RDIM Introduction The WHY - benefits of data management and sharing

The WHY - benefits of data management and sharing

Good data management and sharing your data can help maximise the efficiency and integrity of your research, and increase your visibility and impact as a researcher.

More and more funders, publishers and institutions are mandating data storing and sharing, so developing your skills in Research Data Management is essential.

Five key benefits of managing and sharing your data:

Publishing your data makes it discoverable, allowing others to find, cite, and build on your work. Data shared in a repository (such as Research Data JCU) can be formally cited and linked with your publications and profiles.

💡Evidence shows a citation advantage for publications that include DOI links to datasets — one study found an increase of up to 25.36 % in citation rates:
Colavizza G, Hrynaszkiewicz I, Staden I, Whitaker K, McGillivray B (2020) The citation advantage of linking publications to research data. PLoS ONE 15(4): e0230416.  https://doi.org/10.1371/journal.pone.0230416

Data sharing increases your visibility by enabling:

  • Mutual visibility between your datasets and publications through DOI links and  repository records.
  • Recognition through data citations in papers and researcher profiles (e.g., ORCID iD) alongside citations for more "traditional" research outputs.
  • Tracking and metrics — for example, via DataCite Commons, which connects publications, datasets, people, and institutions through DOI relationships.
  • Broader engagement — with Altmetric tools can capture online attention and discussion around your datasets and related publications.

A culture of data sharing fuels collaboration and innovation across disciplines. Making your data available allows researchers to connect ideas, reuse resources, and build on each other’s work.

Data sharing enhances collaboration and innovation by:

  • Enabling new discoveries through the combination and re-analysis of existing datasets.
  • Demonstrating the value of publicly funded research through openness and transparency.
  • Raising researcher profiles and attracting future partnerships or funding opportunities.

Shared data also amplifies research impact:

  • The Data Impacts site and ARDC case studies showcase examples of shared data driving real-world benefits — from protecting ecosystems to shaping public policy.
  • Modern tools for data mining, visualisation, and collaboration make cross-disciplinary research easier than ever.

Organising and documenting your data saves time, reduces frustration, and makes your research process more efficient.
Good data management helps you and your collaborators find, understand, and reuse data — even years after the project ends.

Effective data management improves efficiency by:

  • Maintaining consistency through clear file naming, version control, and folder structures.
  • Choosing sustainable formats that support long-term access and reuse.
  • Capturing clear documentation and metadata to explain your data for future use.
  • Planning for scale by identifying storage, workflows, and resource needs as projects grow.

Data sharing also improves efficiency across the research community:

  • Reducing duplication of effort and data collection.
  • Minimising participant burden by avoiding repeated sampling of small or vulnerable groups.
  • Accelerating discovery through faster access to high-quality data — critical during health or environmental emergencies.

Good data management underpins the credibility and reliability of research. When data are well-documented and preserved, results can be validated, reproduced and built upon with confidence.

Good data practices strengthen integrity and reproducibility by:

  • Supporting validation of published results and enabling others to test your analyses.
  • Ensuring transparency through supporting documentation, including metadata, code and methods.
  • Encouraging peer review of datasets (or “data papers”) to enhance the robustness of results.
  • Preventing misconduct — the Retraction Watch faked-data archive shows how poor data management can undermine trust.

Effective data management helps you meet funder, publisher, and institutional requirements while protecting participants and intellectual property.

Good data management supports compliance and security by:

  • Ensuring secure storage and backup to protect against loss or damage.
  • Maintaining ethical and legal compliance with privacy/ethics, copyright/IP, and data-use agreements.
  • Providing transparent governance aligned with funder, ethics, and institutional policies.

At JCU, HDR students and researchers must comply with the JCU Code for the Responsible Conduct of Research (based on the National Code), as well as:

  • Funder policies (e.g. ARC, NHMRC).
  • Privacy and ethics protocols.
  • Publisher data-availability policies (e.g. PLoS, PeerJ, Nature Springer).
  • HDR requirements for Confirmation of Candidature and Thesis Submission.

Appropriate storage and access controls also prevent unauthorised use and protect sensitive or confidential information.

Data Sharing: Overcoming the Barriers

Here are some common barriers to sharing data and some possible solutions:

Your dataset certainly may have value to future research! It is also very hard to anticipate what data may be sought after by future researchers. Even so-called "niche" data can be interesting or useful to others, including researchers from other disciplines. The many datasets collected before "climate change" became a critical research field — that have since become invaluable — are an obvious example.

Providing good documentation and contextual information for your project and data will help other researches understand your data, and use it correctly. Publishing your data could be a good way to counter wilful misinterpretation of your data as you can quickly point to the real data on the web to refute this. If data are sensitive or likely to be misinterpreted you also have options for controlling access (see 'My data is too sensitive to share').

You have a competitive advantage in that you understand your data better than others - even with the best metadata descriptions. Other researchers should cite your data but if you are concerned about others analysing it before you publish you can often embargo your data pending publication(s). Metadata/data repositories such as Research Data JCU can assist you with this.

Sharing sensitive data can often be made possible with a combination of informed consent, anonymisation and controlling access to the data, as outlined in this website. Making anonymised data available via negotiated access can be a good option for sensitive data. It allows you to retain oversight e.g. you can make sure requestors are genuine researchers, that they will maintain confidentiality and security and you can discuss how they plan to use your data. You can also consider making some of your work public while restricting access to other data.

Ideally, you should seek permission from the IP owners early in the research project and/or use data that is licensed for re-use. Sometimes it can be difficult to tell where data has come from and this "taints" the whole dataset for sharing. If nobody really knows who owns the data try contacting who has management over the area the dataset belongs to and have them assign an owner or give permission. Making the data with clear ownership available while restricting other data can be an option, although in some cases this will destroy the integrity of the dataset and its re-use value.

This is valid concern, particularly when data is difficult and time-consuming to prepare, describe and/or share - and this varies across the disciplines. Planning and generating good documentation during the Research (Data and Information) Asset Lifecycle can help mitigate this. The eResearch Centre provides storage and data curation services through Research Data JCU and can assist.

The lack of reward for time invested in archiving and sharing data (see #6) is a recognised barrier to best practice Research Data Management. As Couture et al. (2018) suggest "personal incentives such as data citations should be more widely used to increase the impact of a particular dataset and provide recognition or credit for data creation." Assigning DOIs allows data to be tracked and cited in the same way as publications. See the DOIs and Data Citation section of this website for more information. As data citation becomes more routine citations may be incorporated into research evaluation and reward practices - see for example, the DORA (Declaration on Research Assessment). There can also be a citation advantage for publications associated with open data.

Adapted from:

Closed Data … Excuses, Excuses (blog post from the University of California Curation Centre (UC3) accessed 2 January 2018 and UK Data Archive‘s list of barriers and solutions to data sharing, available from the Digital Curation Centre‘s PDF, RDM for Librarians, pp. 14-15.)

Couture JL, Blake RE, McDonald G, Ward CL (2018) A funder-imposed data publication requirement seldom inspired data sharing. PLoS ONE 13(7): e0199789. https://doi.org/10.1371/journal.pone.0199789.