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Research Data Management

McGill University Dataverse

The McGill Libraries offer an institutional data repository, the McGill University Dataverse, for research data publishing and archiving. McGill faculty, students, and staff are welcome to deposit datasets in the McGill Dataverse repository. All data are stored securely on servers located in Canada. Data can be publicly accessible, available to specific individuals, or private/restricted. 

The McGill Dataverse can be found by following this URL: https://borealisdata.ca/dataverse/mcgill

Here are the instructions for creating a draft and submitting the dataset for publication (datasets will be reviewed by the McGill Libraries RDM Specialist before publication):

A few notes: 

  • Any data format can be deposited in the McGill Dataverse collection, but there is a limitation on the file size (each file must be 5GB or smaller). 

  • Once a dataset is published, it cannot be unpublished - this action is irreversible. 

  • The default license is CC-0 public domain, meaning you would give up all copyright. If you want a different license, for example CC-BY, make sure to change this when uploading the dataset draft

  • Sensitive data cannot be published in the McGill Dataverse. If your dataset contains information collected from anonymized human participants, contact the RDM specialist (rdm.library@mcgill.ca) with the consent form. The RDM specialist reviews all consent forms prior to granting permissions for dataset deposit.

  • When you upload the dataset, it will be a draft.  When you want to publish it, the dataset should be submitted for review and it will be published if it's not missing any information.

How to deposit: 

  • Create a Dataverse account by logging in (go to log in page and select McGill University from the drop-down under "Your Institution" and it will log you in automatically via single-sign on or you will be prompted to log in via McGill single-sign on): https://borealisdata.ca/dataverse/mcgill

  • When you’re logged in, go to the main McGill Dataverse page and you should see an Add Data button (https://borealisdata.ca/dataverse/mcgill). You can create a draft by clicking on that button and filling out the information/uploading files.

  • Provide a descriptive title for the dataset and enough information in the description for other users to understand where the information comes from, how it was collected, etc.  

For training on using Dataverse, please see this series of self-paced online modules: Dataverse 101: A Portage Training Module Series

Additional Data Repositories

A wide variety of additional data repositories and databases are available that archive research data from many subject areas. Coverage varies by discipline.   

McGill researchers who wish to look for a domain-specific data repository are encouraged to start by using Re3data.org which provides a comprehensive listings of disciplinary and institutional repositories to host and share research data.  

Other places to find lists of data repositories include: 

The following list names a few, reputable general data repositories:

  • Figshare - a general purpose repository often used in partnership w/ PLOS publications.
  • Dryad - frequently used for scientific and medical publication
  • Zenodo - a general purpose repository that integrates with Github for archiving and minting DOIs for Github repos
  • ICPSR - a repository commonly used for social science data
  • Qualitative Data Repository - for qualitative data, typically used for digital humanities and social sciences
  • FRDR - The Federated Research Data Repository is a Canadian solution for archiving large/big data 

Persistent Identifiers (PIDs) for Data

Persistent identifiers are:

  • A publishing initiative
  • A permanent link that points to your data, making the data findable (e.g. DOI)
  • Your data might move locations (URLs, repositories, etc), or the way we access the internet might change, but the DOI will always be the same
  • Machine-readable

Persistent identifiers allow for:

  • Archiving and preserving data (digitally) for the long-term
  • Linking a data article to a published study (normalizing best practices)
  • Connect PID of Dataset with DOI of journal article, and then subsequent studies that reuse the data, potentially leading to higher citation impact (up to 25%)

Data Journals

Data journals publish data articles, which are mini-publications about a dataset or database. Similar to the peer review process for the write-up of a journal article or study, the data would be peer reviewed (for an example of peer review guidelines for data articles, see the Earth Science System Data Journal guide). Data articles can be about data that underlie existing publications or they can be independent publications. Publishing data as their own research product allows you to cite the data easily in subsequent publications, link the data to publications, and potentially receive credit for the data itself in addition to any related studies.

Types of data publications:
  • Data articles
  • Data papers
  • Data notes
  • Data descriptors
What data can you publish?
  • Data underlying or linked to another study
  • Orphan data, dark data, null results
  • Updating an existing database or creating a database as a resource
  • Pilot studies/preliminary results
  • Reporting additional controls
  • Descriptions of data

A (slightly outdated but still accurate) list of data journals

Additional information on data journals:

Librarian

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Alisa Rod
Contact:
McGill University Library
Digital Initiatives
550 Sherbrooke West, West Tower
Montreal, QC H3A 1B9


Research Data Management Specialist

Library Resources Key

  • McGill users only
  • Open access resource
  • Free resource
  • In-library-use only
  • Catalogue record

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