Open data, code and protocols: Best practices, guidelines and lessons from open science leaders

T

The Researcher's Source

By: Deidre Hudson Reuss, Tue Oct 13 2026

Author: Deidre Hudson Reuss

Senior Marketing Manager

As we mark Open Access Week 2026, researchers around the world are once again reflecting on a simple but powerful question: how can we make research more transparent, reproducible and useful? While open access publications are a cornerstone of open science, sharing the underlying data, code and protocols openly is increasingly recognised as essential for maximising the value and impact of research.

Over the past year, The Researcher’s Source has spoken with seven researchers from different disciplines about putting open science into practice. Ahead of Open Access Week, we have brought together their key lessons, including the benefits of sharing research outputs openly, the barriers researchers encounter and the cultural and practical changes needed to make open science work.

Transparency builds trust

For computational scientist Geir Kjetil Sandve, transparency is fundamental to the scientific process. He explained how the open sharing of code and computational workflows enables other researchers to understand, verify, and build upon research findings, making science more robust and reproducible.

Rather than viewing open science as an additional burden, Sandve sees it as a way of working that should be embedded in the research process from the outset. As computational and AI-driven approaches become increasingly common across disciplines, he argues that open science practices help strengthen trust, foster collaboration, and maximise the impact of research. As Sandve puts it: “In science, we shouldn't be forced to trust people. We should instead always be open to check each other’s work.”

While challenges such as data sensitivity and the computational demands of modern machine learning remain, Sandve believes that open science is ultimately about making research genuinely reusable and accessible to others.

Open science amplifies impact

Clinical geneticist Zornitza Stark highlighted another important benefit of open science: helping research reach the people who can benefit from it. Drawing on her work in translational genomics and rare disease research, Stark explained how sharing genomic data openly enables faster diagnoses, accelerates gene discovery, and supports international collaborations that would be impossible for individual institutions to achieve alone. As she notes: “Open science ensures we share knowledge as quickly as possible, because that's what impacts both research and clinical outcomes.”

For Stark, open science is rooted in the belief that publicly funded research should create public benefit, while shared data and knowledge help researchers, clinicians, and patients work together to advance healthcare.

Open science starts with research culture

While tools and repositories are important, open science ultimately depends on people. Bioinformatician Johannes Koester, creator of the workflow management system Snakemake, emphasised the role of research culture in supporting reproducibility and transparency. His work demonstrates how well-designed workflows, version control and openly shared computational methods can make research more reliable, accessible and easier to build upon.

Koester argues that open science should not be treated as a separate activity that happens at the end of a project. Instead, reproducibility should be embedded throughout the research lifecycle, with code recognised as a core research output. When data, code and workflows are systematically organised and shared, researchers can save time, reduce errors and create work that others can readily understand, verify and reuse.

Purpose should guide open science practices

For computational biologist Casey S. Greene, open science is most effective when it is guided by a clear research purpose. Greene argues that the goal should be rigorous, impactful science, with open sharing serving as a means to achieve it rather than an end in itself. As he explains, “My interest isn't really open science; it's high-quality, rigorous science, and often that happens to be open.”

Open practices such as sharing data, code and methods can accelerate discovery by allowing others to build on existing work, avoid unnecessary duplication and contribute new perspectives. Greene emphasises that openly available resources create opportunities for collaboration far beyond a researcher's immediate network, helping scientific communities work together to answer important questions and deliver real-world benefits.

Open protocols are essential for reproducibility

For Steven Henikoff, reproducibility depends not only on data and code, but also on the methods used to generate results. In our interview, the molecular biologist reflected on practicing open science long before the term became widely used. Throughout his career, sharing experimental methods and protocols has been central to ensuring that other researchers can replicate and build upon his work.

Protocols provide the context that allows data and results to be interpreted effectively. Without clear and detailed methods, reproducing experiments can become difficult, even when datasets are publicly available.

Henikoff's experience highlights the importance of viewing protocols as valuable research outputs in their own right rather than simply supplementary information attached to a publication.

Reproducibility matters even more in the age of AI

As artificial intelligence becomes increasingly embedded in the discovery and creation of research, open science practices are taking on a new significance. Chemical engineer and AI researcher Connor W. Coley discussed the importance of transparency and reproducibility in machine learning research. In fields where results can be influenced by training datasets, model architectures and complex computational pipelines, openly sharing code and workflows helps others evaluate and reproduce findings.

Coley's perspective reflects a broader challenge facing many disciplines today. As research becomes more computationally intensive, it is no longer sufficient to describe methods at a high level. Access to code, models and supporting materials is often essential for validating results and advancing the field.

His experience offers an important reminder that open science is not only about sharing outputs. It is also about creating the conditions that enable trustworthy and reproducible research.

Infrastructure makes open science possible

Researchers are often at the centre of conversations about open science, but making it sustainable also requires infrastructure. Computer scientist Carole Goble has spent decades helping build the systems, standards and services that enable researchers to share and reuse research outputs effectively.

Her interview explores the often-overlooked infrastructure that underpins modern open science, from repositories and metadata standards to workflows and community platforms. Goble argues that successful sharing depends on more than individual goodwill. Researchers need tools that make it straightforward to deposit, discover and reuse data, code and methods. As she notes, “Making something available is easy; making it reusable is not.”

The easier it is to use, the more likely open science practices are to become routine. Her perspective highlights an important reality: lasting cultural change depends on both researcher engagement and institutional support.

Turning principles into practice

While these researchers come from different disciplines, several common themes emerge from their experiences. First, open science works best when it is integrated into research workflows from the beginning rather than added as an afterthought. Second, transparency strengthens trust by enabling others to understand, validate and build on research findings. Third, sharing data, code and protocols openly creates benefits that extend far beyond the original project, generating new collaborations, discoveries and greater real-world impact.

Ahead of Open Access Week 2026

Open Access Week provides an opportunity to celebrate progress toward a more open research ecosystem. But it is also a reminder that access to publications is only part of the picture.

The researchers featured in this series demonstrate that open data, code and protocols are powerful tools for improving transparency, reproducibility and collaboration. Their experiences show that while open science may look different across disciplines, its core goals remain remarkably consistent: making research more trustworthy, more accessible and more useful to others.

As research continues to become increasingly digital and collaborative, these practices will play an ever more important role in supporting scientific discovery. Whether you are sharing your first dataset, publishing a protocol, making code available, or building infrastructure that enables reuse, every step towards greater openness helps strengthen the foundations on which future research is built.

P站视频