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York University Dataverse Deposit Guidelines

The York University Dataverse, a member of Borealis: The Canadian Dataverse Repository, is available to all York University faculty, students and staff needing to deposit data with the institution. Dataverse allows for data to be stored on Canadian servers, released with a DOI, and shared openly or privately with precision at the file level. 

York University Dataverse conforms with the Borealis Terms of Use and Privacy Policy.

Data Preparation: Expectations for Data Depositors

The deposit and publication of original research datasets should support data discovery and reuse in order for other researchers to fully understand the context around these datasets and their applicability to future research projects. Therefore, data files should be prepared according to established standards such as the FAIR (Findability, Accessibility, Interoperability, and Reuse) guiding principles and other data sharing best practices. As such, before proceeding with depositing data to the York University Dataverse, researchers are expected to:

Ensure ethical and legal requirements are met

Data owners and depositors are solely responsible for confirming their rights to deposit and publish data. The York University Dataverse does not accept content that contains confidential or sensitive information, and it is primarily a platform for publishing and sharing datasets. It is not appropriate for the storage of highly sensitive or confidential data. Datasets must not contain information that could directly or indirectly identify a subject, except where the release of such identifying information has no potential for constituting an unwarranted invasion of privacy and/or breach of confidentiality. Data owners and depositors are solely responsible for confirming their rights to deposit and publish data. If a submission contains material from a copyright-protected source, depositors must document that they have obtained the necessary permissions from copyright owners and that third-party owned material is clearly identified and acknowledged within the content of their submission. For more information on relevant ethical and legal requirements for data sharing, please consult the following resources:

  1. York University Dataverse Collection Policy
  2. Data Retention and Deposit Guidelines for Research Involving Human Participants
  3. Research Data Management Language for Informed Consent (Portage Network)
  4. De-identification Guidance (Portage Network)

Use consistent and understandable file names and structures

  • Files must be named consistently
  • File names must be descriptive, but short (< 25 characters)
  • Do not use spaces. Instead, use underscores (e.g. first_study), hyphens (e.g. first-study) or camel case (FirstStudy)
  • Avoid non-alphanumeric characters like \ / ? : * ” > < | : # % ” { } | ^ [ ] ` ~ æÆ øØ åÅ äÄ öÖ …
  • Identify dates using ISO8601 standard YYYY-MM-DD (e.g. 2019-01-10)
  • The name of a file in original file format must be identical with the name of the corresponding file in preferred file format
  • Follow other best practices within your discipline for organizing your files

Use preferred file formats

Using preferred file formats helps ensure your data can be preserved and remain readable in the future. Preferred file formats are usually based on open standards and are non-proprietary. For more information on preferred file formats, consult the below resources:

  1. Recommended formats (UK Data Service)
  2. Prepare your data: Preferred file formats (DataverseNO)

Document data through a ReadMe file to facilitate data reuse

A ReadMe file describes your dataset in detail so other researchers can more easily understand it. ReadMe files should use forced numbering in the filename (e.g. 00_ReadMe.txt) to make it appear at the top of the file overview, be saved in PDF or plain text format, and contain the following information at minimal:

  • Title of the dataset, DOI, contact information
  • Methods
  • Data and file overview
  • Data-specific information
  • Terms of Reuse

For more information on ReadMe files, consult the following resources:

  1. ReadMe file template (York University)
  2. Social Sciences ReadMe file (DataverseNo)
  3. Life Science ReadMe file (DataverseNo)
  4. Guide to writing a ReadMe file (Cornell University)

Use metadata to document and describe data

Fill in all required and relevant recommended metadata fields when depositing or provide accurate and complete information to curators so they can assist with metadata creation in Dataverse. The Libraries may recommend changes to the descriptive metadata to enhance preservation and discoverability. Consult the following resource for more information on the metadata fields available through Dataverse:

  1. Dataverse North Metadata Best Practices Guide v 3.0 (Portage Network)

Assign a license to your data

Dataverse requires depositors to assign a license to their research datasets so that users can understand how to reuse and share data and provide proper attribution. When encountering a data set online, in the absence of an explicit license, it is prudent to treat the data as copyrighted material and to contact the copyright holder(s) to secure permissions for use. The assignment of open licenses to data sets adds clarity around permissions for use/re-use and eliminates the need to contact the copyright holder unless one wishes to secure rights above and beyond those granted by the license. To learn more about open licenses and your options for identifying reuse terms for your data, please consult the following resources:

  1. Conformant Licenses (Open Knowledge Foundation)
  2. Dataverse Dataset Terms (Borealis)

Library Support for Data Deposit and Curation

York University Libraries support data publishing through Dataverse and work beside our researchers during the curating process. We provide:

Administrative support

  • Setting up dataverses and/or sub dataverses for researchers, labs, or projects and assigning appropriate roles for researchers as data creators or contributors
  • Reviewing documentation to provide recommendations on potential ethical, legal, and commercial obligations

Training support

  • Training researchers on principles and best practices for data sharing
  • Providing training materials and tools to researchers needing to organize and name files/folders and prepare data/code and documentation

Depositing and publishing support

  • Assisting researchers with depositing data or reviewing self-deposited data sets
  • Reviewing data/code and documentation files to ensure they are complete, accessible, and provided in open formats if possible
  • Obtaining information from researchers to enhance metadata used to describe datasets
  • Explaining and providing additional information and resources to researchers on choosing open data licenses
  • Reviewing and approving data for publication

Technical and additional support

  • Providing technical support or connecting researchers with the Borealis team for advanced technical support
  • Providing additional recommendations to researchers

The guidelines document is licensed under a Creative Commons Attribution 4.0 International License and has been adapted from:

DataverseNO. Deposit Guidelines: Prepare Your Data.
Portage Network. Dataverse Curation Guide (Version 01).
Scholars Portal. Dataverse Deposit Guidelines Template.
University of Victoria. Dataverse Collection Deposit Guidelines.