Effective research data management (RDM) is essential to maintaining the integrity, transparency, and accessibility of scholarly work. At Sacramento State, good data management practices not only help researchers meet the requirements of funding agencies but also ensure that their data can be easily discovered, accessed, shared, and reused by others.
The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—serve as the cornerstone of modern data management:
Findable: Well-managed data is easy to find by both humans and machines. This means data should be stored in repositories with clear, persistent identifiers (such as DOIs), and described using rich metadata so that it can be easily searched and located.
Accessible: Once found, data should be accessible under well-defined conditions, ensuring appropriate access controls. Data may be shared publicly or with certain restrictions, depending on ethical or legal requirements.
Interoperable: To be most useful, data needs to be interoperable across different platforms and systems. This involves using standardized formats, vocabularies, and ontologies to ensure that datasets can be integrated and understood by a wide variety of applications.
Reusable: Finally, data should be reusable for future research. This requires clear documentation, proper licensing, and sufficient detail so that others can confidently use the data in new contexts while ensuring attribution to the original source.
At Sacramento State, RDM is not just a compliance requirement—it’s a crucial aspect of advancing research impact, increasing efficiency, and promoting collaboration. By properly organizing, documenting, and preserving your data, you contribute to the overall transparency of science, facilitate reproducibility, and open new pathways for innovation.
Anonymization/De-identification
The process of removing or masking personal identifiers from datasets to protect the privacy of individuals, particularly in sensitive or human subjects research.
Controlled Access
A process where access to certain datasets is restricted, requiring approval or authorization before they can be used. This is common with sensitive or proprietary data.
Data Citation
The practice of formally referencing datasets in publications to give credit to data creators and enable others to locate and reuse the data. Data citations typically include author(s), title, version, repository, and DOI.
Data Life Cycle
The stages through which research data passes, from planning and collection to processing, analysis, preservation, and sharing. Each stage has specific best practices to ensure data integrity and usability over time.
Data Management Plan (DMP)
A formal document that outlines how data will be collected, organized, stored, and shared during and after a research project. It’s often required by funders like the NSF or NIH to ensure proper data stewardship.
Data Preservation
The long-term storage of research data in formats and locations that ensure it remains accessible and usable in the future, often via institutional or subject-specific repositories.
Data Security
Practices and technologies that protect data from unauthorized access, modification, or loss. It includes encryption, secure storage, and access control to ensure the confidentiality and integrity of data.
Data Stewardship
The responsible management and oversight of data throughout its lifecycle, ensuring that it is collected, stored, and shared in ways that uphold ethical, legal, and quality standards.
FAIR Principles
Guidelines for making data Findable, Accessible, Interoperable, and Reusable. These principles help ensure that research data can be discovered, accessed, and used by others efficiently.
Metadata
Descriptive information about data that explains its content, structure, creation process, and how it can be interpreted or used. Metadata is essential for making data findable and reusable.
Open Data
Data that is made freely available for anyone to access, use, and share, often with minimal restrictions. Open data promotes transparency and collaboration in research.
Persistent Identifier (PID)
A long-lasting reference to a document, dataset, or other digital object. Common PIDs include DOIs (Digital Object Identifiers), which help ensure that data can be reliably cited and accessed.
Repository
An online platform where research data is stored, preserved, and shared. Repositories can be subject-specific (e.g., ICPSR) or general (e.g., Zenodo, Dryad).
Sensitive Data
Data that includes personal, confidential, or proprietary information that requires special handling to protect privacy or comply with legal and ethical standards (e.g., health records, financial data).
Version Control
A method for tracking and managing changes to datasets over time. It is essential for maintaining data integrity and preventing confusion, especially in collaborative projects.
Research Data Management (RDM) by Mary-Kate Finnegan is marked with CC0 1.0 Universal 
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