DataScience Workbook / 09: Project Management / 3. Resource Management / 3.1. Data Management / 3.1.1. Open Science policy / FAIR principles

Strategies to ensure research data is Findable, Accessible, Interoperable, and Reusable.

Introduction

The FAIR Principles are key guidelines in data management, widely acknowledged in the scientific community. They advocate for making data Findable, Accessible, Interoperable, and Reusable. These principles are essential in the modern research landscape, enhancing the efficiency and effectiveness of data handling and usage.

The FAIR Principles were designed to improve the way data is handled in research, ensuring that it’s easy for both people and machines to find and use the data they need. These principles have become a standard in the scientific community, helping to streamline data management and maximize the utility of data across different research fields.

In essence, the FAIR Principles are all about making the most out of data in science. They ensure that data is not only easy to find and access but also that it’s set up in a way that others can use it both now and in the future. It’s a framework that encourages scientists to manage data in a way that’s open, clear, and beneficial for the entire scientific community, much like how the advocate for fairness and justice in data governance related to Indigenous communities.

FAIR Principles

The FAIR Principles serve as a roadmap for researchers and data managers, guiding them towards more effective, efficient, and responsible data management practices. By adhering to these principles, the scientific community can ensure that data resources are maximized and beneficial for a wide range of research purposes.

This table outlines the core aspects of the FAIR principles:

acronym	principle	definition	importance
`F`	Findable	Findable data means that it can be easily located by both humans and computers. This is facilitated by the use of unique identifiers and rich, machine-readable metadata.	By making data findable, researchers and machines can quickly locate the specific datasets they need, enhancing the efficiency of research processes.
`A`	Accessible	Accessibility refers to the ability of data to be retrieved and accessed, possibly requiring authentication and authorization.	Ensuring data is accessible allows a broader range of users, including those with disabilities, to utilize the data. Clear accessibility guidelines ensure that data usage is transparent and understandable.
`I`	Interoperable	Interoperability is the ability of data to be integrated with other datasets and to work seamlessly with various applications and workflows.	This facilitates collaborative research, allowing data from different sources to be combined and used in diverse research contexts.
`R`	Reusable	Reusability implies that data can be effectively used in the future by others, and for purposes other than what it was originally collected for.	Enhancing the reusability of data maximizes the value derived from it, promoting efficiency and reducing redundancy in data collection.

Who? When? Why?

The FAIR Principles were developed in 2016 to address the arising challenges of managing the vast amounts of data generated in scientific research, ensuring that this data is handled in a way that maximizes its discoverability, accessibility, and utility for both human and machine use.

Who proposed FAIR principles?

The FAIR Principles were first published in 2016 (The FAIR Guiding Principles for scientific data management and stewardship). They were the result of a collaborative effort by a group of stakeholders including scientists, publishers, funding agencies, and policy makers. The initiative was part of a broader vision to create an infrastructure supporting machine-actionable data reuse, aiming at maximizing the opportunities for efficient discovery and reuse of research outputs. This vision was later termed the Internet of FAIR Data and Services (IFDS).

When were they implemented?

The FAIR Principles were officially introduced in 2016 and quickly gained recognition and endorsement within the scientific community. Their adoption was further accelerated by their inclusion in the European Horizon 2020 program, which mandated adherence to these principles for data published under the program.

Why were these principles implemented?

1. Facilitating Efficient Data Management
With the exponential growth of data, especially in research and scientific fields, there was a pressing need for principles that could guide the effective management, sharing, and reuse of this data.

2. Promoting Machine-Actionable Data Reuse
The FAIR Principles emphasize the importance of making data not only usable by humans but also by machines. This approach is crucial in managing the increasing volume, complexity, and speed of data creation.

3. Improving Data Discoverability and Accessibility
By making data findable, accessible, interoperable, and reusable, the FAIR Principles aim to enhance the visibility and utility of research data, thereby contributing to scientific progress.

4. Supporting Open Science
The principles align with the Open Access philosophy, promoting a common framework that increases the potential and efficiency of Open Access data and resources.

FAIR in your Research

Why does FAIR matter to scientists?

The FAIR Principles are vital to scientists because they ensure that research data is managed in a way that maximizes its accessibility, utility, and impact. By adhering to the FAIR guidelines scientists can enhance the efficiency, transparency, and collaborative potential of their research, leading to broader scientific innovation and discovery.

Efficient and Transparent Data Management
FAIR principles encourage practices that make research data easily discoverable and accessible, not just for current use but also for future research, increasing data transparency and efficiency in scientific inquiry.
Facilitation of Collaborative Research
By making data interoperable, FAIR principles enable researchers to combine datasets from different sources seamlessly, fostering collaboration and multidisciplinary research.
Enhancing the Impact of Research
Reusable data, as promoted by the FAIR principles, can be repurposed beyond the scope of the original research, multiplying its value and impact in the scientific community.
Compliance with Standards and Funding Requirements
Many funding agencies and scholarly journals now require or highly recommend adherence to FAIR principles, aligning data management practices with global research standards.

FAIR Data and Open Science

Basically, sticking to FAIR principles is a big win for Open Science. It makes research more open, trustworthy, and way more impactful.

Enhancing Open Science through FAIR Principles
The FAIR principles align closely with the goals of Open Science, which aims to make scientific research more transparent, accessible, and collaborative. By ensuring that data is easily findable, accessible, interoperable, and reusable, FAIR principles contribute to making scientific research and data more openly available to the broader scientific community and the public.

By making data easily accessible and reusable, other researchers can validate, replicate, and build upon existing research, thus supporting the reproducibility of scientific findings. This transparency is a cornerstone of Open Science, fostering trust and credibility in scientific research.

By adhering to FAIR principles within the framework of Open Science, researchers contribute to a more efficient and effective use of data. This not only accelerates scientific discoveries but also maximizes the impact and utility of research investments, benefiting the global research community and society at large.

Resources and Tools

There are several tools available to assess “How “FAIR” your data is?”, each offering unique features and functionalities:

F-UJI is an automated tool, developed by FAIRsFAIR. It allows you to assess the FAIR maturity level of a dataset. You simply need to enter the persistent identifier (like a DOI) or a URL of the dataset. F-UJI then evaluates the dataset against the FAIR criteria, providing feedback on areas of compliance and where improvements are needed.

FAIR-Aware This online tool tests your knowledge about making datasets FAIR before uploading them to a data repository. It includes questions enriched with additional information and practical tips about FAIR data.

FAIR-Checker is an automated tool for FAIR assessment. The FAIR-Checker is useful for evaluating online resources like datasets or software applications. It provides a visual representation of the assessment results, including a radar chart and a detailed scores table.

ARDC FAIR Data Self Assessment Tool, created by the Australian Research Data Commons. This tool enables you to determine the FAIRness of a dataset through a series of questions. It provides insights on how to enhance the FAIRness of the data based on your responses.

SATIFYD This self-assessment tool helps you evaluate whether your research datasets comply with FAIR principles. By answering a set of questions, SATIFYD offers guidance on improving the FAIRness of your data.

Orvium is a platform designed to improve the accessibility and transparency of the scientific publication process. It utilizes blockchain technology and artificial intelligence to manage scholarly publications’ life cycles and to ensure data is stored securely and efficiently. Orvium aims to streamline the publication process, making it more open, fair, and beneficial to the scientific community. For more detailed information on FAIR principles assessment, you can visit Orvium Blog.

The table below provides a quick overview of the key aspects of each tool, helping you to choose the one that best suits your needs for assessing the FAIRness of your data.

Feature/Tool	F-UJI	ARDC FAIR Data Self Assessment Tool	SATIFYD	FAIR-Aware	FAIR-Checker
Accessibility	online	online	online	online	online
Automation	automated	manual (requires human input)	manual (requires human input)	manual (requires human input)	automated
Type of Data Assessed	datasets	datasets	research datasets	datasets before uploading to repositories	online resources (datasets, software applications)
Price	Free	Free	Free	Free	Free
Ease of Use	user-friendly, requires basic familiarity with FAIR principles	simple, but knowledge of FAIR principles is helpful	requires understanding of FAIR principles	designed to educate about FAIR principles	user-friendly, automated assessment
Output	visual representation (multi-level pie chart), detailed report	progress bars, score, description for each question	score and tips for improvement	knowledge test with additional information and tips	radar chart, detailed scores, log messages, recommendations
Additional Features	uses external registries and resources for assessment	provides recommendations based on answers	self-assessment with improvement tips	tests knowledge about FAIR data preparation	checks FAIRness maturity, provides suggestions for improvement

Best Practices for FAIR Data Management

Getting your data to play nice with the FAIR principles isn’t just good science — it’s smart science. Here’s how you can make your data more Findable, Accessible, Interoperable, and Reusable, and why it really pays off in the long run.

Practical Steps to FAIRify Your Data

Choose the Right Repositories
Pick data repositories that are known for their FAIR compliance. Think of places where your data can be easily found, accessed, and reused.
For instance, repositories like Zenodo or Dryad are great because they assign DOIs (Digital Object Identifiers) to your datasets, making them super findable and citable.
Craft Solid Data Management Plans
Have a clear plan for how you’re going to handle your data throughout its life cycle. This includes how you’ll store it, share it, and keep it secure. There are tools out there, like DMPTool, that can guide you through this process.
Use FAIR-Friendly Tools
Leverage tools like F-UJI or ARDC’s FAIR Data Self Assessment Tool. They’re like your FAIRness coaches, helping you figure out where your data stands and how to up its FAIR game.

The Long-Term Perks

FAIR data gets noticed. It’s easier for other researchers to find, cite, and build upon your work, which can lead to more citations and recognition.

When your data is easy to access and use, it can be repurposed for new, sometimes unexpected, research avenues. This not only stretches the usefulness of your data but also sparks new collaborations and discoveries.

The world of data is always changing, and staying FAIR-compliant means you’re keeping up. It’s about being adaptable and ready for new data technologies and standards as they come.

FAIR principles

Aleksandra Badaczewska