Research Data Service
Pubrica’s research data service is for authors who need to share the data behind a publication or meet a funder’s data sharing condition. Our data curation specialists prepare your dataset for deposit: de-identified, documented, licensed and placed in a suitable long-term repository. We also write the data availability statement that links to it, because journals and funders no longer accept statements that data are available on request.
- A real data availability statement, linked to a deposited dataset with a DOI rather than to the corresponding author
- Repository selection: a domain repository where one exists, a general repository where it does not, and controlled access where the data are identifiable
- De-identification with a formal risk assessment, not only removal of names
- Documentation for reuse: README, data dictionary, variable definitions, units, missing-value codes and the code that produced the analysis
- Funder requirements met, including data management and sharing plans where they are a condition of the grant
Data availability statementIncluded
Repository selection and deposit preparationIncluded
De-identification and risk assessmentIncluded
README, dictionary and metadataIncluded
Deposit under your accountYours to submit
Dataset → assessed → documented → deposited → cited
- Re-identification risk assessed
- Repository selected for your data
- Documented for independent reuse
- Licence chosen deliberately
- Statement points at a DOI
Deliverables
Four Components of Every Research Data Service
Each project covers one dataset and its consent basis. What participants agreed to determines what can be shared, and this cannot be changed afterwards.
Statement exampleData are available from the corresponding author on reasonable request.The dataset is deposited in a named repository under DOI 10.xxxx/xxxxx, openly available under CC0. Analysis code is in the same deposit. Access does not depend on the authors responding to requests.
Risk assessment exampleNames and hospital numbers removed.Rare diagnosis plus postcode district plus date of birth re-identified a single individual in 400 records. Year of birth, region, and the combination re-checked rather than assumed safe.
Deposit exampleThe test is whether a researcher with no contact with the authors can reproduce the analysis. Deposits that fail this test may be technically compliant but are of little practical use.
Compliance check exampleFunder and journal requirements differ, and the strictest one applies. Checking this before deposit avoids the need for a second deposit.
Samples are illustrative and anonymised. Hover over a sample to replay it.
Compliance and Standards
FAIR Principles, ICMJE, Funder Data Policies and ISO 9001:2015
Where a journal or funder states a requirement, it takes precedence, and the strictest requirement applies.
Findable, Accessible, Interoperable, Reusable (Wilkinson et al., 2016)
FAIR is the standard against which funders and repositories assess a deposit. Most of its requirements concern metadata and documentation rather than openness.
ICMJE Recommendations (data sharing statements)
A data sharing statement is required for clinical trials, covering what will be shared, when, with whom and by what mechanism.
Funder data policies (NIH DMS policy, Horizon Europe, Wellcome)
Data management and sharing plans as a grant condition, with deposit and documentation expectations attached.
Quality management (cert. H3CR9TCU20251212INDQM10N)
Our quality management system governs how work is briefed, produced, reviewed and released.
Responsible Use of AI
Data Provenance and Responsible Use of AI
Datasets are used by researchers who were not involved in collecting them. To be usable, each item needs a record of its source and of who checked it.
Why We Do Not Publish AI Accuracy Figures
Accuracy figures for AI tools are usually reported by the companies that sell them.
Pubrica therefore quotes no self-generated accuracy figure, as it would carry the same conflict of interest. Instead, we tell you which tool was used on your project, at which stage, and what a human editor changed afterwards.
How It Works
How Our Research Data Service Process Works
We check consent first, because if it does not permit sharing, further preparation cannot resolve that.
Consent and permissions check
We confirm what participants consented to, what the ethics approval permits and what any data-sharing agreement allows before any preparation begins.
De-identification and risk assessment
We remove direct identifiers, assess indirect identifiers against the actual dataset, and reduce data resolution to what the analysis requires.
Documentation and repository
We prepare the README, data dictionary, metadata and analysis code, and select a repository: domain-specific where one exists, with controlled access where the data require it.
Deposit and statement
We prepare the deposit under your account, agree the licence with you and write the data availability statement linking to the resulting DOI.
Our experts
Our Research Data Team
The same named team works on your project from scoping to delivery, and your project coordinator introduces each member before work begins.
Reviews the scientific content for accuracy and confirms that each conclusion is supported by the evidence. The expert is matched to your field.
Checks that statistical methods, tables and conclusions are consistent with the analysis plan and that the analysis can be reproduced.
Prepares, cleans and documents the data so that every step from raw data to final output can be traced and reproduced.
Independently checks code, tables and outputs against the analysis plan. Discrepancies are logged and resolved before delivery.
Packages
Research Data Service Packages
The cost depends on dataset size, data sensitivity and the amount of existing documentation.
Dataset deposit
Authors with a paper in press or a funder condition to meet
Quoted per projectOn size, sensitivity and documentation state
Delivery By scope
- ✓Consent and permissions check
- ✓De-identification with risk assessment
- ✓README, data dictionary and metadata
- ✓Repository selection and deposit preparation
- ✓Licence chosen deliberately
- ✓Data availability statement written
Where data cannot be shared openly, controlled access is usually available and is preferable to a statement that readers cannot act on.
Deliverables Request a quoteData management and sharing plan
Grant applications with a plan as a condition
Quoted per projectOn funder and project scope
Delivery By scope
- ✓Plan written to the funder’s template
- ✓Data types, standards and volumes described
- ✓Preservation and access route specified
- ✓Roles, costs and timeline set out
- ✓Consent language checked against the sharing intent
A plan written at the application stage also identifies consent language that would prevent the sharing it describes.
Deliverables Request a quoteSend the dataset and target journal, and we will tell you free of charge whether the consent covers deposit. Consent cannot be amended after participants have signed.
FAQs
Research Data FAQs
If your question is not answered here, please ask an expert. No quote is required.
In practice, such statements usually mean the data are unavailable. Studies of data-on-request statements repeatedly find that most requests go unanswered or are refused.
Journals and funders now expect a deposited dataset with an identifier, or a stated reason why deposit is not possible. A statement directing readers to the authors satisfies fewer policies each year.
In that case, controlled access is appropriate. The data are deposited in a repository that manages access requests against stated conditions, and the metadata remain openly discoverable even where the data are not.
This is a much stronger position than a general statement that data cannot be shared, and it satisfies most funder conditions.
We check this first, as it is the most common obstacle. Consent that does not mention data sharing, registry deposit or future research generally cannot support them.
Consent cannot be amended after participants have signed, so this question should be addressed at the protocol stage. We also raise it as part of our consent form service.
We prepare the deposit and can submit it under your account, but the deposit is made in your name as the responsible party, exactly as with registry disclosure.
This is important because the dataset carries your authorship and institutional affiliation, and the licence decision is yours.
Related services
Related Publication Support Services
Data sharing relates to consent, disclosure and the manuscript itself.
1CONSENT AND DATA COLLECTIONbefore data are shared
Informed consent formSecondary use addressed before signature. Medical data collectionCollection, de-identification and governance.2MANUSCRIPT SUBMISSIONthe same submission
Journal submissionFiles, declarations and the portal. Post-acceptance supportProofs, licence and deposit.3STATISTICAL SUPPORTreproducibility
BiostatisticsReproducible code alongside the numbers. Clinical trial supportRegistry records and results disclosure.Request a Free Consent and Data Sharing Check
We will tell you free of charge whether the consent covers deposit, as consent cannot be amended after participants have signed.