Data Management

Data Governance: The overall management of the availability, usability, integrity, and security of the data.

Data Acquisition: The process of collecting, preparing, and ingesting data from various sources.

Data Storage: The management of available and potential storage resources for data and metadata.

Data Processing: The transformation of data into valuable insights and analysis through various techniques, such as data mining, cleansing, or normalization.

Data Analysis: The exploration and dissemination of data models and results to stakeholders for informed decision-making.

Data Quality: The assessment of data accuracy, consistency, completeness, and validity.

Data Privacy: The protection of personal or confidential information from unauthorized access or disclosure.

Data Security: The protection of data from unauthorized access, misuse, modification, or destruction.

Metadata Management: The management of information that describes data characteristics, content, structure, and context.

Data Sharing: The distribution of data to authorized users or stakeholders for collaborative purposes.

Data Archiving: The process of preserving and managing data for long-term access and retrieval.

Data Preservation: The act of maintaining the usability and accessibility of data over time, despite technological advances or system failures.

Digital Curation: The management of digital assets, such as files or artifacts, to ensure their usability, authenticity, and integrity.

Open Science: The movement towards enhancing transparency, collaboration, and accessibility in scholarly research.

Institutional Repositories: Digital archives containing scholarly or intellectual content from an academic or research institution.

Data Management Plans: A written document outlining the data management practices and protocols for a research project or collective.

Research Data Management Policies: Institutional or organizational mandates or guidelines for research data management.

Data Management Best Practices: Industry standards, guidelines, or recommendations for effective data management.

Metadata Management: Managing descriptive information about data assets, including information about their creation, authorship, ownership, and usage.

Data Acquisition Management: Managing the acquisition of data through various methods, such as surveys, experiments, simulations, and observational studies.

Data Quality Management: Ensuring that data is accurate, complete, and reliable by establishing quality control measures, data validation, cleaning, and audit trails.

Data Storage Management: Organizing and managing data storage infrastructure to ensure data security, backup, and retrieval.

Data Access Management: Managing who can access, share, and use data and under what conditions, by implementing access controls, licenses, agreements or permissions.

Data Preservation Management: Ensuring the long-term preservation and accessibility of data by implementing preservation strategies, such as data archiving, migration, and replication, according to recognized standards and guidelines.

Data Sharing Management: Making data available to others through data sharing platforms and repositories and supporting open access to scientific data to facilitate collaboration and reuse.

Data Citation Management: Citing data sources and associated metadata in scholarly publications to provide proper attribution and enable data discovery and reuse.

Data Analysis Management: Managing the processing and analysis of data, including data cleaning and transformation, statistical analysis, data mining, and data visualization.

Data Security Management: Managing the confidentiality, integrity, and availability of data by implementing security measures such as encryption, access controls, and backups.