Research Data Management (RDM): Frequently asked questions

Although specific details and information contained in data management plans vary depending on the nature and type of research being conducted, data management plans typically include sections on data collection, storage and preservation of data, data security, data curation, data sharing (if applicable), roles and responsibilities among team members involved in data management, and description of any ethical, legal, or commercial obligations related to the data

Data management plans do not set standards on what is considered acceptable practice, but rather document how researchers plan to manage data, leading to internal and external review and potentially emerging discipline or institutional norms that need to be followed. (adapted from Science Canada, 2021).

The Digital Research Alliance of Canada has made available online a free bilingual assistant called DMP Assistant for creating data management plans and accessing examples of DMP.

To simplify the writing process, ÉTS offers a template for its research community which contains sections and specific directives for ÉTS.

Users are advised to review and revise their plan throughout the lifecycle of their project. A complete or partial published plan may be shared with others.

There are various other online tools available to guide researchers through the elements of a data management plan, such as the Digital Curation Centre and the California Digital Library, and resources offered by institutions like Science Canada. (adapted from Science Canada, 2021).

When conducting research involving sensitive data or potential dual use, researchers may be required to take additional measures to balance the need for sharing and access to data with protecting against threats.

In order to ensure the integrity of their research is not compromised and the results of research (such as datasets, publications, patents) are secured until they choose to share them, researchers should implement good practices and cybersecurity infrastructure. These practices must be accepted by all members and partners of the research team.

Research conducted in Canada can be an attractive target for those who want to steal, use, or adapt research for their own purposes and gain. In some cases, research could lead to advances in strategic, military, or intelligence capabilities of other countries or cause deliberate harm.

Therefore, it is important to evaluate and clarify the intentions of your research partners and take reasonable risk-based measures to safeguard your research. For more information on protecting your research, risk assessments, or best practices for travel abroad, please contact the responsable de la Sécurité de la Recherche, au Bureau de la prévention et de la sécurité de l'ÉTS and visit Safeguarding your research ( Adapted from Science Canada, 2021)

Depositing data means placing them in a digital repository such as the ETS Dataverse, the Federated Research Data Repository or a wide range of generalist, discipline-specific or other repositories, with the aim of ensuring safe preservation and making them accessible after completion of the research project. Data deposit also allows researchers to decide how much data can be accessed by others and under what conditions.

Sharing data promotes reuse, validation, and linking to other data and research results. Making data available for use by others, in general, is called sharing data. Depositing data can be a way of sharing data, but it is not the only way. You can provide data to others upon request or publish them on a website. (Adapted from University of Calgary, 2022)

No! There are many ways to make your data available to others without making it openly accessible. By example, some data repository will allow you to restrict who can acess your data. Having a clear statement about data availability in publications or on your website can also let people know that your data is available. (Adapted from University of Calgary, 2022)

Yes! Implementing good RDM practices in your work can make a big difference in efficiency and effectiveness of your research. Here are some examples of RDM practices that can help you throughout a research project:

• Creating consistent and transparent file and folder names makes it easy to find what you need.

• Including information about the version like the date or the processing stage in your file and folder names helps you identify which is the latest version of your work (and where you have put it!)

• Creating clear documentation of data collection and analysis processes for yourself and your research team help you stay organized and integrate new members more easily

• Including robust metadata that describes your data well, so that you and others can understand it more easily in the future.

• Backing up your data regularly and saving it in multiple locations to avoid losing any data. (Adapted from University of Calgary, 2022).

It depends on the conditions under which you conducted your data collection.

If you have collected data from human participants during your research and your participants have signed a consent form, what they consented to in the form will determine whether you can share your data. If they did not consent to certain types of data being shared or to their data being shared in a particular way, you will generally not be able to share it.

In the case of research projects conducted in partnership, for example, with industry partners or community organizations, your agreements with them will determine whether research data can be shared.

In all cases, it is highly recommended to involve the Research Ethics committee early in the project to ensure that you establish all the necessary conditions that will allow you to share your data at the end of the project, if applicable.

Research materials undergo investigation – of a scientific, academic, literary, or artistic nature – and are used to generate research data. They are transformed into data through methods or practices. For example, it can involve biological samples for a geneticist, primary sources in an archival collection for a historian, or a tank of zebrafish for a biologist.

The corresponding research data for these examples would be data related to gene sequences, chronological analysis of ideas or contributions, and data on zebrafish behavior under certain conditions. The term "research material" refers to a general concept that can be applied to all disciplines, as well as the digital or analog domain.(Science Canada, 2023)

The FAIR principles for the management and stewardship of scientific data are an international best practice for improving the findability, accessibility, interoperability, and reusability of digital assets.

• Findable: The first step in (re)using data is to find it. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata is essential for the automatic discovery of datasets and services.

• Accessible: Once the user has found the required data, they should know how to access it, potentially through authentication and authorization.

• Interoperable: Data should generally be integrated with other data. Additionally, data should be interoperable and capable of working with applications (including software and hardware) or workflows for analysis, storage, and processing.

• Reusable: The ultimate goal of FAIR is to optimize data reuse. To achieve this, metadata and data should be well-described so that they can be reproduced and/or combined in different contexts.

To learn more about the FAIR principles, please refer to GO FAIR (Adapted from Science Canada, 2021)