In recent years, SCI-indexed journals have increasingly required authors to provide access to their underlying datasets for the sake of transparency, reproducibility, and scientific integrity. While this promotes open science, it also raises a critical concern for many researchers: How can you share data without compromising your research advantage or intellectual property?
Before submission, carefully review the journal’s “Data Availability” or “Research Data” section:
Some require open, public access to raw data.
Others allow restricted access upon reasonable request.
Certain journals accept summarized or anonymized datasets instead of full raw data.
Knowing the exact policy lets you prepare a compliant yet protective approach.
Avoid uploading unnecessary sensitive information:
Provide the minimal dataset needed to replicate the results.
Remove unrelated variables or personal identifiers (especially in human-subject research).
Aggregate data to reduce granularity if it doesn’t affect reproducibility.
Instead of making data completely public:
Deposit it in a repository that offers access control, such as Dryad (restricted files), Zenodo with embargo, or institutional repositories with request-based access.
Require requesters to sign a Data Use Agreement (DUA) outlining usage restrictions.
When uploading datasets, choose licenses like:
CC BY-NC (Attribution-NonCommercial): Allows reuse but forbids commercial exploitation.
Custom License: Specify that the data cannot be used for publication without citation or collaboration.
Clearly indicate citation requirements to ensure proper credit.
If your dataset supports multiple studies:
Publish only the portion relevant to the current article.
For ongoing projects, consider an embargo period (e.g., data will be public 12 months after publication).
Document your data thoroughly so you maintain ownership of interpretation.
If you fear others might “scoop” your future work:
Invite potential heavy users of your data to co-author future papers.
Build formal data-sharing collaborations rather than releasing it unconditionally.
Always maintain a complete, timestamped internal record of your dataset. This serves as proof of authorship and origin, useful in cases of dispute.
Data sharing is becoming a non-negotiable requirement for many SCI journals, but it doesn’t mean you have to lose control of your intellectual contributions. By understanding journal policies, limiting exposure to essential data, using controlled repositories, and applying protective licenses, you can comply with open science principles while safeguarding your research advantage.
For academic events and publications that balance transparency with researcher protection, platforms like iconf.org often list conferences and journals with nuanced data policies that respect both openness and ownership.