Separating facts from fiction: dispelling open data myths

Data sharing is often surrounded by misconceptions that can deter researchers from embracing its benefits. It’s time to separate fact from fiction and shed light on the truth about open data. In this guide, we’ll address common myths and give practical advice on how to share your data.

Understanding the diversity of data

Myth: “I don’t have any data!”

Research data takes many forms, including textual, numerical, geospatial, image, audio-visual, and machine-generated data. Even non-digital materials, such as paper documents or analogue recordings, can often be digitised for sharing.

Myth: “Data sharing isn’t relevant in my field.”

While practices differ across disciplines, the advantages of data sharing—reproducibility, credit, and reuse—apply universally. The FAIR principles (Findable, Accessible, Interoperable, Reusable) support all types of research data, and generalist repositories support diverse formats. Increasingly, funders are introducing mandates for data sharing, requiring grantees to abide by funder open data agreements.

Overcoming barriers to data sharing

Myth: “Data sharing is too difficult”

Institutions now provide support through data stewards and librarians, who help with planning and management. Beyond this, many funders have started allowing data management to be included in project budgets. It’s important to give resources for research data management in future proposals.

Myth: “I’m not sure I have the right to share my data”

Collaborating early with stakeholders and using a data management plan helps to clarify rights and responsibilities, ensuring transparency and avoiding any potential conflicts.

Myth: “My data is too sensitive to share”

Sensitive data can often be shared responsibly through anonymization or controlled access. If sharing isn’t possible, publishing metadata records still allows others to discover and understand your work.

For example, you could post a “data codebook” or “data dictionary” in a repository that describes the variables used in your dataset. In this document, you can cite the article in which it appears to connect the data description to the paper. Similarly, you can cite the metadata record in your article as part of a data availability statement, which should also include the conditions under which your data can be accessed.

Addressing concerns about misuse and misinterpretation

Myth: “My data will be misinterpreted.”

Providing clear documentation, such as a data dictionary, helps others understand your dataset. This supports reuse and minimises the risk of misinterpretation.

A data dictionary is a separate file where each variable is defined, including units and ranges, and often includes other useful information for interpreting the dataset. By helping others (and your future self!) better understand your data, a data dictionary supports reuse and reproducibility – and helps avoid misinterpretation.

Myth: “My data will be reused inappropriately.”

Rich metadata and clear documentation prevent misuse and enable tracking of inappropriate use. For sensitive data, data use agreements specify terms for reuse.

Myth: “I’m worried about my data being scooped.”

Data sharing proves ownership through authorship and formal citations. Rather than fearing scooping, researchers can embrace the opportunity to gain recognition and collaborate. When another party reuses your data, you’ll receive credit via a formal data citation.

The broader impact of data sharing

Myth: “My data isn’t useful to anyone else.”

Research data has value beyond academia, helping policymakers, educators, and other stakeholders. Sharing data also helps reduce duplication and encourages integrative analyses.

Myth: “Sharing my data now will impact my ability to publish later.”

 Most journals support data sharing and recognise its value. Additionally, publications associated with shared datasets often receive more citations.

Myth: “I can share my data as a supplementary file or via email.”

For data sharing to reach its full potential, it is important that data is shared as FAIRly as possible. FAIR data principles advocate for preservation in trusted repositories, ensuring data stays accessible and useful in the long-term.

Myth: “Not sharing my data won’t hurt my career.”

Not directly, however data sharing can lead to new collaborations, increased citations, and future career opportunities, particularly as open research gains momentum.

Data sharing is more than a trend—it’s a transformative practice that benefits researchers and the broader research community. By addressing myths and adopting FAIR principles, researchers can unlock the full potential of their data, fostering innovation, collaboration, and recognition.

Learn more about data sharing on HRB Open Research here.