Data Management System for Research Data Sharing
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Solution Overview
Problem
Research data obtained during experiments is often managed individually by researchers, making it difficult for others to access and utilize, hindering data sharing and collaboration.
Innovation Solution
A data management system that organizes measurement data by themes, allowing access control and status management, enabling efficient data sharing and collaboration among researchers through a server device that classifies and displays data using metadata, chapters, and user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measurement data is managed individually by researchers, then data security and access control are maintained, but data sharing and collaboration are hindered
Solution Approach 1:
The system segments data management into hierarchical levels: individual researcher data spaces, project-level data collections, and organization-wide data repositories. Each level has its own access control policies, allowing data to be shared appropriately while maintaining security boundaries. This resolves the contradiction by enabling controlled sharing without compromising access control reliability.
Solution Approach 2:
The patent introduces a data management server as an intermediary between individual researchers and data users. This server mediates access requests, enforces authorization policies, and facilitates data sharing without requiring direct access between individual researchers. The intermediary enables collaboration while maintaining security through centralized policy enforcement.
2Productivity
If data is organized by detailed classification, then data retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The data classification system is segmented into multiple hierarchical levels with different granularity. Top-level classifications provide broad categories for efficient navigation, while detailed classifications are only applied where needed. This multi-level segmentation enables efficient data retrieval without requiring complete detailed classification of all data, thus reducing system complexity.
Solution Approach 2:
Different classification strategies are applied to different data types and contexts based on local requirements. High-traffic data areas receive more detailed classification for efficient retrieval, while less-accessed data uses simpler classification. This local differentiation optimizes retrieval efficiency where needed while minimizing overall system complexity.
Data Source
AI summary
A data management system includes: an input control unit (110) that adds measurement data uploaded by designating a theme that is a unit of access control and acquired by a measurement system, to a dataset classified based on metadata included in the measurement data, within the theme; a management control unit (120) that associates the dataset with a chapter that is a unit of status management and is provided within the theme; and a display control unit (130) that displays information managed by the data management system.