Reproducibility in R&D building trust and digital foundations — Expert perspectives (Part 1)

Author: Saskia Hoving

Editor-in-Chief

Reproducibility establishes credibility in research; it underpins innovation and collaboration and helps build science that stands the test of time. Research that is reproducible provides a solid foundation for trust‑based alliances between groundbreaking researchers and the professionals who support them across academia and industry.

As part of The Link’s ongoing focus on reproducibility, digital labs, and interoperable workflows, this podcast episode is the first of a two‑episode conversation with Emma Ganley, Director of Strategic Initiatives for protocols.io, and Robin Padilla, Director of Product Management at Springer Nature’s Digital Life Science Solutions. Across both episodes, they explore the reproducibility ecosystem, from abstract principles and theory to real‑world context and practical application.

In this episode, Ganley and Padilla reflect on the cultural shifts required to ensure that reproducibility becomes the expectation rather than the exception in science. They discuss the role of digital tools and FAIR (Findable, Accessible, Interoperable, Reusable) principles, share perspectives on the opportunities and limitations of AI, and explore what it will take to shape a more connected and reliable research ecosystem for the future.

Below, we’ve curated some of the key insights from this first episode. You can listen to the full podcast conversation down below. This conversation highlights:

• Why reproducibility underpins trust across partnerships, regulation and translation
• How digital tools and standardised workflows reduce risk, error and inefficiency at scale
• What FAIR and AI‑ready data foundations mean for future‑proofing research

Why reproducibility underpins trustworthy science

“Reproducibility is an indicator of integrity and reliability,” says Ganley, “you’re building knowledge that lasts.” She likens research without reproducibility to a house without foundations: “It can look really impressive initially, even jaw-dropping, but unless it has really strong foundations, it may very well fall down over time.”

When research is transparent, stakeholders can collaborate with confidence, says Ganley, citing an example from healthcare: “When translated to clinical applications, reproducibility can have lifesaving results down the line. If it’s all reproducible, it will stand the test of time.”

Digital tools for reliable results at scale

Padilla discusses the “digital transformation of science” and the importance of embedding reproducibility into new processes. Nowadays, he explains, paper notebooks and handwritten records have been replaced by electronic lab notebooks (ELNs), laboratory information management systems (LIMS), and automated workflows. This supports reproducibility by making research more efficient and reducing mistakes, such as transcription errors, that could have a significant impact on results. “By having processes recorded in digital environments, you don’t have to worry about these things,” says Padilla.

Embedding reproducibility into everyday R&D workflows

Ganley shares her thoughts on the changes required to improve reproducibility in research. Stakeholders, such as publishers, researchers, funders, and policymakers, need to align around shared standards when it comes to open data, code, and protocols. “It’s important that reproducibility is embedded into workflows and protocols, so it becomes a standard practice,” she says. “It should not be a luxury item or an afterthought.” Fostering interoperability and reducing the number of discrete platforms that researchers use will also help to improve reproducibility: “We want everything to be joined up as much as possible,” says Ganley.

Future‑proofing research through FAIR data and AI

Ganley and Padilla also talk about how they envisage reproducibility in the future, including the role of AI. Padilla draws a distinction between probabilistic and deterministic AI systems, explaining that the former is powerful but prone to hallucinations, the latter is more consistent but limited in scope. The key is taking the best of both, he says: “Being able to balance out different types of AI systems, especially in the context of reproducibility, I see that as being really, really important.”

Ganley is confident that AI never completely replace scientists. However, she believes that applying it cleverly will be vital for the future of reproducibility: “Having AI-ready data and standardised formats will lead to big progress and should make reproducibility a lot faster and easier to achieve for researchers.”

Overall, our guests believe that a “cultural shift” is needed to ensure that reproducibility will become the expectation rather than the exception in the future. Transparent data, digital tools, and FAIR principles, well applied, will help to achieve this and, as Ganley puts it, “turn research results into a global resource rather than a lockbox.”

Listen and explore reproducibility in practice

Reproducibility sits at the intersection of culture, technology and collaboration and, as this conversation shows, is becoming an increasingly strategic concern for research organisations across academia and industry, particularly as R&D environments grow more digital, distributed and interconnected. Listen to the full podcast conversation with Emma Ganley and Robin Padilla here or click on the image below.

In the second post in this two‑episode series, we’ll continue the discussion by focusing on transparency in practice, from everyday research behaviours and documentation to the role of digital tools, publishing practices, and incentives that support reproducible research.

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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