# research data management

> activities around the life cycle of research-related data

**Wikidata**: [Q30089794](https://www.wikidata.org/wiki/Q30089794)  
**Source**: https://4ort.xyz/entity/research-data-management

## Summary
Research data management (RDM) encompasses the activities and processes involved in managing research-related data throughout its entire lifecycle. It is a discipline focused on ensuring data is findable, accessible, interoperable, and reusable (FAIR) for researchers and institutions. RDM includes planning, organizing, documenting, storing, preserving, and sharing research data.

## Key Facts
- RDM is a subclass of data management, focusing specifically on research-related data
- The term has aliases in multiple languages including "RDM," "traitement des données de recherche," and "FDM"
- RDM is described in the Open Science Thesaurus with a reference date of October 23, 2021
- The concept is documented in Wikipedia in German and Turkish languages
- RDM has a Google Knowledge Graph ID of /g/11c2l4qqrh
- The field includes specialized initiatives like NFDI4BIOIMAGE and NFDI4Immuno, both founded on March 1, 2023
- RDM professionals include data stewards, programmers, and researchers like Cord Wiljes
- Related tools include RSpace (electronic lab notebook), SaQC (quality control software), and spatial.IO (geospatial data management)

## FAQs
### Q: What is the main goal of research data management?
A: The main goal of research data management is to ensure research data is properly organized, documented, preserved, and made accessible throughout its lifecycle, enabling reproducibility and reuse of research findings.

### Q: Who needs to implement research data management?
A: Researchers, research institutions, universities, libraries, and data stewards all need to implement research data management to comply with funding requirements, ensure data integrity, and facilitate collaboration.

### Q: What are the key principles of research data management?
A: The key principles of research data management are the FAIR principles: Findable, Accessible, Interoperable, and Reusable, which guide how research data should be handled and shared.

## Why It Matters
Research data management is critical in modern scientific research because it addresses the growing complexity and volume of research data. Without proper RDM, valuable research data can be lost, misinterpreted, or become inaccessible, undermining the reproducibility and credibility of scientific findings. RDM ensures that research data remains usable long after projects end, supports compliance with funding agency requirements, and enables new discoveries through data sharing and reuse. It also helps institutions manage their data assets effectively, reduces duplication of effort, and supports open science initiatives. As research becomes increasingly collaborative and data-intensive, RDM provides the infrastructure and practices necessary to maintain data quality, security, and accessibility across disciplines and institutions.

## Notable For
- Establishes FAIR principles as the foundation for modern research data practices
- Supports reproducibility and transparency in scientific research
- Enables data sharing and reuse across institutions and disciplines
- Addresses growing regulatory and funding requirements for data management
- Provides specialized tools and platforms for different research domains

## Body
### Core Components of RDM
Research data management involves multiple interconnected activities throughout the data lifecycle. Planning occurs before data collection, establishing protocols for data organization, storage, and sharing. Documentation ensures data is properly described with metadata and contextual information. Storage and backup procedures protect data from loss or corruption. Preservation strategies maintain data accessibility over time, while sharing mechanisms enable controlled access to research data.

### Specialized RDM Initiatives
The National Research Data Infrastructure (NFDI) network includes specialized initiatives like NFDI4BIOIMAGE for microscopy and bioimage analysis, and NFDI4Immuno for immunology research. These initiatives, both launched March 1, 2023, demonstrate how RDM adapts to specific research domains with tailored infrastructure and practices.

### RDM Tools and Platforms
Various tools support RDM activities across different research contexts. RSpace provides an integrated platform combining electronic laboratory notebooks with sample management and FAIR data principles. SaQC offers specialized quality control for time series data, while spatial.IO delivers cloud-ready geospatial data management capabilities. These tools address specific RDM needs within their respective domains.

### Professional Roles in RDM
RDM involves multiple professional roles including data stewards who manage institutional data policies, programmers who develop RDM tools and infrastructure, and researchers who implement RDM practices in their work. Professionals like Cord Wiljes exemplify the interdisciplinary nature of RDM, combining expertise in data management, programming, and research.

## Schema Markup
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## References

1. [Source](http://data.loterre.fr/ark:/67375/TSO-GDCXNW8F-5)