Understanding Open Data

Open Research Europe endorses the FAIR Data Principles, alongside an open data policy, as a framework to promote the broadest reuse of research data. We believe that sharing research data can accelerate the pace of discovery, provide credibility and recognition for authors, and lead to increased public trust in research. This also brings benefits for wider society, including driving innovation in technology, better evidence-based policymaking, and economic benefits. 

What is Open Data?  

Open Data is data that is available for everyone to access, use and share. For researchers, this refers to any information or materials that have been collected or created as part of your research project – such as survey results, gene sequences, software, code, neuro-images, even audio files. In research, open data practices are also known as ‘data sharing’. 

What is FAIR data?  

The FAIR Guiding Principles were published in Scientific Data in 2016, providing a new framework for research data management, designed to maximize its reuse and support open data practices.  
 
FAIR data is Findable, Accessible, Interoperable, and Reusable. FAIR data goes beyond open data, aiming to make the data itself more useful and user-friendly. 

Data collection can seem daunting, especially for early career researchers (ECR)s. Check out these handy tips and tricks to help you navigate this tricky topic, with advice on: 

• Ensuring reproducibility 
• Collaboration for data collection 
• Maximizing data reuse 

1. Prepare your data for sharing

This step is the most time-consuming, but also the most important. Firstly, consider how to make your data as open as possible, and as closed as necessary. Are there any ethical or security issues around sharing your data? Do you need to anonymize your dataset to protect patient or participant privacy? If you’re unsure, contact the Open Research Europe Editorial team for advice. 

Are there subject-specific data standards relevant to your research? If so, make sure your data meets these standards, and that you label your files according to discipline-specific best practice.  

Finally, ensure details of any software that is required to view your datasets is included – if you’ve coded the software yourself, the code should be made openly available too.

2. Select a repository

Your datasets should be deposited in a stable and recognized open repository, under a CC0 license. Your community might have a recognized repository, and some data types (such as genetic sequences or protein structures) have specific data banks they should be deposited in. Struggling to decide which repository is right for your research? Read our Data Guidelines for more information.

3. Add a Data Availability Statement to your article

On Open Research Europe, all articles must include a Data Availability Statement, even when there is no data associated with the article. This statement helps your reviewers and readers find and access the data underlying your results.

4. Link your datasets to your article

Once your article is published, update your repository project with the DOI for your article. Linking your data and your article in this way means they are reciprocally connected, ensuring you receive credit for your work.