data copy & paste copy & paste link SQL queries processed data R-statistics GenStat CropStat export link

  15. Structured Data in a Spreadsheet

  16. Structured Data in a Database

  17. Transforming, Reshaping and Selecting Data with SQL

  18. Transforming, Reshaping and Selecting Data with SQL

  19. Visualizing and Analyzing Data with R-statistics

  20. Course Materials

  21. Cookbook and Recipes

  22. Cookbook and Recipes

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