Problem Statement
Analytical methods are typically manually captured in either electronic documents or as structured tabular information in a software application. Manual entry is subject to mistakes, inconsistencies, and incomplete information that is unintentionally passed on to future users of the method during transfers. To meet today’s digital challenges, methods instead should be standardized and machine-readable instructions that interact directly with software to improve data integrity and facilitate automation, and linked to results to simplify interpretation and troubleshooting.
Idea Proposal and Value Proposition
To fully meet FAIR (Findable, Accessible, Interoperable, and Reusable) data standards, a digital analytical method must be the complete set of instructions needed to plan and execute a method that is both human and machine readable. The digital analytical method should integrate best practices in data management, metadata annotation, and software engineering to maximize transparency, reproducibility, and utility across diverse scientific and technical domains. Therefore, a digital analytical method isn’t just a software tool. It is a fully documented, versioned, and interoperable digital asset that ensures its outputs and processes are discoverable, accessible under clear conditions, compatible with other systems, and reusable by others with minimal barriers. This approach fosters transparency, reproducibility, and collaborative innovation in data-driven research and applications.
To achieve these goals, the project will develop a reusable data model schema that can be broadly applied to standardize various types of analytical methods and easily connect to instrument results. The project will also build multiple examples of application of the data model schema in software applications to demonstrate potential value and identify some key implementation considerations for the Pistoia community to consider.
Join the Collaboration
We’re bringing together experts to create interoperable digital analytical methods that drive efficiency and innovation. Reach out to Birthe Nielsen to explore how you or your organization can take part.
Targeted Outputs:
Deliver a compliant and reusable digital analytical method data model as an Allotrope Simple Model (ASM) representation for an example of a moderate complexity UV/VIS spectroscopy-based method. Updates to Allotrope Foundation Ontologies (AFO) will be made where needed to introduce new terms and new reusable ASM schema building blocks will be created as required to accommodate new data patterns through the expertise of the Allotrope Product Team working with project subject matter experts. The team will collaborate with solution providers to deliver prototype implementations of the ASM model as input and output for an example electronic notebook (ELN) and digital method execution (DME) / Laboratory Execution System (LES) solution. This work would be a logical extension of the prior Pistoia Methods Hub project, but with expanded scope beyond standardized method parameters to include materials used, product specifications, sample preparation, system suitability, external references, etc to make the digital representation more complete.
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Build the first digital analytical method example for UV/VIS spectrometry method in Allotrope Simple Model (ASM) format to ensure coverage for targeted use case(s) (e.g., protein concentration measurement)
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Establish & govern additions to Allotrope Foundation Ontologies (AFO) necessary to support the new ASM model
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Build a prototype example of the above UV/VIS digital method ASM implemented in two different example solutions of greatest impact to R&D and Commercial operations
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Electronic Lab Notebook (ELN) for R&D operations
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Laboratory Execution System (DME/LES) for Commercial operations
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Evaluate the generalizability of the UV/VIS digital analytical method ASM to cover additional analytical method types
Example Use Case(s):
- Method Development – standardized, connected, and interoperable methods and results data can be leveraged more easily in AI / ML modeling and predictions
- Routine Method Use – standardized, connected, and interoperable methods and results data can more easily be analyzed, trended and visualized in a vendor agnostic manner
- Method Transfer to Another Laboratory – standardized, connected, and interoperable methods and results data can reimagine manual and labor-intensive method transfers as digital method sharing and evolution
Why this is a Good Idea / Why Now:
Implementing digital analytical methods aligned with FAIR data standards can transform data-driven workflows by significantly reducing cycle times, lowering costs, improving data quality and integrity, and enabling scalable, collaborative innovation. These benefits collectively enhance the overall productivity and impact of research and operational activities. As reinforced in the Allotrope Connect workshop this past July, the problem statement of needing consistent digital representations of analytical methods resonated across multiple organizations as problems they are currently concerned about or working towards addressing, so the problem is very timely to address. Additionally, this collaboration between Pistoia and Allotrope leverages the strengths of both organizations to efficiently solve this pressing problem instead of addressing it independently; this synergy will be an accelerator.
Author: Allotrope Foundation (Vinny Antonucci)
Idea Originators: Allotrope Foundation
Strategic Priority: Delivering Data-Driven Value at Scale