Transparency in R&D and reproducible research in practice — Expert perspectives (Part 2)

Author: Saskia Hoving

Editor-in-Chief

Reproducibility is widely recognised as a cornerstone of trustworthy research, shaped by the tools, standards and data that support it, as well as the everyday practices that make research transparent and traceable. Across R&D, there is a growing focus on turning high‑level principles into practical, repeatable ways of working.

As discussed in the first episode of this two‑part podcast series, building reproducibility at scale starts with strong cultural and digital foundations. In this second episode, our expert guests, Emma Ganley and Robin Padilla, shift the focus to what that looks like in practice, with a particular emphasis on transparency. They explore practical ways to improve transparency and how documentation, digital tools, publishing practices and incentives come together to support reproducible research in real‑world settings.

In this companion article, we take a closer look at why transparency plays such a central role and share practical ways researchers, institutions and publishers can embed it more consistently across the research lifecycle.

Why transparency is essential for reproducible research

Transparency supports reproducibility and enhances trust in science by enabling knowledge to be shared and ensuring open access to data. It is a vital thread of trust that connects researchers, infrastructure, policy, and support roles, including research organisations and publishers. In practical terms, it means showing others what you have done and how you did it.

“Doing maths at school, you were always asked to show your working. That should be standard practice in research,” Ganley says. Documenting methods and results at every step produces a comprehensive record for researchers, their peers, and future generations. It also supports self-correction as research evolves.

Done well, transparency becomes a powerful enabler of reproducibility. “Only by being fully transparent about the approaches taken can another group, or even the same group at a later time, reliably reproduce the work,” Ganley explains. She suggests keeping detailed records including step-by-step methodologies, complete descriptions of reagents, materials and equipment and troubleshooting notes.

Practical ways to improve transparency in research workflows

So, what does this look like in practice? At its core, transparency is built through simple, consistent habits that make it easier for others to understand and reproduce research:

• Start documenting methods early: Capturing details as the research progresses helps ensure accuracy and completeness when drafting the manuscript.
• Treat method documentation like writing a recipe: Capture every parameter, quantities, timings, temperatures, equipment settings, and any other details needed, for someone to follow the protocol exactly.
• Build a habit of daily tracking: Record all process steps, optimisations, adjustments, unexpected interruptions, and variations as they happen. Even small tweaks can explain why a process succeeds or fails, and thorough documentation ensures those insights aren’t lost.

For R&D teams, transparency is increasingly a core part of research infrastructure. Supporting reproducibility at scale means enabling researchers with the right tools, integrated workflows and incentives, ensuring that clear documentation and open sharing are built into processes from the outset.

Digital tools and infrastructure for transparent research

These everyday practices are becoming easier to sustain as research environments evolve. Digital tools and infrastructures now play a central role in enabling transparency at scale. Ganley sets out the advantages of digital tools, including making information visible, processes consistent, and decisions traceable. “This makes sharing easy and ensures transparency is the default rather than an afterthought,” she says. Technical improvements, such as interoperability between platforms and the machine readability of content, further support these efforts.

With new technologies rolling out faster than ever, Padilla notes that “researcher behaviour, which long moved at glacial speeds, is being upended and quickly.” Developers building tools to support research need to maintain “tight and fast feedback loops” to understand which technologies are being adopted and for what purposes.

At the same time, the fundamentals remain the same: “Create new knowledge that can be reliably used by others, whether they are people or bots,” Padilla advises.

As digital tools evolve, transparency becomes closely linked to organisational infrastructure, requiring joined‑up systems, interoperable platforms and shared standards that allow information to flow seamlessly across the research lifecycle.

Advancing transparency across the research ecosystem

Building on these digital and organisational foundations, stakeholders across the research ecosystem play a key role in shaping how transparency is applied and incentivised. According to Ganley, the incentive for corporate stakeholders to be transparent is clear. However, she advocates for more incentives for academia, including changes in responsible research assessment and recognition of good practice, such as the UK’s Hidden REF initiative.

Ganley and Padilla see a cultural shift already taking shape, with journals increasingly requiring data availability statements, code deposits, and method sharing. Advancing transparency as a standard practice calls for coordinated effort across publishers and research community. In practice, this includes:

• Establishing and upholding clear open research policies covering data, code, methods, and materials.
• Enabling greater transparency in evaluation processes, including more open approaches to peer review.
• Supporting upfront research design through mechanisms such as pre-registration and early-stage review.
• Recognising and valuing a broader range of outputs, including null results, replications, robustness studies and supportive research.
• Connecting tools and infrastructure to make it easier to follow open research best practices.
• Providing training and guidance so researchers can apply these standards consistently in their day-to-day work.

These perspectives highlight how transparency connects individual practice with digital infrastructure and organisational strategy. As expectations evolve across academia and industry, embedding transparency into tools, workflows, and incentives will be key to scaling reproducibility in increasingly digital and interconnected R&D environments. In this way, transparency becomes more than a principle, it becomes a shared foundation for more reliable, collaborative and future‑ready research. To listen to the full podcast conversation with Emma Ganley and Robin Padilla click here or on the image below:

This discussion builds on the themes explored in the first article and podcast episode, which examine the cultural shifts and digital foundations shaping reproducibility across the research ecosystem.

Related content

• How digital labs enhance reproducibility and accelerate innovation
• Reproducibility as a competitive advantage in life science R&D
• From silos to systems building interoperable R&D workflows
• Digital twins in R&D: Bridging the GAP between simulation and experimentation
• Improving research reproducibility with open method sharing

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