Research Data Management and Sharing

Research data is the foundation of scholarly inquiry. It includes any information collected, observed, generated, or created to validate research findings. Depending on the discipline, research data can take many forms—numerical values, images, videos, code, survey responses, transcripts, lab notebooks, models, or physical specimens.

Research Data Management (RDM) refers to the practices and decisions you make throughout the research lifecycle to organize, document, store, preserve, and share your data. RDM begins at project planning and extends through long-term preservation and reuse, serving as the foundation for reproducibility and replicability in science.

Below is a Research Data Lifycycle diagram developed by a working group at Harvard that illustrates the major stages every research project (especially in biomedical fields) typically goes through—from planning all the way to reuse.

1. Plan & Design – decisions made at the start about how data will be collected, what metadata to capture, the file structure, naming conventions, and compliance or policy issues.

2. Collect & Create – gathering or generating the data, using tools or software that support reproducibility, with clear documentation. 

3. Analyze & Collaborate – processing and analyzing data, version control, collaboration across teams, ensuring data remain usable.

4. Evaluate & Archive – assessing what data need to be kept, ensuring secure storage, planning for long-term retention and eventual archiving. 

5. Share & Disseminate – making data available to others through repositories, using data use agreements, ensuring open access where possible. 

6. Publish & Reuse – the final stage where research outputs are published; data are reused by others for new analyses, replication, or secondary research. 

Good data management and sharing practices are more than compliance requirements—they benefit you, your collaborators, and the wider scholarly community. By managing data thoughtfully, you protect your research, meet funder and publisher expectations, and contribute to a more open and trustworthy scholarly ecosystem.

Benefits for Researchers:

• Efficiency: Well-organized data saves time when analyzing, reusing, or revisiting datasets.

• Reproducibility & Credibility: Documented and structured data strengthens transparency, helping others validate and trust your findings.

• Citations & Impact: Studies show that publications with openly available data receive more citations.

• Funding & Compliance: Many funders, such as NIH and NSF, require data management and sharing plans.

• Data Security: Good practices reduce the risk of loss, corruption, or misuse.

Benefits for the Research Community:

• Advancing Knowledge: Shared data enables replication and secondary analysis.

• Reducing Redundancy: Openly available data reduces duplication of effort.

• Fostering Collaboration: Accessible datasets make interdisciplinary and cross-institutional projects easier.

Benefits for Society:

• Public Trust: Transparent data practices increase public confidence in science.

• Innovation: Shared datasets fuel discoveries, policy development, and real-world applications.