Open Science in the European Research Area: A Guide to Compliance and Best Practices

Open science in the European Research Area refers to the set of policies and practices that make research outputs, methods, and data accessible, transparent, and reusable. For researchers funded by Horizon Europe, the EU's ninth framework programme for research and innovation, open science is not optional. The programme mandates open access to publications and applies open science principles across funded projects, with a budget of EUR 95.5 billion supporting this approach. This guide explains the specific requirements you must meet, the practical steps for compliance, and the common pitfalls that lead to non-compliance findings during grant reporting.

Understanding the European Open Science Policy Framework

The European Union has built its open science policy around the principle that publicly funded research should produce publicly accessible results. Horizon Europe, which runs until 2027, carries forward the three-pillar structure of its predecessor Horizon 2020: Excellent Science, Global Challenges and European Industrial Competitiveness, and Innovative Europe. The programme introduces several novelties compared to earlier frameworks, including mission areas, European partnerships, open science obligations, and gender equality measures. The Cancer Mission is the first of five mission areas established under the programme.

Open science policy under Horizon Europe is mandatory. This means that grant agreements include binding obligations for beneficiaries, also recommendations. The mandatory elements cover open access to peer-reviewed publications, responsible management of research data, and adherence to open science principles throughout the research lifecycle. Researchers who fail to meet these obligations risk having their costs declared ineligible or facing reduced grant payments.

The policy framework operates alongside national and institutional requirements. Each EU member state implements the European Open Science Cloud and national open science strategies differently, which creates a layered compliance environment. A researcher based in Germany, for example, must satisfy both Horizon Europe requirements and the German national open access strategy. Understanding which requirements apply at each level is the first step toward building a compliant workflow.

At a Glance: Core Open Science Obligations for EU-Funded Research

Core Principles of Open Science Compliance

Open Access to Publications

Open access means that anyone can read, download, and reuse your published research findings without payment barriers. Horizon Europe requires that beneficiaries ensure open access to all peer-reviewed scientific publications relating to their project results. The requirement covers the final published version or the accepted manuscript, and it applies regardless of whether you publish in a subscription journal or an open access journal.

The practical implementation involves depositing your publication in a trusted repository. For most researchers, this means choosing between a subject repository, an institutional repository, or a generalist repository such as Zenodo. The deposit must happen at the time of publication or within the embargo period specified in your grant agreement. You must also ensure that the publication carries a license that permits reuse, with Creative Commons Attribution (CC BY) being the preferred option.

The NCBI Literature Resources and PubMed platforms illustrate how open access infrastructure supports biomedical research discovery. PubMed, operated by the National Library of Medicine, provides free access to millions of biomedical citations and abstracts, and it links to full-text versions where available. Depositing your publications in repositories that feed into such discovery systems increases the visibility and potential impact of your work.

Research Data Management and the FAIR Principles

Research data management under Horizon Europe centers on the FAIR principles: Findable, Accessible, Interoperable, and Reusable. Your Data Management Plan must describe how your project data will be handled throughout its lifecycle, from collection through processing, analysis, preservation, and sharing.

The Research Data Framework developed by the National Institute of Standards and Technology provides a structured approach to thinking about data management. While this framework originates in the US standards community, its emphasis on defining data roles, documenting data provenance, and establishing quality controls translates directly to the planning work required for EU compliance. The framework helps you identify who is responsible for each data asset, what documentation is needed, and how data quality will be maintained.

A compliant Data Management Plan addresses several specific elements. It identifies the datasets your project will produce or reuse. It describes the standards and formats used for data collection and processing. It specifies which data will be shared openly and which will remain restricted, with justification for any restrictions. It names the repositories where data will be deposited. It assigns responsibilities for data management tasks to named individuals. It describes the resources needed to implement the plan.

Preregistration and Research Transparency

Preregistration involves registering your study design, hypotheses, and analysis plan before you collect data. This practice distinguishes confirmatory analyses, which test pre-specified hypotheses, from exploratory analyses, which search for patterns without prior specification. For hypothesis-testing research, preregistration provides a timestamped record that protects against questionable research practices such as selective reporting and p-hacking.

The Experimental Design Assistant from the NC3Rs offers a practical tool for planning animal research studies. It guides researchers through the design process, helping to ensure that experiments are adequately powered, properly randomized, and appropriately blinded. While the tool is designed primarily for animal research, its underlying logic applies to any study where experimental design quality affects the validity of conclusions.

The EQUATOR Network provides reporting guidelines for health research. These guidelines specify what information must be included in research reports to ensure transparency and reproducibility. Using the appropriate reporting guideline for your study type is a concrete step toward open science compliance, because it ensures that your methods and results are described completely enough for others to evaluate and replicate.

Practical Steps for Building a Compliance Workflow

Step 1: Review Your Grant Agreement and Call Conditions

Your grant agreement is the primary legal document defining your open science obligations. Read the specific articles covering open access, research data management, and open science practices. Note any call-specific conditions that add requirements beyond the general framework. Some calls may require specific repositories, particular licenses, or additional deliverables related to open science.

Create a compliance calendar that maps each obligation to a specific date. Include the Data Management Plan submission deadline, expected publication dates, and project milestones where data sharing becomes relevant. Assign responsibility for each obligation to a named team member. This prevents the common failure of open science tasks falling between project roles.

Step 2: Write Your Data Management Plan Early

Do not wait until the formal deadline to begin your Data Management Plan. Start drafting it during the proposal stage, because the plan you submit with your proposal shapes your project's data infrastructure. The plan should identify the data types your project will generate, the volume of data expected, and the storage and backup arrangements needed.

Use the template provided by the European Commission or your funding authority. The template asks specific questions about data discoverability, accessibility, interoperability, and reusability. Answer each question concretely, naming specific repositories, formats, and standards. Vague answers such as "data will be shared where possible" do not satisfy the requirement for a concrete plan.

Step 3: Select Repositories Before You Generate Data

Repository selection should happen at the project planning stage, not after data collection begins. The choice of repository affects how your data are formatted, what metadata are required, and how access is controlled. For sensitive data, you need a repository that supports restricted access with a clear application process.

Consider the distinction between domain-specific and generalist repositories. Domain repositories, such as those maintained by scientific societies or research communities, offer metadata standards tailored to your field. Generalist repositories accept any data type and provide basic preservation services. Your Data Management Plan should name the specific repository for each dataset and explain why that repository was chosen.

Step 4: Establish Documentation Standards

Open science compliance depends on documentation quality. Establish file naming conventions, version control procedures, and metadata standards before data collection begins. Create a data dictionary that defines every variable, its units, and its coding scheme. Document any transformations applied to raw data so that downstream users can understand the provenance of derived variables.

The Research Data Framework emphasizes the importance of defining data roles and responsibilities. Assign a data steward for each project work package. This person ensures that documentation standards are followed, that data are backed up according to the plan, and that deposits happen on schedule.

Step 5: Plan for Publication and Deposit

When you begin writing a manuscript, identify the repository where the accepted version will be deposited. Check the journal's open access policy to understand what version can be deposited and whether an embargo applies. Confirm that the repository you plan to use accepts the version you intend to deposit.

Deposit your publication at the time of acceptance, not after the article appears. This ensures that the record exists even if the journal's production process is delayed. Include the project grant number in the deposit metadata so that the connection between the publication and the funded project is clear.

Step 6: Link Publications to Underlying Data

Open science compliance requires more than depositing publications and datasets separately. You must link them so that readers can find the data underlying a publication and data users can find the publications that used the data. Include dataset identifiers in your publication's data availability statement. Include publication identifiers in your dataset metadata.

This linking serves both compliance and scientific purposes. It allows reviewers and readers to verify your findings by examining the underlying data. It increases the visibility of your datasets, because readers of your publications will discover them. It also supports the broader goal of making research reproducible.

Options and Tradeoffs in Open Science Implementation

Green Open Access versus Gold Open Access

Green open access involves depositing your accepted manuscript in a repository, often after an embargo period. Gold open access involves publishing in a journal that makes articles freely available immediately, usually with an article processing charge. Horizon Europe allows both routes, but the practical implications differ.

Green open access is generally less expensive but may delay public access to your findings. The embargo period, typically 6 to 12 months for subscription journals, means that your work is not immediately available to all readers. Gold open access provides immediate access but requires funding for article processing charges. Your grant budget may include funds for these charges, but you should confirm this before committing to a gold route.

Some journals offer hybrid models, where individual articles in a subscription journal are made open access for a fee. These models can be more expensive than pure gold journals and may not provide the same reuse rights. Check the specific license terms before choosing a hybrid option.

Data Sharing: Full Open Access versus Controlled Access

Not all research data can be shared openly. Human participant data, commercially sensitive data, and data with security implications require controlled access. Your Data Management Plan must justify any restrictions on data sharing and describe how access will be managed.

Controlled access typically involves a data access committee that reviews applications from researchers who want to use the data. The committee evaluates whether the proposed use is consistent with participant consent and ethical approvals. This process adds administrative burden but enables data sharing that would otherwise be impossible.

The key compliance point is that restricted access must be justified and documented. You cannot simply state that data are sensitive without explaining the specific reasons and the access mechanism. Reviewers will check whether your restrictions are proportionate to the actual risks.

Preregistration: Full Protocols versus Analysis Plans

Preregistration can range from registering a brief analysis plan to registering a full study protocol. The appropriate depth depends on your research context. For clinical trials, full protocol registration is mandatory under EU regulations. For basic research, a shorter preregistration that specifies hypotheses and analysis plans may be sufficient.

The Experimental Design Assistant helps researchers think through experimental design decisions before data collection. Using such tools improves the quality of your preregistration because it forces you to specify your design choices explicitly. The tool also helps identify design weaknesses that could undermine your study's validity.

Observations and Measurements for Compliance Tracking

What to Track

Maintain a compliance register that records each open science obligation and its status. For each publication, record the deposit date, repository, version deposited, and license. For each dataset, record the repository, DOI, access level, and any access restrictions. For each preregistration, record the registration date, registry, and version.

This register serves multiple purposes. It provides evidence for grant reporting. It helps you identify obligations that are falling behind schedule. It creates institutional memory so that staff changes do not result in lost compliance information.

How to Measure Compliance

Compliance measurement should focus on verifiable outputs instead of intentions. For open access, the verifiable output is a repository deposit with a persistent identifier. For data management, the verifiable output is a Data Management Plan that names specific repositories and standards. For preregistration, the verifiable output is a registration record with a timestamp.

The Mapping the Landscape of Open Access Dashboards dataset demonstrates how open access progress can be tracked at institutional and national levels. This dataset identifies over 60 dashboards that monitor open access publication rates across research-performing organizations and countries. While these dashboards operate at a macro level, the same logic applies to project-level tracking. You can measure the proportion of your project's publications that are openly accessible and the proportion of datasets that have been deposited.

Records You Should Maintain

Keep the following records for each project:

• The grant agreement and any amendments
• The approved Data Management Plan and all subsequent versions
• Repository deposit confirmations for publications and datasets
• Persistent identifiers for all deposited outputs
• Correspondence with repository administrators
• Records of any access restriction decisions and their justifications
• Preregistration records and any amendments to registered plans

These records should be stored in a location accessible to all project team members. They should be organized so that a new team member can understand the project's compliance status without extensive briefing.

Common Failure Patterns and How to Avoid Them

Failure Pattern 1: Treating the Data Management Plan as a One-Time Document

Many researchers write a Data Management Plan at the start of the project and never update it. This fails because the plan is meant to be a living document that reflects the project's actual data practices. When the project changes direction, adds new data types, or encounters unexpected data challenges, the plan must be updated.

Avoid this failure by scheduling Data Management Plan reviews at each project milestone. Assign a specific person to check whether the plan still matches actual practices. If the plan and practice diverge, update the plan and document the reason for the change.

Failure Pattern 2: Depositing Publications Late or Not at All

Publication deposit often falls through the cracks because it happens at a busy time in the research cycle. The manuscript is accepted, the team moves on to the next experiment, and the deposit is forgotten. This creates a compliance gap that is difficult to close retroactively.

Avoid this failure by making deposit a step in your publication workflow. When you submit a manuscript, note the expected acceptance date and schedule the deposit task. When acceptance arrives, complete the deposit before celebrating the publication. Some repositories allow you to deposit the accepted manuscript immediately, even if the final version is not yet available.

Failure Pattern 3: Choosing Repositories Without Checking Their Requirements

Not all repositories accept all data types or all publication versions. Some repositories have strict metadata requirements. Some do not accept data with restricted access. Some cannot handle large files. Choosing a repository that cannot accommodate your data creates delays and may require finding an alternative repository late in the project.

Avoid this failure by testing your repository choice early. Deposit a small sample dataset during the first months of the project. Confirm that the repository accepts your file formats, that the metadata schema works for your data, and that the access controls function as needed.

Failure Pattern 4: Ignoring the Link Between Publications and Data

Depositing publications and datasets separately without linking them creates a fragmented record. Readers cannot find the data underlying a publication, and data users cannot find the publications that used the data. This reduces the value of both deposits and may fail compliance requirements that expect clear connections.

Avoid this failure by including data availability statements in all publications and by including publication references in dataset metadata. Use persistent identifiers for both publications and datasets so that the links remain stable over time.

Failure Pattern 5: Assuming Open Science Compliance Is the Same Across All Funders

Different funders have different requirements. What satisfies Horizon Europe may not satisfy a national funder or a charitable foundation. Some funders require immediate open access with no embargo. Some require specific licenses. Some have specific repository requirements.

Avoid this failure by reviewing the open science requirements of every funder supporting your work. If a project has multiple funders, identify the most restrictive requirements and comply with those. Document how you are satisfying each funder's specific conditions.

Quality and Welfare Controls in Open Science Practice

Reporting Guidelines and Research Quality

Open science is also about making outputs accessible. It is also about ensuring that research is conducted and reported to high quality standards. The EQUATOR Network provides reporting guidelines that specify what information must be included in research reports. Using these guidelines improves the completeness and transparency of your reporting, which in turn makes your research more useful to others.

For health research, the EQUATOR Network hosts guidelines for randomized trials, observational studies, diagnostic accuracy studies, systematic reviews, and many other study types. Selecting the appropriate guideline for your study and following it during manuscript preparation ensures that your report includes all essential information.

Experimental Design Quality

The Experimental Design Assistant from the NC3Rs supports researchers in designing rigorous animal experiments. The tool helps identify potential sources of bias, such as inadequate randomization or lack of blinding, and suggests design improvements. Using such tools before data collection improves the validity of your findings and reduces the risk of wasted resources.

Good experimental design is a prerequisite for meaningful open science. Sharing data from poorly designed experiments does not advance knowledge. The open science framework therefore emphasizes both transparency and quality. Your compliance efforts should include attention to experimental design, also to deposit and sharing logistics.

Ethics and Data Protection

Open science compliance must respect ethical and legal obligations, particularly regarding personal data. The General Data Protection Regulation (GDPR) imposes strict requirements on the processing of personal data, including research data. Your Data Management Plan must address how personal data will be handled, including anonymization or pseudonymization strategies and lawful bases for processing.

The European Laryngological Society's consensus on bioethics committee approvals highlights the complexity of ethics review in multicenter European research. The regulatory context combines EU Regulation 536/2014 and GDPR with substantial national variation in research ethics committee structures and procedures. Core challenges include heterogeneity of documentation, ambiguity about single versus multiple approvals, and limited guidance for registry-based and real-world data designs.

For multicenter studies, clarify early whether single-country or cross-border ethics approvals are required. Standardize documentation templates and timelines across sites. Align consent and data-handling procedures with ethics review requirements. These steps reduce the risk of compliance failures that emerge late in the project.

Limitations and Boundaries of Open Science Compliance

What Open Science Cannot Achieve Alone

Open science practices improve transparency and accessibility, but they do not guarantee research quality. A poorly designed study that is fully open is still a poorly designed study. The Panel stacking is a threat to consensus statement validity analysis demonstrates that even consensus statements, which are influential in medicine and public health, can be misleading when panel members are selected to favor a particular position. Open science practices such as transparency about panel selection and conflicts of interest can help, but they cannot substitute for rigorous evidence synthesis.

Similarly, open data sharing does not ensure that data are correctly interpreted. Users of shared data must understand the context, limitations, and appropriate uses of the data. Your documentation should provide this context, but you cannot control how others use your data.

Jurisdiction-Specific Requirements

Open science requirements vary across jurisdictions. The EU-China research cooperation under a de-risking framework analysis examines how the securitisation of knowledge in EU science policy affects international research collaboration. Researchers working with international partners must understand how open science obligations interact with national security considerations and export control regulations.

The Science diplomacy as a foreign policy tool for Turkey article illustrates how research collaboration with the EU carries diplomatic and policy dimensions beyond the scientific content. Researchers engaged in international collaboration should be aware that open science requirements may differ across partner countries and that some data may be subject to national restrictions.

Technical Limitations

Some open science requirements face technical limitations. The Detection, identification and quantification of NGTs in EU authorization procedures open letter examines the gap between legal requirements and scientific feasibility for new genomic techniques. The authors find that no purely technical solution is forthcoming for certain identification requirements and that legislative change is necessary.

This example illustrates a broader principle: compliance requirements may sometimes exceed what is technically achievable. When you encounter such situations, document the limitation clearly, explain what you have done to address it, and escalate the issue to your funding authority early instead of waiting for the final report.

Safety and Regulatory Context

Research Integrity and Public Trust

Open science practices support research integrity by making the research process visible and verifiable. The Making data sharing the norm in medical research article argues that data sharing should become standard practice in medical research. When data are shared, other researchers can verify findings, conduct secondary analyses, and identify errors that might otherwise go undetected.

Public trust in research depends on the perception that research is conducted honestly and transparently. Open science practices contribute to this perception by demonstrating that researchers are willing to expose their methods and data to scrutiny. Conversely, research that is conducted behind closed doors, with data that are never shared, invites suspicion regardless of its actual quality.

Health and Environmental Research Context

The 2024 report of the Lancet Countdown on health and climate change demonstrates the importance of open data for tracking global health threats. The report, which brings together over 300 multidisciplinary researchers and health professionals, monitors the health impacts of climate change. Its findings, including record-breaking heat-related mortality among older adults, depend on access to diverse data sources from around the world.

Similarly, the Global burden of enteric infectious diseases analysis relies on data from systematic reviews, population-based surveys, claims data, and hospital sources across 204 countries and territories. The Mapping drivers of life expectancy change in Asia study uses Global Burden of Disease data to analyze life expectancy trends across 34 Asian countries. These large-scale analyses are only possible when researchers share data openly and when data infrastructure supports integration across sources.

The overlooked burden of persistent physical symptoms article calls for increased research funding and improved research practices in European healthcare. Open science practices support these goals by making research findings accessible to clinicians, policymakers, and patients.

Emerging Research Areas

Open science practices are particularly important in emerging research areas where evidence is still developing. The Fragile promise of psychedelics in psychiatry article examines a field where public interest has outpaced the evidence base. Transparent reporting and data sharing help ensure that clinical recommendations are based on solid evidence instead of enthusiasm.

The Readiness Assessment for AI in Nursing Care Projects study identifies five core dimensions for AI readiness in nursing care: regulatory, processual, technical, social, ethical, and community building requirements. The study's systematic approach to identifying readiness attributes demonstrates how open science methods can support the responsible development of emerging technologies.

Professional Escalation Criteria

When to Seek Help

Some open science situations require professional advice beyond your immediate team. Escalate to your institution's research office, legal department, or data protection officer when you encounter any of the following:

• Uncertainty about whether your data sharing plans comply with GDPR or other data protection regulations
• Requests from funders or journals that conflict with your institution's policies
• Data that may be subject to export control or national security restrictions
• Disputes with collaborators about data ownership or sharing arrangements
• Ethical concerns that are not addressed by your existing approvals

When to Contact Your Funding Authority

Contact your funding authority early when you anticipate difficulty meeting an open science obligation. This includes situations where:

• A repository you planned to use becomes unavailable
• Data cannot be shared for reasons not anticipated in your Data Management Plan
• A publication venue does not permit the required deposit
• Technical limitations prevent compliance with specific requirements

Early communication allows the funding authority to provide guidance or approve alternative arrangements. Waiting until the final report creates a situation where the authority must decide whether to accept non-compliance or impose penalties.

When to Consult Domain Experts

Domain-specific open science questions may require expert advice. For animal research, consult the Experimental Design Assistant and your institution's animal welfare body. For health research reporting, consult the EQUATOR Network for the appropriate reporting guideline. For data management questions, consult your institution's data librarian or research data support team.

Frequently Asked Questions

What is the difference between open access and open science?

Open access refers specifically to making research publications freely available to read and reuse. Open science is a broader concept that includes open access plus open data, open methods, preregistration, open peer review, and other transparency practices. Under Horizon Europe, open access to publications is one component of a larger open science framework that also includes research data management, data sharing, and research integrity practices.

Do I need to make all my research data openly available?

No. Horizon Europe requires that research data be managed responsibly and that data be shared in accordance with the FAIR principles, but it allows justified restrictions. Personal data, commercially sensitive data, and data with security implications may be restricted. Your Data Management Plan must explain any restrictions and describe how access will be managed. The key requirement is that restrictions are justified and documented, not that all data are openly available.

What happens if I do not comply with open science requirements?

Non-compliance can result in reduced grant payments, ineligibility of costs, or other penalties specified in your grant agreement. The specific consequences depend on the nature and severity of the non-compliance. The best approach is to communicate with your funding authority early if you anticipate difficulty meeting an obligation, instead of waiting for the issue to be discovered during reporting.

How do I choose a repository for my data?

Choose a repository that accepts your data types, supports the access level you need, and provides persistent identifiers. Consider whether a domain-specific repository offers metadata standards that fit your data. Check the repository's preservation commitments and its policies on data versioning and withdrawal. Your Data Management Plan should name the specific repository for each dataset and explain why it was chosen.

What is the role of preregistration in open science?

Preregistration creates a timestamped record of your study design and analysis plan before data collection begins. This distinguishes confirmatory analyses, which test pre-specified hypotheses, from exploratory analyses, which search for patterns without prior specification. Preregistration protects against questionable research practices such as selective reporting and helps readers interpret your findings appropriately.

How does open science apply to research that involves human participants?

Research involving human participants must balance open science goals with privacy and consent obligations. Personal data must be handled in accordance with GDPR and other applicable regulations. Your Data Management Plan must describe how personal data will be anonymized or pseudonymized, what lawful basis supports processing, and how access to sensitive data will be controlled. Ethics approvals must be obtained before data collection begins.

What are reporting guidelines and why should I use them?

Reporting guidelines specify what information must be included in research reports to ensure completeness and transparency. The EQUATOR Network hosts reporting guidelines for many study types, including randomized trials, observational studies, and systematic reviews. Using the appropriate guideline improves the quality of your reporting and makes your research more useful to others.

How do I handle data from collaborative projects with partners outside the EU?

International collaboration raises additional compliance considerations. Open science requirements may differ across partner countries, and some data may be subject to export control or national security restrictions. The EU-China research cooperation analysis illustrates how knowledge securitisation affects international research collaboration. Clarify data ownership and sharing arrangements with partners early, and seek advice from your institution's research office when international data transfers are involved.

Related Articles

• Bioinformatics Portfolio: Projects That Demonstrate Real Research Judgment
• Git for Research Projects: Version Control for Code, Data, and Analysis Notes
• Research Data Management Plan: From File Naming to Long-Term Sharing
• Data Visualization for Biological Research: Choosing Figures That Preserve Meaning
• Biomedical Science Research Building

References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action.. Lancet (London, England), 2024.
• Global burden of enteric infectious diseases, diarrhoeal diseases, and corresponding aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.. The Lancet. Infectious diseases, 2026.
• Making data sharing the norm in medical research.. BMJ (Clinical research ed.), 2023.
• Panel stacking is a threat to consensus statement validity.. Journal of clinical epidemiology, 2024.
• Mapping drivers of life expectancy change in Asia from 1990 to 2023.. Nature, 2026.
• Horizon Europe, research and innovation programme until 2027 - what is new comparing with Horizon 2020?. Casopis lekaru ceskych, 2022.
• The overlooked burden of persistent physical symptoms: a call for action in European healthcare.. The Lancet regional health. Europe, 2025.
• Fragile promise of psychedelics in psychiatry.. BMJ (Clinical research ed.), 2024.
• Readiness Assessment for AI in Nursing Care Projects: Multimethods Study.. 2026.
• Detection, identification and quantification of NGTs in EU authorization procedures - No solution without legislative change.. 2026.
• Bioethics committee approvals in multicentre laryngology research across Europe: the European Laryngological Society's consensus on legal basis and quality assurance.. 2026.
• Mapping the Landscape of Open Access Dashboards - A Dataset for Research and Infrastructure Development.. 2026.
• Author compliance in following open journal system of communication science in Indonesia. Journal of Physics: Conference Series, 2019.
• Author Compliance in Following OJS Information in The Field of Science Communication in Indonesia. 2018.
• Acarbose in Prevention of Stroke. International Journal of Clinical Case Reports and Reviews, 2025.
• Comprehensive evaluation of power information system security protection based on entropy weight-TOPSIS algorithm. Computer Technology and Information Science, 2022.
• Towards structured log analysis. International Joint Conference on Computer Science and Software Engineering, 2012.
• Horizon Europe, research and innovation programme until 2027 - what is new comparing with Horizon 2020?. Casopis Lekaru Ceskych, 2022.
• The Role of TRIMIS as a Policy Support Tool. Lecture Notes in Mobility, 2025.
• Science diplomacy as a foreign policy tool for Turkey and the ramifications of collaboration with the EU. Humanities and Social Sciences Communications, 2021.
• EU-China research cooperation under a “de-risking” framework: the securitisation of knowledge in EU science policy (2021-2025). Asian Review of Political Economy, 2026.
• A Scoping Review of STEAM Policies in Europe. Education Sciences, 2025.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.