The Douglas Research Centre has committed to open science at the institutional level. The Douglas Open Science Program aims to create a framework that supports transparent, collaborative, and responsible research that will achieve cultural change. Building on this foundation, the Open Science Team and D3SM community are taking a leading role in putting these principles into practice within the digital mental health space.

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D3SM members and project teams act as open science champions, testing new approaches, sharing tools, documenting processes, and developing reproducible, privacy-conscious research workflows. Across every stage of project development, our teams develop practical ways to make digital mental health innovation more open, ethical, and accessible.

 

Below are a summary and examples of the open science practices we encourage within the D3SM. Individual project pages highlight how each team applies these practices.

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• Persistent identifiers

  • Adopting persistent identifiers for different project components, such as ORCID iDs for researchers and DOI for diverse types of research outputs.

• Co-design with people with lived experience and/or relevant stakeholders (e.g., clinicians)

  • Co-designing data management plans, project design, governance, consent language, and knowledge mobilization outputs in partnership with patient partners or initiatives such as the CEYMH councils.

• Pre-registration and data management

  • Pre-registering hypotheses and protocols through the Open Science Framework (OSF) or systematic reviews through PROPERO.

  • Sharing data management plans created through platforms such as the OSF and the DMP Assistant;

  • Including privacy preservation strategies into data management plans (e.g.,(pseudo) anonymization, restricted access, synthetic datasets, aggregate data);

  • Adopting open and standardized file formats such as CSV, JSON, BIDS, etc, as well as standardized metadata schema.

  • Sharing data, when generated by the project, through appropriate platforms (e.g., Douglas Databank, Zenodo, FRDR).

• Open methods

  • Developing diverse types of open methods such as open software and/or analysis code, hardware, measures (e.g., psychological assessments), and protocols (including recruitment material documents such as flyers, consent scripts, and poster ads);

  • Sharing of software and/or code through version-controlled repositories such as GitHub, including README files and other documentation to ensure reusability;

  • Adopting open licenses for all types of open methods and their accompanying files (e.g., MIT or Creative Commons licenses).

• Adoption of open non proprietary technologies

  • Reusing existing open software for data analysis or project management (e.g., internal communications).

• Knowledge mobilization

  • Developing diverse outputs such as video capsules, training kits, and leaflets to diverse audiences such as service users, loved ones, and clinicians;

  • Archiving and openly sharing knowledge mobilization outputs (e.g., toolkits, webinars, workshops) through platforms such as OSF;

  • Adopting open licenses for knowledge mobilization outputs (e.g., Creative Commons licenses).

• Open access publishing

  • Posting pre-prints prior to publishing of the final publications.

  • Publishing peer-reviewed, open access publications in open access journals or self-archiving post-prints for green open repositories such as eScholarship@McGill.

• Open innovation

  • Adoption of commercialization pathways that forego restrictive intellectual property protection.

• Centralized project archiving

  • Creating webpages or OSF projects that archive all open outputs, including documentation, or links to all listed URLs, and ideally with version history. It will enable a long term legacy that fosters reuse and adaptation.