Glossary Term

Research Data Management

Research Data Management (RDM) includes the planning and processes by which research data is organized, collected, stored, shared, and preserved by researchers.

Main Definition

Research Data Management (RDM) Includes the planning and processes by which research data is organized, collected, stored, shared, and preserved by researchers.  Good RDM ensures data remains findable and usable long after a project or grant ends, supports compliance with funder mandates, and prevents costly loss of institutional knowledge. For technology executives at universities and research organizations, RDM is both a compliance obligation and a strategic asset.  It directly affects grant competitiveness, since many funders require documented data management plans.

Storage optimization is a good example of the many challenges in RDM. It is too expensive for research organizations to keep all research data on hot, active storage. However, moving the data to less expensive storage is not straightforward. The movement task often has a high learning curve for the researchers to do it themselves. Automation may not provide a solution since it requires a clear definition of cold data.  To further complicate things, the scientific metadata is often kept separately in paper notebooks, away from the actual files. 

Modern solutions give researchers increasing control over their own data, allowing them to add file metadata via tags, archive, delete and restore data according to their specific research requirements.  They provide both infrastructure leaders and researchers with the tools needed to execute data management plans and manage costs. 

Starfish Storage provides a data management platform that both researchers and infrastructure leaders can benefit from. Researchers can associate the files with all the scientific metadata, both automatically extracting it and manually entering it by applying tags. This allows for all the information to be in one place and searchable. Armed with information, researchers can  categorize their data as hot or cold  with corresponding tags. This shows storage admins (or storage automations) which data is truly cold and can be moved to appropriate storage.

Related Links

Recent Posts

From Unsearchable Archive to Self-Service Knowledge Platform: How ASU Transformed 20 Years of Data

June 4, 2026

Starfish Storage Wins 2026 Bio-IT World Innovative Practices Award, Showcases Life Sciences Use Case at Conference

May 6, 2026

Starfish Storage Wins “Data Solution of the Year for Research” in 2026 Data Breakthrough Awards Program

April 16, 2026

Upcoming Events

Date
July 26, 2026 - July 30, 2026
21-things-banner-600x600