Frequently Asked Questions

Beneath is a compilation of Frequently Asked Questions (FAQs) organized by relevant RDM sections and pages. If you have any additional questions that are not covered by this list and it's external resources, or simply require RDM support please contact the McMaster Library Maps, Data & GIS department through rdmgmt@mcmaster.ca.

General

Research Data Management

Why should I manage my research data?

Managing research data can improve your research efficiency, promote interdisciplinary study, minimize your risk of data loss, add value to your current research project, provide enhanced access to your publications, and enable your long-term access to your data. Learn more.

What is involved in Research Data Management (RDM)?

Research Data Management (RDM) consists of documenting, preserving, and sharing your data. Creating a Research Data Management Plan (DMP) prior to beginning your research is an effective way of incorporating RDM best practices into your project.

I want to manage my research data, where should I begin?

Regardless of your position in the research lifecycle (whether you are preparing to start a project, immersed in one, or have already completed one) the best way to begin with RDM is to plan your journey. Developing a DMP allows you to idealize and map out your strategy to see how it will be integrated into your research life cycle.

How does RDM in Canada compare to other countries?

Regulations have been adopted by two-thirds of Canada's Tri-Council funding agencies (SSHRC & CIHR) to improve data stewardship and sharing, but Canada still lags behind other nations like the UK and US where RDM has become a regular practice required by all major funding agencies.

Where can I learn more about RDM best practices?

To learn more about different components and best practices associated with RDM we recommend reading the UK Data Archive's document on Managing and Sharing Data Best Practice for Researchers.

 

Planning & Preparing

Data Management Plan

What is a DMP?

A Data Management Plan (DMP) is a formal document, often submitted as part of a grant application, that outlines the intended documentation, preservation, and access strategies for the research data. This plan outlines not only how data will be handled during the project, but also following its completion.

Why do I need a DMP?

The submission of a DMP is required by some funding agencies (See Funding Agency Guidelines), but more importantly DMPs help organize the research process, identify challenges with long-term data preservation and accessibility, and foster data stewardship. DMPs strive to ensure that data can be interpreted, accessed, and used in the future. By establishing a DMP prior to beginning a project saves time long-term as there is no need to re-organize or reformat the data to be preserved and readily shared with colleagues. If you are working with colleagues and/or research assistants the DMP can assist with data handling consistency.

What are some good templates for my DMP?

There exist a variety of different DMP templates with several common elements, but there is not real standard template yet in Canada. All of these templates are from organizations in the UK and US.

Funding Agencies

Why are funding agencies interested in research data management?

The interest of funding agencies stems from the philosophy that research information gathered using public funds should be made publically available. When this information is made availablethe overall cost of research is reduced by avoiding repetition. In order to ensure research data is comprehensive and accessible a variety of different RDM procedures can be applied such as a Data Management Plan (DMP) outlining your data management procedures at different stages of the project, and preservation of your data in a data centre or repository.

Why do some funding agencies require a DMP in a grant application?

DMPs show funding agencies that the researcher has considered the organization and maintenance of their research data during and after their research project. The adoption of RDM is  advantageous to most funding agencies because it reduces the overall cost of research.

 

Collecting & Analyzing

Documentation and Metadata

What is metadata?

Metadata is documentation that describes research data and often the associated study. To get better understanding of what metadata is, please follow Understanding Metadata publication.

What is the importance of metadata?

Metadata is essential to interpreting research data because it provides experimental context and details to end users.

What are common metadata elements?

Common metadata elements include common publication details such as author, description, the year created, in addition to information about the data such as its format, source and coverage.

Data Storage & Security

What are different ways of storing my research data? 

Research data can be stored both electronically and physically. It is highly encouraged that all data is stored in multiple locations and checked regularly to ensure to is usable. Physical storage includes CDs, USBs, while electronic storage includes server backups, cloud storage, hardware storage etc. 

How often should I store my research data?

Research data should be stored regularly to multiple locations during the data collection phase. Volumes of data tha accumulate rapidly should be backed up and stored more frequently (e.g. daily).

How can I guarantee my research data is secure?

There exist different approaches to data security, which are dependent on the storage location of the data. The first step to data security is controlling access to the files and ensuring appropriate security measures are in place. For more information explore the UK Data Archive.

Are there necessary precautions I should take for storing personal and sensitive data?

Anonymising your data by removing key personal identifiers the data is not longer classified as personal. A personal identity can be disclosed from direct identifiers (name, address, telephone number, pictures) and indirect identifiers (when linked with other publicly available information could identify an individual). Also ensure data is stored locally as opposed to on third party servers where you are unable to restrict access. Extra measures can be taken to encrypt your data, learn more about handling and storing sensitive data at the University of Ohio's pageThere are concerns that even with anonymization, if enough metadata exists indentities can be revealed. Interesting article addressing the degree of anonymisation here. Other techniques include removing direct identifiers, restricting upper lower ranges of variables to hide outliers, and reducing precision/detail of the data through aggregation/generalisation.

What are appropriate naming conventions for my folders and files?

Folder and file names should reflect their contents, in addition to being unique, meaningful, and brief. The top folder's name should include the project title, and year. Sub-folders should have a clear and consistent naming convention such as different experimental runs, dataset versions, or persons working on the project. File names should include the project name, version,  and any other information that can clearly describes its contents and distinguishes it from other files within the folder.

Formatting Data

What does it mean to format my data?

Data files are often formatted to ensure that their research data remains accessible following the project. For a file to be accessible this means that it can be viewed or modified in different operating systems and programs both short- and long-term. By adjusting the data file type to open extensions (.odt, .csv) as opposed to proprietary extensions (.doc, .xls) it remains accessible to a wider audience for a far greater period of time.

What file formats improve the accessibility of my data?

Saving your data files with open extensions as opposed to proprietary greatly improves the accessibility of your data. A list of these extensions can be found on page 12 of this document.

I can't seem to save my data in an open file format?

Certain programs will only allow you to save your data in certain proprietary file formats. Often these proprietary files can be converted to open files using an online file converter or open-source software to an open file type listed here. If your file type cannot be directly converted, consider whether your data can be better shared/interpreted as any of the open file types.

 

Preserving & Archiving

Archiving Data

Is Dropbox or Google Drive safe means of archiving and sharing my data?

Dropbox and Google Drive are both efficient means of storing and accessing data, however concerns arise because the data put in these cloud services is not stored locally. Both Dropbox and Google Drive encrypt your files while they are in transit and stored on their servers to add an extra level of security. This means that these services also hold the decryption key and are able to it if  they are required to (e.g. by law enforcement).

What would you recommend using to archive and share data instead of Dropbox or Google Drive?

Instead of using dropbox or google drive it is highly recommended that you archive and share your data through a data repository.

What is a data repository?

As the name suggests a data repository is a location where data can be stored and accessed long-term. Data repositories allow different types of data to be stored together and organized into desired classifications. There exist a variety of different data repositories online that make use of cloud-based services to improve your accessibility to your data.

What repository is best for my data?

The McMaster Libraries is using Scholars Portal (SP) Dataverse as a central location for research at McMaster. Scholars Portal Dataverse is a service of the Ontario Council of University Libraries and is hosted right here in Ontario. You might find using SP Dataverse advantageous because it is tailored for multi-disciplinary research and the McMaster Libraries are best equipped to support you in utilizing this service. Despite the library's inclination towards Dataverse there exist several discipline-specific data repositories that you might find better suit your data and your objectives for archiving and sharing it. The Registry of Research Data Repositories can help you find the best discipline-specific repository for your data.

What are some considerations when selecting a repository for my data?

When selecting a repository for your data it is important to consider where your data will reside. Will it be locally within Canada or Ontario, or in a different nation. Often researchers are more comfortable that their research data is stored as locally as possible because this promotes a sense of security. In all cases ensure you are aware of the laws and policies surrounding data preservation and sharing. It is important to understand the measures that a repository takes to ensure your data is safe and secure. Do they encrypt it so employees can't see it? Are backups generated? What sort of security measures keep the server on which they reside secure short and long-term?

What is Dataverse?

Dataverse is a data repository open to all disciplines for archiving, sharing, and citing research data.

How do I use Dataverse?

Anticipating this question we have created an in-depth user guide for Dataverse 3.6.2, which covers the process from registering, creating your dataverse, uploading files, and modifying your permissions. If you have any additional questions that are not covered within this user guide please contact the McMaster Library Maps, Data & GIS Department.

Is my data secure in Dataverse?

Dataverse provides a backup copy of your files for safekeeping and allows users to create their own terms and restrictions for data use and access.

Do I still retain the rights to my data after it is put in Dataverse?

When you put your data in Dataverse you retain your intellectual property rights over the data, but license Dataverse to archive and provide access to your data.

Do I need to share my data publicly if I put it in Dataverse?

No, you as the owner of the data are able to adjust the permissions and sharing settings associated with your Dataverse, study and data files to determine who will be able to access your data and view your study. This means that you can choose to share your data with only your research group, other colleagues, or not at all.  

Is Dataverse only for completed data, or can I use it to upload and share intermediate data with my research group for completion?

Dataverse can be used to share and work on intermediate data files throughout at the course of a research project. Dataverse allows user-specific sharing and displays versioning of data which allows intermediate data to be worked on within a research group before sharing it with a wider audience.

Sharing Data

Why should I share my research data?
There are plenty of reasons to share your research data that include to increase interest in your research, increase the research rate on your topic of student, and to fulfill your funding agency or journal publication requirements. 
 
Where do I find the restrictions on sharing my research data?
Restrictions on sharing your research data can be imposed by your funding agency, the instrument or machine you use, or even the specific data centre or repository. It is important that you check each step of the way if there is anything that restricts your ability to share your research data.
 
How accessible do I need to make my research data?
It is encouraged that you make your research data as open and accessible as possible while remaining conscious of data archiving and sharing requirements from your funding agency, the type data used (sensitive, personal, secondary data with restrictions) and the repository you are using.
 
What sort of Terms of Use/Permissions can I set for those using my research data?
There exist a variety of different terms of use or permissions that you can choose to set on your research data for use and re-distribution. You can find more information about different types of permissions on the University of Oregon Library website.

Intellectual Property Rights

What are intellectual property rights?

Intellectual property rights are legal rights attributed to original intellectual works.

Are my intellectual property rights modified when sharing data through a data centre?

When this data or information is shared through a data centre like Dataverse the researcher retains their copyright over the data, but licenses the centre to archive and provide access to the data.

What is sensitive and confidential data?

Sensitive and confidential data both require higher levels of protection or management. 

University of Michigan has very concise Sensitive Data Classification and their Sensitive Data Guide.

Can I ethically share sensitive and confidential data?

Incorporating special measures into the research process can allow some sensitive and confidential research data to be shared. These measures include anonymizing data where possible to protect identities, incorporating provisions on data sharing into participant informed consent and controlling access to the data.