Findability 

• Has a unique, permanent identifier, such as DOI.
• Has a lot of descriptive metadata (data about the data).
• Indexed in a searchable resource, such as a data repository.

To make data/code searchable:

• Store your data in a repository that provides a DOI.
• Cite it in publications (using the DOI).
• Make sure it is fully documented – fill in all relevant fields when depositing and include a README file.

Accessibility 

• It can be obtained using a standard, free and open protocol that allows authentication where necessary.
• Metadata is available even where there is no data.

To make data/code available:

• Use the appropriate repository for your data/code
• If you cannot share your data/code, create a record with only metadata.

Interoperability 

• Can be integrated with other data, programs, and workflows.
• Use of open or common file formats.

To make data/code compatible:

• Check whether standard or open file formats are used
• Use standard and accessible vocabulary.

Reusability

• Published with a license that specifies how it can be reused, such as a CC license.
• In accordance with public standards.
• Clearly documented.

To make the data/code reusable:

• Use a Creative Commons license for data
• For open source software, select the appropriate license here
• Provide as much information (metadata) about the data as possible when submitting to the repository
• Include a README file to explain and contextualize the data.

These tools help researchers, librarians, and data managers assess whether data complies with FAIR principles:

• • F-UJI
    An automated service for assessing the FAIRness of datasets developed in accordance with EOSC requirements.
    
  • FAIR Evaluator tool
    Manual FAIR Evaluator tool
    Online platforms for checking data or metadata for compliance with FAIR criteria. It works on the basis of international indicators.
    
  • FAIR-Aware
    An easy-to-use researcher self-assessment tool that assesses awareness of FAIR principles and provides advice.
  • ARDC Fair Data self-assessment tool (Australian Research Data Commons)
    Helps researchers assess the extent to which their research dataset is searchable, accessible, interoperable, and reusable. It also provides practical advice on how to improve its FAIRness.
  • Denysenko Scientific and Technical Library of Igor Sikorsky Kyiv Polytechnic Institute (2024). Tools for assessing data for compliance with FAIR principles [Video]. YouTube.

FAIR principles are not just a theoretical concept – thousands of scientists, institutions, and projects have already implemented them in their daily practice.

• Successful cases in scientific institutions

Leiden University, Netherlands

The university has integrated FAIR policy into all stages of the research process, from project planning to data storage. Thanks to the role of “data steward,” each department has an expert who helps researchers implement FAIR tools, including DMPs and registration in repositories.

EMBL-EBI (European Institute of Bioinformatics)

FAIR is used as a prerequisite for uploading biological data to repositories. As a result, the data is easily accessible through APIs, metadata is standardized, and reuse is the basis for machine analysis.

National research institutions of Spain(CSIC)

All researchers are required to submit a FAIR-compliant Data Management Plan when submitting projects. In addition, an open system has been created to assess the FAIR compliance of publications and data.

• Experience in international projects

EOSC-Pillar (EU)

The project helped Central European countries build national infrastructures for FAIR data. Training modules, standards, and pilot repositories were developed to test automated FAIRness verification.

FAIRsFAIR (2020–2022)

The project has been a key driver of policy harmonization, development of FAIR indicators, and training initiatives. Hundreds of universities and libraries have been trained, and standardized FAIR repository assessments have been developed.

ENVRI-FAIR

The project in the field of environmental research integrated FAIR into more than 20 scientific infrastructures (including satellite observations, hydrology, meteorology). Common meta-descriptive models and cross-system APIs have been created.

The CARE Principles are a set of ethical guidelines developed to ensure the fair, responsible and respectful use of data. The CARE Principles were developed by the Global Indigenous Data Alliance (GIDA) in 2019 and are already being actively integrated into open science policies

The name CARE is an acronym that stands for:

C — Collective Benefit 

Data should be used in a way that benefits the very communities from which it comes. This includes local capacity building, economic growth, cultural support, and innovation that serves the community.

A — Authority to Control 

This principle includes the right to intellectual property.

R — Responsibility

Researchers, organizations, and institutions have a responsibility to act responsibly, respectfully, and transparently when working with data that has cultural, historical, or social significance for communities. This also includes the participation of community representatives in all stages of data work.

E — Ethics 

All actions with data should be ethical and take into account social, cultural and historical contexts. This means avoiding harm, respecting consent, respecting traditional knowledge, and sharing benefits equitably.

While FAIR principles focus on technical accessibility and reuse of data, CARE principles emphasize social responsibility and the ethical context of data. Both approaches are not contradictory but complementary:

• FAIR — about machines and metadata

• CARE — about people and communities

Increase the visibility and citation of publications and data

FAIR principles provide for the assignment of unique identifiers (e.g., DOI), the provision of a full meta-description, and the placement of data in open repositories. This allows:

• Ensure that data and publications are indexed in search engines (Google Scholar, DataCite, OpenAIRE).
• Increase discoverability for other researchers looking for related resources.
• Create opportunities for additional citations not only of publications, but also of datasets, codes, or models themselves.
• Strengthen the scientific reputation of the author/institution through transparent and openly accessible research.

Ensuring reproducibility of research

FAIR promotes scientific integrity by ensuring that:

• Documenting the processes of data collection, processing, and analysis.
• Storing data in formats that allow other researchers to verify and replicate the results.
• Including metadata and related code that allows for full reconstruction of the experiment or analysis.
• Use of open tools and environments to ensure transparency of the research process.

Promoting international cooperation and data exchange

FAIR data can be easily integrated into global platforms, facilitating:

• Facilitating the sharing of data between research teams, even across countries and disciplines.
• Interoperability – thanks to agreed standards of format, structure, and description.
• Opportunities to participate in international projects and grants that increasingly require open access to data (e.g., Horizon Europe).
• Avoidance of duplication of efforts – if data is already available, other teams can reuse it instead of conducting an identical collection.

Facilitating analysis, integration and innovation based on open data

FAIR makes scientific data “machine-readable,” which ensures:

• Automated collection and processing of large amounts of data using algorithms, AI, and analytical platforms.
• Combining data from different sources into complex arrays for interdisciplinary analysis.
• Emergence of new research questions due to access to a large array of open, comparable, and structured data.
• Innovations in such fields as bioinformatics, social analytics, agricultural technologies, machine learning, etc.

Compliance with open science, ethics and licensing standards

FAIR principles are closely related to the ethical and legal use of research results. They are:

• Require a clear indication of the terms of data use (for example, through Creative Commons licenses).
• Provide traceability of data origin, which is critical for copyright, privacy, and GDPR compliance.
• Facilitate institutional management of scientific results, in particular in the context of state policy in the field of science.
• Support the responsibility of researchers for the quality and transparency of created resources.

Time constraints may hinder the implementation of FAIR principles for data and code. To minimize resource and time consumption, these aspects should be considered as early as possible in the research planning process. When applying for project funding, it is advisable to include time and resources (possibly a research assistant) to support FAIR practices.

Another significant obstacle for researchers is a lack of understanding of FAIR principles and their application to a particular project. There may be questions and concerns about the specifics of the data, such as how to deal with sensitive data or extremely large amounts of information? What to do with data obtained from third parties?

If you have any questions or need help and support on FAIR and research data management, contact the data management specialist of the KPI Library: data.library.kpi@gmail.com.
The Library conducts workshops for faculty to introduce the principles of good data governance, including FAIR, and provide practical advice on how to implement them. Recordings of these workshops are available on the YouTube channel of the KPI Library.