Data is considered reusable if it is tagged with clear metadata and stored in a way that is accessible and traceable—in accordance with the FAIR principles.
Making research data accessible
How to Find, Understand, and Reuse Data More Quickly
Data Access as a Key Success Factor
How can research data be efficiently analyzed and reused?
Research data continues to grow, often over decades and across multiple projects.
Why the Analysis of Research Data Is Crucial for Science
Without clear structures, metadata, and intelligent search mechanisms, valuable datasets remain untapped. With modern archiving and analysis tools, research data can be quickly located, compared, and reused. This promotes scientific efficiency, quality, and traceability.
Reusing existing data not only saves time and money but also enables entirely new research approaches.
Did you know?
- Up to 80% of all research data remains unused because it is difficult to find.
- Metadata is the key to reusability - it makes content searchable and contextually usable.
- Funding programs such as Horizon Europe require “FAIR Data,” meaning findable, accessible, interoperable, and reusable.
- Automated data analysis enables new scientific insights from existing archives.
- Good data management significantly increases the visibility and reputation of research institutions.
Data overload, siloed systems, and a lack of structure
The Biggest Challenges in Managing Research Data
The growing volume and diversity of scientific data present organizations with the challenge of managing information in a structured, discoverable, and legally compliant manner—especially given distributed systems, missing metadata, and increasing compliance requirements.
Data silos
Information is stored in different systems or drives.
Missing metadata
Without context, searching or analysis is virtually impossible.
Variety of formats
Different file types make integration and analysis difficult.
Compliance Requirements
Funding agencies and laws require structured data management.
Your questions - clearly answered
FAQ: Everything You Need to Know
Here you’ll find answers to the most frequently asked questions about our solutions, products, and applications - from security and archiving to data management.
What does “reusability” of research data mean?
What types of data can be analyzed?
MetadataHub supports common file types (text/documents, images, video, audio) as well as domain-specific data from microscopy and life sciences, sensor technology and experiments, medical imaging, geodata and remote sensing, and industry and engineering.
How does MetadataHub help with data searches?
Using automatic indexing and metadata analysis, relevant data records can be searched by project, parameter, or content.
Does the solution support open science initiatives?
Yes. MetadataHub meets the FAIR data requirements and supports traceability for funding agencies and peer review processes.
Can existing archives be integrated?
Yes. MetadataHub integrates with existing archive and storage systems without requiring any data to be moved.
How exactly do researchers benefit?
You'll save time searching for data, gain new insights through analysis, and be able to efficiently reuse existing results.
