Welcome! This is a guide to the practice of Research Data Management at JCU -- its aim is to provide you with the knowledge and skills to manage the data from your research project(s) according to best practice principles. The various sections of this guide will take you through some of the important elements of Research Data Management, and will introduce you to JCU's data repository, Research Data JCU.
Good research data management is, in many respects, an exercise in risk management. Having a Research Data Management Plan (RDMP) is crucial as it establishes some ground rules for the data produced by your project. The consequences of equipment failure and data loss can be catastrophic, so a considered approach to storing your data is very important. Organising your data is also an important step in conducting your research efficiently, and towards producing and supporting the results and conclusions of your work. Publishing your data, in certain instances and where adequate respect has been paid to the treatment of sensitive data, is also a great way to contribute to the advancement of knowledge in your field, and ensure that your own work is adequately recognised.
JCU supports the principle that, as far as possible, research data should be Findable, Accessible, Interoperable, and Reproducible (FAIR). It is important, also, to be aware that the treatment of research data is subject to certain ethical and legal standards and requirements. The University's policies and procedures in relation to research data have been developed in accordance with the Australian Code for the Responsible Conduct of Research.
As the Australian Research Data Commons guide "What is research data?" suggests, providing an authoritative definition of research data is challenging. The wide variety of different research fields means that a vast array of different types of materials are used for analysis -- research data may be physical or digital, experimental or observational, qualitative or quantitative, raw or processed - and in any format or media.
When thinking about storing or publishing your data, it may be useful to consider what needs to be kept for the validation and (in certain cases) reproduction of research findings, rather than focusing on the intrinsic properties of the data itself. This can also inform data management activities such as file formatting and documentation as these impact on the future value and use of the data.
The Data Retention and Preservation section of this guide provides more information about the types of data that should be retained.
Data management extends over the whole life of a research project, from data collection or creation through to archiving and (if appropriate) publication and/or sharing of data -- and may continue long after the research project is completed, as data is shared, re-used and cited.
This toolkit provides information and advice about these data management activities. You might be surprised to see how many activities you already include in your research practice!
(Adapted from: The University of California, Santa Cruz (2018) Data Management LibGuide, Research Data Management Lifecycle diagram, distributed under a Creative Commons Attribution Non-Commercial international license.)
Why poor data management is bad news for the progress of knowledge. A short video by Karen Hanson, Alisa Surkis and Karen Yacobucci at NYU Health Sciences Library (4.40min).
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