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Formulaire de recherche

Research Data

Borealis (Dataverse)

Borealis, the Canadian Dataverse Repository, is a free and open repository to find and deposit research data. Fully hosted in Canada and recommended to Polytechnique researchers by the Bureau de coopération interuniversitaire (BCI).

"Dataverse is ideal for depositing and sharing research data, and supports a variety of research data types and formats. You are welcome to upload research data, supplementary tables and documentation, publications associated with data, presentations, etc." (Source)

​Consult this table for more information.

Learning module: Dataverse 101 – Module 1: Introduction to Dataverse (Portage)

Consulte the Dataverse Guide.

If you have any questions, please contact Arina Soare or Brendan Fitzgibbon.



● "A Dataverse is a container for one or more Datasets or Dataverses. Each university in the Scholars Portal Dataverse has an Institutional Dataverse", including Polytechnique Montreal.

● "A Dataset is a container for a particular research data set (this can include research data, code, and documentation)." (Source)

Before Uploading Your Data in Dataverse

● It is possible to generate a "Private URL to allow those without Dataverse accounts to access your unpublished dataset". If you would like to publish an article in a journal that requests access to the data used for that article, you could submit the URL along with the article. The dataset can be published later, after the article is published.
● The default license in Dataverse is the Creative Commons CC0 license, but you can choose another type of Creative Commons license, depending on the contract.
● Have the data description and keywords in both English and French, to increase the chance that your dataset is retrieved by other researchers.

The Library will publish a dataset after checking the metadata (description, keywords, etc.).

Please note! If a dataset is published, it will no longer be possible to delete the attached files without deleting the entire dataset and thus losing the DOI assigned to it.

It is possible to add more files to the dataset after publication and thus a new version of the dataset will be created. The previous version will always remain visible in Dataverse.

Steps to Create the Unpublished Draft Version of Your Dataset

1. Create your Dataverse account using your Polytechnique email. Polytechnique's institutional Dataverse is a sub-unit of the wider Borealis, the Canadian Dataverse Repository network. In some cases, the Library may create sub-dataverses for research groups within the Polytechnique Montreal Dataverse.
2. + Add Data – New Dataset
3. The host Dataverse should be Dataverse de Polytechnique Montréal (or the Dataverse of the research group, previously created by the Library).
4. Complete the fields: Title, Author (including ORCID; click on + to add other authors), Contact, Description (in both languages), Subject, Keyword (in both languages under Term) and Language of the article.
In Related Publication, enter the DOI and the reference of the article based on this data.
5. Attach the files and save the dataset. Dataverse will assign it a DOI. (See the maximum size of an individual file).
6. Click on the Files and Metadata tabs if you want to make changes.
7. To change the default CC0 license, click on Terms Edit Terms Requirements – No, do not apply the CC0 "Public Domain Dedication". Enter your custom terms of use in Terms of Use (e.g., CC BY license). Here, Dataverse provides a Sample Data Usage Agreement for datasets that have de-identified human subject data.
Complete all necessary sections under Terms, describing how you would like your data to be used by other parties.
8. Select the Guestbook / Registre des visiteurs.
9. Save the changes.

● To obtain a private link: Edit Dataset – Private URL – Create private URL.

When you are ready for your dataset to be published, click Publish Dataset and the Library will review the metadata and publish the dataset.
Remember that if the dataset is published, it will no longer be possible to delete the attachments without deleting the entire dataset and thus losing the DOI assigned to it.

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