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Open data is a central part of open science, but data sharing can be difficult for researchers unfamiliar with open data practices. In this blog we provide some useful tips and guidance that will help you share data effectively.

Write a Data Management Plan

A Data Management Plan (DMP), or Output Management Plan, allows you to identify, from the outset, before you even begin your research, the types of data you may collect, create, or reuse throughout the project. 

Creating a Data Management Plan and using the information within it will help inform how you carry out your research and how you deal with data at each project step. This can help to: 

• Identify any data sharing issues in advance 
• Provide adequate time to implement alternative data sharing measures 
  • Allow you to obtain guidance from institutions, ethics boards, or funders before starting the time-sensitive publication process. 

There are lots of resources available to help researchers write DMPs, including: 

• Practical Guide to the International Alignment of Research Data Management 
• DMP Tool 
• Digital Curation Centre (DCC) 

A thorough DMP should do the following:  

1. Identify related data policies, including privacy policies, ethical policies, and other legal requirements. 
2. Identify the types of data you will collect or use, including sensitive or third-party data, how it will be collected or created, and quality control measures being implemented for data collection (especially in relation to sensitive data or third-party IP). 
3. Identify the data storage requirements for this data, including formats of data, where it will be stored, volumes for storage, version control, and how it will be backed up and accessed. 
4. Consider what information will be needed for the data to be interpreted and reused in the future, identifying how the data will be documented, and what metadata will accompany the data. 
5. Describe how you propose your data to be shared, including where, how, and to whom it will be made available. The methods used to share data will be dependent on a variety of factors including, the type, size, complexity, and sensitivity of data. 
6. Identify the roles and responsibilities for all activities including data capture, metadata production, data quality, storage and backup, data archiving, and data sharing. Consider who will be responsible for ensuring relevant policies will be respected and name individuals.  

Apply an open license 

One of the most effective ways to communicate data reuse permissions to potential reusers of data is to apply an appropriate license. If you don’t apply an open license to your research data, the default position of “all rights reserved” will apply to your work, which prevents other researchers from reusing your data. By applying a license, you make your expectations around reuse clear and place the obligation to respect your wishes and rights on the user. 

Applying an open license to your dataset is a straightforward process: 

First, choose which license best suits what you want others to do with your dataset. Your choice of license depends on the following: 

• The type of research data 
• The extent of reuse you wish to allow 
• Compliance with relevant funder, institution, or government policies 

Additionally, if you plan to submit your paper to a specific publisher, they may require you to apply a specific license to your work. For example, to submit your work to HRB Open Research, you must have applied a CC-BY license to your work. 

Find out more about applying different types of licenses.  

If you have generated software or code, this should also be licensed openly so that other users can reuse it. You should ideally choose a license approved by the Open Source Initiative (OSI) to enable reuse. Popular OSI licenses include MIT, GNU General Public License, and Apache License 2.0.  

Once you have chosen your license, it’s important to display this clearly in the repository. 

Choose a suitable repository

A repository is an online storage infrastructure for researchers to store data, code, and other research outputs for scholarly publication. An open access data repository openly stores data, including scientific data from research projects in a way that allows immediate user access to anyone.  

Depositing your data in a publicly accessible, recognized repository ensures that your dataset continues to be available to both humans and machines in a usable form. 

Deciding on the right repository requires authors to consider a few things: 

1. Are you working with sensitive data? If so, you will need to consider a controlled access repository. Some of the repositories that allow you to limit access to your data include: FigShare, Zenodo, and OSF. 
2. Are you working in a particular research area? If so, you may want to use a specific repository for your work. Your funder, institution, or research partners may be able to suggest an appropriate repository. Some specifics include National Addiction & HIV Data Archive Program, Cancer Imaging Archive, NeuroVault or Protein Data Bank. 
3. Does your institutional repository accept data? Many institutions offer support by providing repository infrastructure to their researchers for managing and depositing data, rather than having to source their own repository. 

Find more repositories recommended by HRB Open Research. Additionally, the repository finder tool, developed by DataCite allows you to search for certified repositories that support the FAIR data principles. 

Make your data easy to find 

Once your dataset has been published in your chosen repository, it’s important to boost its visibility by citing it in your related article when published. By reciprocally linking both the paper and the repository, your work can be found by prospective readers through either source. 

Additionally, including a Data Availability Statement with your article tells the reader how, where, and under what conditions the data associated with your research can be accessed and reused, which can again boost readership of the data itself. 

Finally, you could also choose to publish a Data Note to maximize the potential of your research data. Data Notes are a peer reviewed article type that indicates why and how your data was collected, analyzed, and validated. 

Support for sensitive data

It’s important to note that researchers working with sensitive data need to take even more care when sharing their research data. To help, we explored the ways in which researchers can share sensitive healthcare data openly but safely. 

Next steps 

If you’d like to find out more about the benefits of sharing data, visit our Data Notes resources page. 

And if you’re ready to join the thousands of HRB-funded researchers already publishing their work with HRB Open Research, submit your research for publication today.