From silos to systems building interoperable R&D workflows

Author: Saskia Hoving

Editor-in-Chief

Despite rapid digitalisation, research workflows often remain fragmented. Disconnected tools and data create unnecessary friction for labs and teams. In this blog, three data‑driven innovators explore why interoperability matters, the challenges labs face today and what more connected workflows could enable.

Every day, research teams lose valuable time and insights because their systems don’t connect as they should. Data sits in proprietary formats, workflows don’t quite line up, and sharing information across teams is harder than it needs to be. These everyday frictions often go unnoticed, yet they quietly slow down research and innovation. At a time when speed, transparency and reproducibility matter more than ever, such inefficiencies can have real consequences.

This is why interoperable workflows are moving onto the strategic agenda for digital labs. When tools, systems and data are able to work together, organizations can streamline research processes, collaborate more effectively and produce results that are both robust and scalable. To better understand how this shift is taking shape, we spoke with three experts at Springer Nature: Rob Padilla, Product Director, Digital Life Science Solutions; Emma Ganley, Director of Strategic Initiatives at protocols.io; and Prathik Roy, Product Director, Data Solutions & Strategy. They share their perspectives on why interoperability matters, the practical challenges labs face today, and how publishers and technology providers can help build more connected research environments.

Breaking down barriers to release value

As we’ve seen, siloed activity risks slowing the rate of discovery and innovation. This isn’t to say there’s no place left in the lab for modular platforms so long as they’re integrated well to avoid fragmentation. Modularity provides the flexibility to maximise internal efficiency without disrupting the entire system.

Interoperable systems on the other hand extend a platform’s reach seamlessly across tools, systems and organisations. What were once isolated tools are transformed into a connected ecosystem. And the benefits are immense. Digital lab operations are smarter and faster, generating more reproducible results. All of which leads to better collaboration and decision-making, increasing efficiencies and ultimately greater speed to market.

Achieving interoperable workflows is not a straightforward process though, as Rob Padilla points out. “Many big organisations are very complex and could potentially have many different ELNs at different locations, with formats that aren’t interoperable. If you want to export from an ELN to a different company or team, this will really hold you back. And this is an ongoing problem; when applications were originally designed, the main objective was “does it work?”. They were never built with interoperability in mind, but now people want digital product ecosystems.”

Emma Ganley agrees. She points out that tool providers need to bear these developments in mind. “They need a holistic view of what they’re offering, and how all these tools work together.”

Interoperable systems – key elements

Interoperable research environments are built on a small number of core capabilities that allow systems, data and teams to work seamlessly together. These elements provide the technical and organisational foundations for integrated workflows, trusted data use and collaboration at scale. The sections below highlight three key components that support connected digital labs in practice.

1. APIs driving integration across R&D systems

APIs (Application Programming Interfaces) play a central role in the connected lab environment. They act as a communication layer, allowing different systems to talk to each other in a format they can all understand. They’re the glue that holds together all the components of the digital lab, making it a cohesive whole.

“APIs play a crucial role in busting siloes,” says Prathik Roy. “A big pharmaceutical company will have all the major sources of data, but they need to make sense of it all. APIs enable them to integrate content directly into their lab environment, which bypasses the barriers they would otherwise encounter looking for solutions externally.”

This is where publishers play their part in promoting interoperability. “Corporate organisations want content in machine-readable format, and they need the interoperability to ingest the content with their AI systems such as ELNs,” says Prathik. “Springer Nature have created APIs for the integration of their vast collection of healthcare and medicine articles, personalised for the pharma/healthcare space. We enrich the content by annotating it, so relevant information can be easily extracted.”

2. Open data is the power behind connected research ecosystems

Lack of standardization has been a persistent challenge since data sharing began. As recently as 5 to 10 years ago, microscopy image data was notoriously difficult to access due to vendors’ proprietary file types, creating enormous problems for researchers and labs. The advent of open (FAIR) data and open-source tools was a major factor in resolving the issue.

“The concept of FAIR data is really big, and informs the discussion,” says Rob. “It’s a set of principles designed to improve reproducibility, stating that data should be Findable, Accessible, Interoperable, and Reusable.” He adds that FAIR data has lately come to mean Fully AI Ready as well. “Any AI system is only as good as the data going into it. High quality data going in is the foundation for good quality information coming out.”

Prathik adds that Springer Nature’s role is critical to healthcare and medicine. “You could build a drug discovery pipeline built on misinformation. Having access to proper, peer-reviewed data is vital for AI in medicine.”

3. Collaboration is driving speed and scalability in the cloud

These common principles are a foundation for trustworthy, scalable and efficient science. They facilitate collaboration by reducing ambiguity and making it easier for teams across disciplines, institutions or countries to share and integrate their data.

Cloud-based platforms are an essential element of the collaborative digital lab. They accelerate communication by allowing teams to work together seamlessly in real time, no matter where they’re located. Researchers at a global biotech company can simultaneously review experimental data, update project notes and adjust workflows, allowing discoveries and development cycles to progress more quickly and accurately. The result? Faster iteration and speed to market.

Building a future-ready digital lab for scalable innovation

Together, APIs, open data and cloud‑based collaboration provide the foundations for interoperable digital labs. When these elements work in combination, they support workflows that connect systems, enable data reuse and help teams collaborate effectively across platforms and locations. Applying shared standards, including FAIR principles, supports environments that can evolve alongside research needs.

Interoperable and well‑structured data environments contribute to consistency across research processes. Accessible, well‑integrated data supports reproducible results and enables researchers to build on existing work with confidence. This emphasis on reliability underpins scientific progress and supports innovation at scale as research continues to become more data‑driven.

Gain further insights into reproducibility and interoperability

Across research environments, interoperability takes shape through everyday decisions about how systems connect, how data is structured and how teams collaborate. From APIs that link tools and content, to open data practices and cloud‑based platforms, these approaches help research workflows come together in a more coherent way. The result is an environment where data can be reused with confidence, collaboration is woven into day‑to‑day work, and reproducibility remains central across the research lifecycle.

For those interested in exploring these ideas further, the white paper Reproducibility in the Life Sciences looks more closely at how reproducible research practices are developing across the life sciences community. You can also find more information on Springer Nature’s platforms and services, which are designed to support connected workflows and the reliable use of research content within digital lab environments.

Related content

• How digital labs enhance reproducibility and accelerate innovation
• Digital twins in R&D: Bridging the GAP between simulation and experimentation
• Reproducibility as a competitive advantage in life science R&D
• Improving research reproducibility with open method sharing
• Top 5 benefits of digital labs in life sciences R&D

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