At McMaster we support McMaster Dataverse, a collection within Borealis, the Canadian Dataverse Repository. Dataverse is an open source repository software developed at Harvard which provides secure access controls, licensing, version management, data citation, DOI minting, and more. Data deposited in McMaster Dataverse is visible in data discovery services such as Lunaris, the Canadian research data discovery service, OpenAIRE, a European open research portal, Google Dataset Search, and the Library's search tool.

Data is curated, reviewed and supported by Research Data Management Services. If you’re interested in publishing data on the McMaster Dataverse, please contact us at rdm@mcmaster.ca. 

Deposit in Dataverse

Start depositing data on your own by downloading and working through our Dataverse Depositor Checklist. 

There are more details on depositing data in McMaster Dataverse in our Data Deposit Guidelines. We also have a step-by-step Walkthrough of Depositing Data on McMaster Dataverse.

All data deposited in McMaster Dataverse requires a README file to describe your dataset to your future self and other researchers.

Sub-Collections for Labs and Research Groups

Researchers looking to gather all their datasets into one place can set up a sub-collection for their lab or research group in McMaster Dataverse. Data collections can be embedded in your lab website using an integration feature. A Dataverse sub-collection supports smooth offboarding and exit protocols for departing graduate students!

Request a sub-collection at rdm@mcmaster.ca alongside the following information:

• Requested Dataverse/Collection Name: 
• Dataverse Description:  
• Quicklink URL: i.e. https://borealisdata.ca/dataverse/macstroke
• Dataverse administrator contact email(s): Please identify at least 2 contacts.
• Who should have submit access? Lab members will need to register with Dataverse first before we can assign a role to them. They can log in using McMaster’s Single Sign-On! 

Recent examples include the Karen Kidd Lab, MacStroke Canada Lab - School of Rehabilitation Science, and the Fahnestock Neurotrophin Lab.

Including a license with your published data set allows you to define what others can and cannot do with your data. Licenses range from giving data into the public domain, where it is free for anyone to use for any purpose, to narrower licenses which may restrict use to non-commercial use only or require attribution to the author when the data is used.

The most common data licenses are Creative Commons (CC) and Open Data Commons. McMaster Dataverse defaults to the CC0 Public Domain Dedication license. This license dedicates the dataset and its content to the public domain, free and unrestricted for everyone to use. However, Dataverse offers the ability to choose from a variety of CC licenses during the deposit process. Though CC0 waives copyright controls, researchers using data from Dataverse are still expected to follow the Dataverse Community Norms as well as scientific best practices which assert that proper credit should be given through citation of datasets. CC license limitations include BY which requires users attribute the creator and NC which requires data not be used for commercial purposes. If you're not sure which license fits your research best, CC has a license chooser tool.

Licensing software code uses similar but slightly different licenses. There are many potential choices here. If you are looking to share research software code you have written, the ‘Choose a License’ website developed by GitHub can guide you through the process. 

​​​​READMEs and Documentation

​A README file is a document that describes the files and organization of your data. Data repositories are increasingly starting to ask researchers to provide readme files, because they dramatically increase the legibility of datasets. 

In the context of data deposit, they make your data future-proof by helping you remember what you've done years after you've done it. They also support open science by helping other researchers understand, reuse, or reproduce your research if you decide to share/publish the data. READMEs should be simple text documents (.txt, .md, or .pdf). They typically include:

• Basic project metadata: contact information and links to associated publications and data sources
• A description of the project's folder hierarchy and file naming scheme
• Description of the contents of important data files

McMaster has developed a custom McMaster-specific README template which can be used to support your data deposits. 

Other resources might be helpful in drafting your README:

• Read a bit more context on what to include in a README using Cornell's guidance on writing readme-style metadata
• Review an example template by Francesco Varrato, Alain Borel and Chiara Gabella, "README file for Datasets - Best practices and template"
• If you are working in quantitative Social Science, this template by Social Science Data Editors may be more helpful – “A template README for social science replication packages”
• For more information on building readme files, see the Harvard Longwood README file guide and this post by data professional and author Kristin Briney called README.TXT

In addition to a README, you should include any additional files another researcher would need to understand and reproduce your results. This can include data dictionaries, codebooks, field notes, as well as noting what versions of software you've used. Learn more in Data Documentation.

Repository Metadata

Metadata is "data about data." In addition to project-level metadata that might help your data align with disciplinary standards, there are also metadata—links and keywords—that are important to add to your data deposit. Keywords (author, title, language, description, etc) as well as details like geographic coordinates or dates of collection help make sure your data is findable by other researchers. These fields are often interconnected across different websites and mean that when you share data in McMaster Dataverse, information is searchable in national and international research data sources. Make sure as many fields are inputted as are relevant to your research.

Some repositories have their own metadata standards, systems which ensure data is described, interpreted, and used the same way across users and groups. For example, the Inter-university Consortium for Political and Social Research (ICPSR)'s metadata standard is Data Documentation Initiative (DDI), which is commonly used for data produced for surveys. If you end up depositing in McMaster Dataverse, the metadata standard is Simple Dublin Core, and it is also compliant with DDI and DataCite. 

You should also reference any disciplinary project-level metadata in your README file and the repository's metadata fields, where appropriate. 

Connect Researchers, Publications, and Code

Research involves a lot of stakeholders, products, sources: researchers, publications, data, code, grants, institutions, and more. Persistent identifiers (PIDs) are unique and immovable touchpoints or anchors. They help facilitate links between researchers, publications, and more. PIDs make information clearer for humans reading them, and also make more stable, interconnected systems for computers to read. Alongside keywords, it's important to include these relevant links alongside your data deposit. 

Open Researcher and Contributor IDs (ORCIDs) are are persistent identifiers for researchers, contributors, and authors. ORCID is an open, global, community-built not-for-profit organization that is free for users and sustained by member organizations. ORCIDs help authors who share a name (i.e. Nushi Devi, John Smith, Grace Wang) distinguish themselves from other researchers. It also lets you automate connections between your research. When you publish data in a data repository, including your ORCID means it can easily port to a research profile like McMaster Experts. Register for an ORCID. Consider linking to the many people involved in your research. The Contributor Role Taxonomy (CRediT) can help you think through all of the contributors to your dataset.

Digital Object Identifiers (DOIs) are persistent identifiers for articles, books, ebooks, datasets, software, and more. DOIs are managed by not-for-profit organizations Crossref and DataCite, which can create DOIs. When you deposit data in a repository, make sure to your publication DOI in the corresponding field. Publishing your dataset will create a DOI. Link the dataset DOI to your publication and include a citation.

Research Organization Registry (ROR) is a persistent identifier for universities, research institutions, research centres and more. ROR is a global, community-led registry, a collaborative initiative of the California Digital Library, Crossref, and DataCite. McMaster University's ROR is https://ror.org/02fa3aq29 and the McMaster Children's Hospital's ROR is https://ror.org/03cegwq60. You can include this in your README file and the repository fields.

Sometimes determining where Code and Research Software should go in a data deposit can be difficult. If your code and scripts are directly developed to process or analyze your dataset, it's best to include them alongside your data deposit. However, you may prefer to deposit code and software in a platform made for those formats. These often include version control and license options specifically designed for software. In these cases, we recommend depositing in Zenodo, an open research platform designed for publications, software, and more. You can even link GitHub with Zenodo to ensure each release triggers a new version. In this case, you can add the DOI from your code or software to your dataset and vice versa. McMaster's Research Software Development team can help in guiding this, or you can contact RDM Services or set up an appointment to discuss further.