jtleek/datasharing

Features

• Provenance Packaging - Provides a provenance-based packaging system that groups raw sources, processed datasets, and processing recipes together.
• Provenance Packaging Protocols - Establishes a protocol for packaging raw data and processing recipes to maintain a verifiable record of origins.
• Data Lifecycle Provenance - Maintains a complete history of data derivation by associating metadata with data structures to track their origin.
• Data Science and Research - Offers tools and guidelines for creating code books and reference files essential for scientific research data interpretation.
• Tidied Transaction Views - Arranges data with one variable per column and one observation per row using descriptive headers for interoperability.
• Data Standardization - Implements standards for structuring datasets into tidy formats with consistent variable encoding for interoperability.
• Data Processing - Transforms raw data into tidy formats using reproducible scripts to make the analysis process reproducible.
• Script-Based Transformations - Converts raw data into tidy formats using reproducible scripts to ensure consistent processing results.
• Data Processing Recipes - Creates script or pseudocode recipes that convert raw data into tidy datasets to ensure computational reproducibility.
• Data Provenance Frameworks - Implements a provenance protocol by packaging raw data, recipes, and code books to track data origins.
• Data Structures - Organizes data so each variable occupies one column and each observation occupies one row for easy merging.
• Raw Data Archiving - Retains original, unmodified source data to ensure provenance and allow independent verification of results.
• Research Data Sharing Frameworks - Provides a standardized framework for organizing raw data and tidy datasets to ensure computational reproducibility.
• Research Dataset Documentation - Provides frameworks and templates for creating detailed code books and reference files to ensure research transparency.
• Tidy Data Specifications - Implements a structural standard for organizing datasets with one variable per column and one observation per row.
• Tidy Data Structuring - Implements tidy data specifications where each variable occupies one column and each observation occupies one row.
• Data Delivery Packaging - Organizes raw data, tidy datasets, code books, and processing recipes into a standardized package to ensure data provenance.
• Research Variable Standards - Defines a methodology for documenting variable context and measurement units within standardized reference files.
• Raw Source Preservation - Ensures original unmodified source files are preserved to allow independent verification of all data transformation steps.
• Storage Immutability - Implements an architectural pattern where source data is treated as fixed to ensure reproducibility.
• Reproducible Data Transformations - Transforms raw datasets into tidy formats using documented recipes to ensure analysis can be repeated.
• Workflow Reproducibility - Ensures consistent results through standardized script sequences for transforming raw data into tidy formats.
• Categorical Value Encoding - Implements standardized text-based encoding for categorical and missing values to prevent data interpretation errors.
• Categorical Variable Encoding - Standardizes the representation of categorical values and missing data markers to prevent errors during analysis.
• Research Delivery Packaging - Provides a delivery format that preserves original sources and analysis context for clinical research.
• Code Book Specifications - Provides a framework for creating detailed code books that define variable units and experimental designs.
• Data Cleaning Pipelines - Provides reproducible workflows for transforming raw source data into standardized, model-ready formats using scripts.
• Dataset Metadata Mapping - Decouples metadata from datasets by storing variable units and experimental design details in separate reference files.
• Variable Context Documentation - Provides a system for documenting variable units and experimental designs in reference files to provide necessary analysis context.
• Databases and Analytics - Guide for sharing research data.
• Data Science Foundations - Guidelines for sharing data and code for reproducible research.
• Educational Resources - Best practices for sharing data and research findings.

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