Data Management & Policy Guidelines

Research Data Repository

UiTM Research Data Repository serves as a centralized platform dedicated to storing, managing, and disseminating research data produced by the faculty, staff, and students of Universiti Teknologi MARA (UiTM). As a leading institution in Malaysia, UiTM recognizes the critical role that research data plays in advancing knowledge across various disciplines. The repository ensures the preservation of valuable research outputs and promotes transparency, reproducibility, and collaboration within the academic community.

UiTM Research Data Repository facilitates interdisciplinary research endeavours, fostering innovation by providing access to diverse datasets spanning a wide range of fields. It contributes to the collective body of scholarly knowledge. With its user-friendly interface and robust infrastructure, the repository is vital for researchers, policymakers, and other stakeholders seeking reliable and accessible data for their endeavours.

It is universally acknowledged that researchers are interested in data of all kinds, regardless of origin or type. Here are recognized definitions of research data from global institutions:

"Research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results."

"Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media."

"The recorded information (regardless of the form or the media in which they may exist) necessary to support or validate a research project’s observations, findings or outputs."

Research data represents significant value to researchers and the University. Good stewardship of research data is necessary to validate outcomes and maintain the integrity of research results.

Integrity & Reproducibility

Ensuring research integrity and full reproducibility of published outcomes.

Research Efficiency

Increasing overall workflow and data retrieval efficiency across projects.

Authenticity & Accuracy

Ensuring research data and records are accurate, complete, and authentic.

Time & Resource Savings

Saving valuable time, effort, and institutional resources in the long run.

Data Security

Enhancing protection standards and minimizing the risk of accidental loss.

Prevent Duplication

Preventing redundant effort by enabling peers to re-use structured datasets.

Industry Standards

Complying with best practices conducted across modern industry and commerce.

Longitudinal Analysis

Facilitating temporal trend comparisons by establishing data baselines.

Research Data Management (RDM) is defined as:

"how you look after your data throughout your project. It covers the planning, collecting, organising, managing, storage, security, backing up, preserving, and sharing your data and ensures that research data are managed according to legal, statutory, ethical and funding body requirements"

Whyte, A. & Tedds, J. (2011)

RDM occurs at every stage of the research lifecycle—not just at the conclusion where data files are prepared for final archiving.

Research Data Planning Lifecycle

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