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Research Data Management Guide

Research Data Management Guide


Research Data Management Overview

Research data management is a challenging topic, but if handled effectively from the beginning, it might save you a lot of time and bother when it comes time to write up your thesis or prepare your data for publishing.


Research data can be found in a variety of formats, including measurements, numbers, photographs, documents, and publications. The tools on these pages were created to assist you in organizing, creating, sharing, and maintaining your research materials.


Further readings:


Research Data


Data can be described more broadly in the context of the research from an information science perspective as any information that has been gathered, observed, generated, or developed to validate initial research findings. Research data can also be found in non-digital formats like lab notebooks and sketchbooks, despite being mostly digital.


Data produced by non-research activities such as university administration or teaching, as well as accidental or administrative data created during personal activities, desktop or mailbox backups, are not considered research data. The utilization of the research data can be utilized to define it. Depending on whether the information is being used as a key component of a research activity, the same information, for example, may be research data for one researcher but not for another.


Some benefits of managing research data are:

  • Locate and comprehend facts when required
  • Maintaining the project despite changes in researchers or staff
  • Organized data saves time
  • Reduces the danger of data being lost, stolen, or improperly used
  • Comply with funder and journal requirements
  • Facilitates easier results validation
  • Data can be shared, leading to collaboration and greater impact

Additional resources and information about research data:



Data Management Plan


Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This can save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It can increase the research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future.


Data management plans should be updated as the project moves forward or if there are any substantial deviations from the original project plan because they are meant to be working documents.


Data preservation and archiving is a part of a data management strategy. By selecting an archive in advance, the data collector can format data as it is collected to make submitting it to a database simpler in the future.


Example Data Management Plan;


  1. Storage and Backup


Saving data means keeping track of research materials so that you or others can access and utilize them in the future. Here are three things to think about before preserving your data.


  1. i) Location: When you can, make numerous copies of your data and store them on various types of media. Although the dependability of hard drives, cloud storage, and other choices varies, they will all ultimately break down or become obsolete.


  1. ii) Time: Although data saving takes time, data loss wastes more time. You should regularly back up your data as part of your research procedures, and you should have a strategy for how to save your data when your study is finished.


iii) Format: Data should be kept in a format that makes it possible to use it later. This may involve storing data in open or easily accessible file formats or just keeping your data on hand with the supporting documentation and other research resources.


Examples of generalist repositories:

General information about data storage and backup:



  1. Data Sharing


Why share your data?

  • Essential to publishers
  • A condition of government support
  • Enables the use of data to address new questions
  • Increases research transparency
  • Increases the value and citation of your articles among other scholars


How to Share Data

  • Depositing data in a disciplinary repository
  • Depositing data in the institutional repository
  • Publishing in a data journal
  • Submitting data with a journal article as a supplemental file


Additional resources and information about data sharing and preservation:


  1. Data Citation

Datasets used during the research process should be cited like you would cite an article - in the reference, cited sources, and bibliographies sections of your works. The practice of citing research data has evolved as researchers and stakeholders have come to understand the value of including data in the scholarly record between a research output and the supporting evidence that supports it.


Citing data will give credit to the responsible researchers and enables those who share the data to assess its impact. It also supports the research infrastructure by linking data and published research, which increases access to the data, offers opportunities for data verification, and encourages the use of data as a scholarly output on par with written works.


Although it is now expected to cite data, academic and professional communities have mostly had difficulty creating standards for mentioning data inside their established citation formats. Follow the citation guidelines provided by the publisher when referencing a dataset in a publication. Gather all the essential components and match the reference for textual articles if they don't specify a format for datasets.


Example citations

Ministry for the Environment. (2016). Vulnerable catchments (Version 17) [Data set].

Ministry of Education. (2015). Transient students [Data set].

Klette, R. (2014). [Data for computer vision spatial value statistics] [Unpublished raw data]. Auckland University of Technology.

General information about data citation:


Additional Resources:

Updated:: 16/12/2022 [j_mohdshahril]


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