Making Your Research Data FAIR with OSF: A Practical Guide

Recent U.S. administration orders and confusion about access to federally funded research, including the removal of public data, have raised concerns about the future of research infrastructure and access to critical research data. The Center for Open Science (COS) remains committed to advancing collaborative open scholarship and responsible data stewardship. We will continue working closely with the research community to support sustainable, transparent data-sharing practices.

As part of this commitment, COS has become a signatory of  the Invest in Open Infrastructure (IOI)'s Statement of Support for a Global Research Ecosystem, joining others in calling for the support of the essential tools and services that enable research worldwide. Open, well-supported infrastructure is critical to ensuring that researchers can share and preserve their work for the long term.

While funding mandates may evolve, making data FAIR (Findable, Accessible, Interoperable, and Reusable) remains a cornerstone of sustainable research infrastructure. Beyond policy compliance, FAIR practices significantly enhance the impact and visibility of research, foster collaborations, and advance scientific discovery.

The Open Science Framework (OSF) offers robust, user-friendly tools to help researchers implement FAIR principles effectively. Use this guide to ensure that your research data is FAIR and maximize the impact of your work, with the help of OSF's features.

Findability: Making Your Data Discoverable

Make it easier for both humans and machines to discover and access your data by enhancing its findability on OSF.

• Use Descriptive Titles: Choose clear, specific titles with searchable keywords that are relevant to your audience. For example, instead of "Music Preference Study", try "Weather-Driven Music Preferences: Analyzing College Students' Playlist Trends Across Seasons (2024)"
  
  
• Add Rich Metadata: Fill out OSF’s metadata fields with detailed descriptions, relevant tags, and keywords. Specify your research field and related disciplines to improve discoverability. Rich metadata ensures that your work is easily discoverable across repositories and search engines. Moreover, including funder and institutional affiliation details facilitates easier reporting and helps meet funder compliance requirements.
  
  One crucial but often overlooked field is resource type, which classifies the kind of materials in your project (e.g., dataset, collection). This field is essential for DOI creation and improved search visibility. On the OSF, you can select a resource type from a dropdown list of choices.
  
  Learn more about adding metadata.
  
  
• Generate Persistent Identifiers: Create a Digital Object Identifier (DOI) for your dataset on OSF. A DOI ensures that your data remains findable and citable, even if its location changes.
  
  Learn more about creating a DOI.
  
  

Accessibility: Ensuring Your Data Can Be Retrieved

There should be a clear process for accessing your data, including information on how to obtain it and any potential restrictions.

• Set Clear Access Permissions: Use OSF’s "Contributors" tab to manage access levels. Adjust permissions based on your data’s sensitivity, promoting open access whenever possible.
  
  For a deeper dive on handling sensitive research data, check out a recording of our previous webinar with researcher Dr. Rebecca Campbell: Sharing Sensitive Qualitative Data.
  
  
• Provide Detailed Documentation: Include a plaintext README file that explains how to access and use your data, details on software or tools needed to open your files, and any access restrictions or requirements. The file should also specify your dataset’s relationships to other files (e.g., code, datasets, manuscripts). A comprehensive README ensures that your data can be utilized and interpreted by others.
  
  
• Make Public Components: Organize your project into components, and publicly share those without sensitive data. This allows partial access to your research, even if some data must remain restricted.
  
  Learn more about components.
  
  

Interoperability: Making Your Data Work with Other Data

Interoperability ensures that your data can be seamlessly integrated with other datasets and systems.

• Adopt Standard Formats: When possible, save data in widely-used, non-proprietary file formats like CSV for spreadsheets, TXT for plain text, and PDF/A for long-term document preservation.
  
  
• Document Variables: Provide a codebook/data dictionary that explains variables, units, and measurements and store it alongside your data files in OSF. If applicable, include information about how variables map to standard ontologies in your field. This enhances consistency with other datasets.
  
  
• Link Add-Ons: Use OSF's integration features to connect your project with other tools and platforms, including repositories like GitHub and citations managers like Zotero. This creates a network of interconnected research objects.
  
  Learn more about add-ons, storage, and API integration.

Reusability: Enabling Future Research

Support future research efforts and help others build on your work by optimizing your data for reuse.

• Include an Appropriate License: Adding a license to your project allows others to copy, distribute, and use your data while allowing you to retain copyright. Use OSF’s license picker to select from a variety of commonly-used licenses, or upload your own. A well-chosen license promotes transparency, fosters reuse, and ensures proper attribution.
  
  Learn more about adding a license to your OSF project.
  
  For tailored guidance on selecting the right license, consider consulting your institution’s academic librarians, who are well-versed in open access and copyright considerations. You can also explore previous OSF webinars for further insights on best practices, including Your Questions Answered: How to Retain Copyright While Others Distribute and Build Upon Your Work.
  
  
• Document Methods Clearly: Include detailed protocols, analysis scripts, and any custom code used in data processing. Thorough documentation enables others to replicate your work and understand the origins of your data. 
  
  
• Version Control Data: Use OSF's version control features to track updates and corrections to your files. This offers a transparent history of changes for future users.
  
  Learn more about version control on OSF.

Quick Tips

• Begin with the End in Mind: Structure your OSF project with FAIR principles in mind from the beginning — even before data collection starts. Data Management and Sharing Plans are also beneficial for determining what steps you’ll need to take to share your data, and when those steps will occur.
  
  
• Use Templates: Create reusable templates, making it easier to maintain consistency across different projects.
  
  
• Find Support: Feeling confused or stuck? Help is out there! Visit the OSF FAQs for comprehensive guidance, join our monthly webinars for guided walkthroughs of key OSF workflows and features, or explore a wealth of informative webinars and hands-on guides on the Center for Open Science Youtube channel.