Privacy & data handling
How Ref-Check handles your documents, saved checks, optional sharing and reviewer feedback.
Private by default
Uploaded work is held in your workspace rather than made available for other users to browse.
Sharing is your choice
Share for Feedback is optional and uses invitations to enable review and comments.
No AI analysis or training
Ref-Check uses structured, rule-based checks instead of generative AI or machine-learning models. Uploaded documents are never used to train AI models.
Overview
Ref-Check processes uploaded content to provide citation checking, reference verification, reports and document review. Where a user chooses to use Share for Feedback, the platform also supports access to the shared review and the comments or annotations made by invited reviewers. We take a cautious, transparent approach to data handling for education, research and professional use.
What information is processed
When a user uploads a document, Ref-Check processes its content to identify in-text citations, reference entries and associated identifiers (such as DOIs and URLs). It may generate a document preview and save reports and findings so users can return to their work.
Ref-Check may also process basic account, contact and transaction information where required to provide and administer the service. If Share for Feedback is used, relevant information may include invitation and recipient details, access information, selected text, highlights, annotations and reviewer comments.
What Ref-Check does not do
- Ref-Check does not use uploaded documents as a reusable training corpus
- Ref-Check does not use generative AI or machine-learning models to analyse uploaded work
- Uploaded documents are not provided to external AI services or used for external model training
- Ref-Check does not currently perform plagiarism detection or content comparison
External scholarly verification
Where required to verify references and publication details, Ref-Check may submit limited bibliographic information, such as titles, author names, publication details, DOIs or URLs, to external scholarly metadata services. Uploaded documents themselves are not provided to these services.
Share for Feedback: access and privacy
Share for Feedback allows a document owner to invite another person to review their work and provide comments. It can support feedback between students and tutors, colleagues, editors and peer reviewers. Sharing is optional; a check does not automatically make a document available to other users.
What invited reviewers can access
An invited reviewer can access the shared document view and associated Ref-Check findings made available to them through the invitation. Reviewers can highlight passages and add comments or annotations. They cannot edit the original uploaded document through this feature.
Information provided in an invitation and during a review, including reviewer feedback, is processed to deliver the requested collaboration and make that feedback available within the shared review workflow.
Invitations and access controls
Invitations are time-limited, and sharing access can be managed using the available platform controls. Users should check whom they have invited and whether access remains appropriate. An invitation does not grant a reviewer a general right to access other documents in the owner's workspace.
Before sharing confidential work: make sure you are authorised to share the document and its findings with the intended recipient. This is particularly important for unpublished manuscripts, student submissions, identifiable personal information and material covered by institutional, journal or publisher confidentiality requirements. Do not assume the feature provides anonymous or double-anonymous peer review.
Storage, retention and operational data
Data retention
Uploaded content is processed and stored securely to support reference checking, document previews, saved reports and ongoing review. Where Share for Feedback is used, the related invitations, annotations and comments may also be stored to support the review process. Uploaded documents are not used as a reusable training dataset or for external model training.
Users can manage documents within their workspace. If you need information about retention, removing a document or handling associated shared feedback, please contact us using the details below.
Use of AI and automated systems
Ref-Check does not employ generative AI or probabilistic machine-learning models. All checks are based on deterministic, rule-based processes designed to be predictable and explainable.
Logging and operational data
Limited technical logs may be generated to support system reliability, security, and performance monitoring. These logs are designed to avoid capturing document content and are handled in line with information-governance best practice.
Institutional responsibilities and enquiries
Institutional use and governance
For institutions, Ref-Check can provide additional information to support GDPR and information-governance processes. This may include details of hosting region, log retention, and external metadata sources used for verification.
User responsibility
Users are responsible for ensuring they have the right to upload content to Ref-Check and, where applicable, to share that content and its findings with other people. Users should follow their organisation's policies, any confidentiality obligations and applicable data protection requirements when choosing recipients or requesting feedback.
Contact and enquiries
If you have questions about privacy or data handling, including requests relating to access, correction, retention or deletion of personal data, please contact us. For general platform instructions, see the User guide and FAQs.
Contact: info@ref-check.co.uk