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Research Data Management: Research Data Management

This library guide outlines the basics of data management and creating a data management plan.

What is Reseach Data Management (RDM)?

Research Data Management (RDM) is the systematic process of organizing, storing, preserving, and sharing data generated during a research project. It encompasses all stages of the data lifecycle, from planning and collecting data to storing, analyzing, describing, archiving, and reusing it. Effective RDM ensures that data is accessible, accurate, and reusable, enhancing research transparency and reproducibility. It also helps researchers comply with funder requirements and ethical standards, avoid data loss, and facilitate collaboration. The approach to managing data depends on the types of data involved, the methods of collection and storage, and the intended use throughout the research lifecycle. What is Research Data Management | Data Management

 

Benefits of Research Data Management

- Meets funders' requirements, such as those from the NRF,

- Adhere to publishers' requirements

- Ensures the integrity and reproducibility of research

- Guarantees that data and records are accurate, complete, authentic, and reliable

- Effective data management enhances research efficiency

- Saves time and resources in the long run

- Improves data security, minimising the risk of data loss

- Prevents duplication by enabling others to use your research data

- Aligns with industry and commercial practices

- Protects your institution from reputational, financial, and legal risks.

Research Data

Research data may be broadly described as "... data that is collected, observed, or created, for purposes of analysis to produce original research results."  (What is "Research Data")

Research data may be generated for different purposes and through different processes and may be divided into the following categories.  Each category may require a different type of data management plan. 

Observational 

  • captured in real-time
  • usually irreplaceable
  • examples include: sensor readings, survey results, telemetry, sample data, neurological images

Experimental

  • data from lab equipment
  • often reproducible (this can be expensive)
  • examples include: gene sequences, magnetic field readings

Simulation

  • data generated from test models
  • models and metadata where the input is more important than the output data
  • examples include: climate models, economic models.

Derived or compiled

  • reproducible (expensive)
  • examples include: text and data mining, 3D models

Reference or Canonical

  • a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets
  • most probably published or curated. 
  • examples include: gene sequence databanks, chemical structures, spatial data portals. 

These data can come in many forms such as, text, numerical, multimedia, models, software, discipline specific (i.e., FITS in astronomy, CIF in chemistry), or instrument specific.

Sourced from:  Boston University Libraries - Research Data Management 

Research Data Management Policy

The purpose of the Research Data Management Policy is to ensure that research data is stored, preserved, retained, made accessible for use and reuse, and/or disposed of according to legal, statutory, ethical and funding bodies’ requirements. This policy seeks to ensure consistent research practice related to data management principles that support effective data sharing, open access, and for data to be discoverable, accessible, reusable and interoperable according to quality standards.