The first thing you need to do is collect some data!
Next, look over our What Is Dataverse and is it right for me? page to make sure the Carleton University Dataverse Collection satisfies your requirements. If you have additional questions about the service and its capabilities, please email us at email@example.com
- Proceed to the Borealis: The Canadian Dataverse Repository (borealisdata.ca) page
- Next, in the top right corner, click Login
- Then on the bottom of the pop up, click Sign Up.
- Fill in the required fields and use your @carleton.ca email to fill in the Affiliation field.
- Once you’re set up, proceed to the Carleton University Dataverse Collection to create a new dataset or dataverse: https://borealisdata.ca/dataverse/carleton
For more information, review and follow this documentation:
Follow our guides:
for a step-by-step resource on how to create and edit a dataset in the Carleton University Dataverse Collection. Also be sure to consult this 'Documentation and Supporting Material Required for Deposit' guide throughout the process.
Additionally, when you create your first dataset, please remember these rules:
- Use consistent and comprehensible file names and file structures.
Following proper file naming conventions makes it easier to navigate and find specific files, and allows other researchers to understand and reuse your dataset.
- Name files consistently
- Keep files names short (< 25 characters) but meaningful
- Do not use spaces. Use capital letters to delimit words, hyphens, or underscores
- Do not use non-alphanumeric characters
- Denote dates using ISO8601 standard YYYY-MM-DD (e.g. 2019-01-10).
- For more tips, check out this resource on file naming guidelines and this handy file naming guide
- Deposit your files in preferred file format(s) to support preservation and reuse.
The use of preferred file formats are important to support the long term preservation of your research data. Preferred formats are usually: Dataverse accepts all file formats such as Stata, Rdata, SPSS, .wav, .jpeg, .png, audio files, GIS data, csv, tab, ascii, etc... However, non-proprietary formats like .tab, and .csv are preferred.
- Non-proprietary (preferred)
- Open, with documented international standards
- Uses standard character encoding, preferably Unicode (e.g. UTF-8)
- Describe your dataset with sufficiently rich metadata to facilitate discovery.
Depositors must complete all required fields on the descriptive metadata page. Once your dataset is created, depositors are strongly encouraged to edit it by completing the additional metadata fields at the bottom of the metadata page, including geospatial metadata fields and subject specific metadata fields, where appropriate.
- Include one or more ReadMe files that describe your dataset(s) to enable correct interpretation and reuse.
For the research dataset to be read and interpreted correctly, it requires sufficient documentation. All deposited datasets must include a “ReadMe” file that includes the following information:
- Details about the dataset creation
- Description of files contained in the dataset
- Information about dataset completeness
- Limitations on reuse
- Be aware that the order in which you upload is the order in which your files will appear, so you may want to add your Readme file(s) first so that anyone looking at your data will understand the collection.
Dataverse allows for an extensive list of metadata fields that you can fill in to bolster your data, and to make it more findable and more useful to other researchers. Check out the Portage Training Groups Best Practices Guide for detailed examples and explanations of each metadata field.
If you create a Dataset in the Carleton University Dataverse Collection, you are given Contributor permission over that Dataset. As a Contributor, you can view the unpublished dataset, download files from the dataset, edit the dataset, and delete the dataset draft. However, Dataverse also has many other levels of permissions which allow you to add other researchers to your Dataset and Dataverse with different abilities.
If you would like to add someone to be able to only download the files, you can do that by adding them to your dataset and giving them the File Downloader permission.
We recommend that if you would like to add more researchers to your Dataset or Dataverse, contact us and we can help you add that person and set the appropriate permissions for them. You can reach us at firstname.lastname@example.org
Template licences options include:
- CC 0 (public domain, unambiguously waive all copyright control over your data in all jurisdictions worldwide. Data released with CC0 can be freely copied, modified, and distributed, even for commercial purposes, without violating copyright). This is the default licence in Dataverse, as one goal of the project is to promote open science best practices.
- CC BY (This licence lets others distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation.)
- CC BY-NC (This licence lets others remix, adapt, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.)
- CC BY-SA (This licence lets others remix, adapt, and build upon your work even for commercial purposes, as long as they credit you and license their new creations under the identical terms)
- CC BY-NC-SA (This licence lets others remix, adapt, and build upon your work non-commercially, as long as they credit you and licence their new creations under the identical terms.)
Not sure what licence to select? Creative Commons has a neat tool to help.
Guestbooks are created and edited at the collection level, but they are enabled at the dataset level. Therefore, researchers who wish to use a guestbook for their dataset located directly within an institutional collection would need to work with the institutional collection administrator to set-up the guestbook and to obtain the responses.
Step-by-step instructions that you might find useful:
Prior to publishing, check out this exemplar dataset and considering mimicking its completed metadata fields for maximum discoverability.
This is a common error message when uploading tabular data, so don't panic. Please review our FAQ to learn more!