Graphical Workflow for Reproducible Data Analysis
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Solution Overview
Problem
The reproducibility crisis in scientific research, particularly in computational research activities, is exacerbated by the lack of standardized methods for data and code annotation, unavailability of data and code for reanalysis, and the complexity of bioinformatic workflows.
Innovation Solution
The development of systems and methods that utilize standardized constructs to represent data and operations, enabling visual and interactive graphical workflows. These workflows can be shared and authenticated across different research environments, simplifying data analysis and ensuring reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standardized constructs and graphical workflows are used to represent data analysis, then reproducibility and ease of operation are improved, but device complexity increases
Solution Approach 1:
The system segments the data analysis workflow into discrete, standardized constructs (data containers, transformations, operations) that can be independently defined, combined, and executed. This segmentation enables reproducible workflows by breaking down complex analyses into modular units with consistent interfaces and data formats.
Solution Approach 2:
The system creates executable representations of analytical workflows that can be copied, shared, and executed across different computing environments. The workflow definitions serve as templates that can be instantiated and reproduced without requiring manual recoding, ensuring consistency and reproducibility.
2Ease of operation
If graphical workflows with standardized constructs are implemented, then ease of operation and data discovery are improved, but device complexity increases
Solution Approach 1:
The system introduces standardized data containers and workflow definitions as intermediary layers between the user and the underlying computational complexity. These intermediaries provide user-friendly graphical interfaces and standardized interfaces that abstract away the complexity of data processing operations while maintaining computational power.
Solution Approach 2:
The standardized constructs serve multiple functions: they represent data structures, define operations, specify workflows, and enable execution across different platforms. This multi-functionality reduces the need for multiple separate tools and simplifies operation while managing system complexity through unified interfaces.
3Measurement precision
If comprehensive data and code annotation is performed, then measurement precision and reliability are improved, but loss of time increases
Solution Approach 1:
The system performs data annotation and workflow definition as preliminary actions during the workflow creation phase, rather than during execution. By preparing and validating annotations upfront, the system ensures measurement precision while avoiding time-consuming annotations during actual data processing.
Solution Approach 2:
The system enables self-service annotation where workflow definitions and data containers automatically generate and maintain their own metadata and documentation. This reduces the manual time required for annotation while maintaining comprehensive and accurate measurements through automated processes.
Data Source
AI summary
Aspects of the present invention include methods of representing data analysis comprising: selecting input data comprising standardized data containers, based on a first input from a user, and arranging on a graphical interface graphical icons into a graphical workflow representing a data analysis, based on a second input from the user, wherein the workflow comprises a plurality of standardized data transformations and standardized data containers comprising intermediate and final results of the data analysis. Also provided are systems for performing the methods described herein as well as non-transitory computer readable storage mediums.