Open Science and FAIR principles
Open Science is a modern research approach that promotes free, open and responsible access to scientific information, data, software and other research results for the whole society. It not only facilitates knowledge transfer and innovation, but also strengthens public trust in science.
Open Science covers the entire research life cycle – from idea and data collection, to publication, sharing and data reuse.
The ethical principles of Open Science are based on fairness, transparency and responsibility. Scientific information should be as accessible as possible, while ensuring data quality and personal data protection, however, open science does not always automatically mean that all results are fully freely available, especially in cases where they may infringe on the rights of individuals. At the same time, when results are not freely available, there should be clear and enforceable access conditions.
Why is open science important?
- Increases the transparency of research methodology – research data, methods and results are available for review.
- Improves the reproducibility of results – other researchers can repeat the research and verify the results.
- Increases the social relevance and public impact of research – open science helps research results reach a wider audience and benefit society.
- Saves researchers time and resources by reducing duplication, for example in the design of identical studies.
- Promotes international and interdisciplinary collaboration – sharing data and tools facilitates international and interdisciplinary collaboration.
- Increases public trust in science – the public sees how and why scientific conclusions are made.
- Base principles of open science (8 pillars)
These elements form the basis of European open science policy and practice.
1. FAIR principles - data should be:
- Findable;
- Accessible;
- Interoperable;
- Reusable.
2. Research integrity - scientific integrity, accuracy, objectivity and accountability.
3. Next-generation metrics - alternative ways to measure research impact (not just by number of publications or journal impact factor).
4. The future of scientific communication - moving towards open peer review, publishing data alongside articles, dynamic and living papers.
5. Citizen Science - citizen participation in data collection, research design or analysis, interpretation.
6. Education and skills - educating researchers and students on open science issues.
7. Recognition and reward through awards and career development systems that support open science practices.
8. European Open Science Cloud (EOSC) - a digital infrastructure that provides reliable, secure and uniform access to research data, tools and services across Europe.
- Components of Open Science
- Open Access Publications - Scientific articles are freely available to the public, providing unrestricted access to scientific research findings free of charge, for example, published in Open Access journals or institutional repositories.
- Open Research Data - Carefully documented data that is available to other researchers for reanalysis or for new research (for example, published in institutional repositories).
- Open Source Software - Scripts, codes, and tools published under open licenses that help review analyses, adapt methods, and promote reproducibility.
- Open Lab Notes - Experimental logs or documentation available to others.
- Open Peer Review - Reviewer comments are publicly available to promote transparency and openness.
- Open Educational Resources - Free materials for learning.
- Citizen Science - Public participation in research, such as data collection.
- Useful resources on Open Science
Latvia's Open Science Strategy for 2021–2027
The European Union's Open Science Strategy
The Open Science Training Handbook
OpenPlato: All courses | OpenPlato
What is Open Science? | ORION Open Science
UNESCO Recommendation on Open Science - UNESCO Digital Library
The FAIR principles were formulated in 2016 to provide guidance to data developers and publishers. The aim is to ensure that research data can be used as widely as possible – to accelerate scientific discovery and benefit society. The FAIR principles are a set of actions to make data discoverable, accessible, interoperable and reusable.
FAIR places particular emphasis on improving the ability of machines to automatically find and use data, while also increasing the possibilities for reuse for researchers.
Following the FAIR principles in the data management process promotes the impact of research.
- Helps colleagues and yourself to understand the data of relevant research projects in the future (good data documentation, long-term availability).
- Promotes data sharing and collaboration.
- Increases the visibility of research, which increases citations.
- Improves the transparency, credibility and reproducibility of research.
- Prevents data loss.
- Ensures compliance with the requirements of research funders and publishers.
By ensuring that your data complies with the FAIR principles, you will continue to be able to easily find, access and reuse your data. The researcher is perhaps the first and most important beneficiary of making their data FAIR.
- Explanation of the acronym FAIR
- Findable
This means that data can be discovered by both humans and machines, for example by creating meaningful metadata (data about the data) and keywords that are accessible to search engines and research data catalogues. Data are found by unique and persistent identifiers, such as DOIs or handles, and the metadata includes the identifier of the data being described.
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata
F3. clear association between metadata and data
F4. (meta)data are registered or indexed in a search resource
- Accessible
This means that the data is archived for long-term storage and can be made accessible using standard technical procedures. This does not mean that the data must be accessible to everyone, but there must be information available on how the data can (or cannot) be retrieved. For example, the data can be marked with Access only with the express permission of the author and include the author's contact information. However, ideally, information on the availability of the data can also be read by machines, for example using machine-readable standard licenses.
A1. (meta)data can be retrieved by their identifier
A1.1 The format is open, free and universally implementable
A1.2 Access may include an authentication or authorization procedure, if necessary
A2. Metadata is available even if the data is no longer accessible
- Interoperable
This means the exchange and use of data in different programs, tools and systems - also in the future, for example by using open file formats. The data can be integrated with other data from the same or from other research fields. This is possible by using metadata standards, standard ontologies and controlled vocabularies, as well as links between the data and other digital research objects.
I1. (meta)data are described using a formal, accessible and generally accepted approach to representing knowledge
I2. (meta)data use vocabularies that comply with the FAIR principles
I3. (meta)data contain qualified references to other (meta)data
- Reusable
This means that the data is well documented and organized, and provides rich information about the context in which the data was created. The data should meet community standards and have clear terms for access and reuse, preferably under machine-readable standard licenses. This allows others to both evaluate and validate the results of the original research, thus ensuring the reproducibility of the data, and to develop new projects based on the original results. Reusable data encourages collaboration and avoids duplication of work.
R1. Meta(data) is richly described with accurate and relevant attributes
R1.1. (meta)data is published with a clear and accessible data usage license
R1.2. (meta)data can be traced back to its origin
R1.3. (meta)data complies with domain-specific community standards
- Useful resources on the FAIR principles
Why FAIR - FAIR data principles
The FAIR Guiding Principles for scientific data management and stewardship | Scientific Data
How to get started – three easy steps
- 1. Create a Data Management Plan (DMP)
A data management plan is a living document that specifies what data will be used in a research project and how it will be processed, stored and archived. Preparing a DPP is an important first step in ensuring that data complies with the FAIR principles. It is also a frequent requirement from funders. RSU researchers have access to the RSU DPP template for this purpose, which can be filled out in the online system Argos.
- 2. Describe and document your data
To make data easily findable, it needs appropriate metadata. This can include keywords, references to related documents, researchers' ORCID identifiers, and research grant numbers. It is also important to prepare documentation that explains how the data was created, structured, and processed. This will allow the data to be not only findable, but also reusable.
For questions about metadata and documentation, contact the data curators at datukuratori
rsu[pnkts]lv.To facilitate the process of depositing data in RSU Dataverse, complete the minimum metadata survey.
- 3. Make data available in a trusted repository
When the data is ready for publication, choose a repository that:
- assigns a permanent identifier to both the data and metadata;
- adds metadata to the data according to standard metadata schemas;
- grants a license to the data;
- provides access to the data and provides metadata using an open and standard communication protocol (e.g. http or XML).
Adhering to these criteria ensures that the data complies with many, if not most, FAIR principles. RSU has the RSU Dataverse repository available for this purpose, which meets all of the above conditions. It can store datasets up to 50 GB free of charge.
