FAIR-ready

Legacy LIMS: hampering true interoperability, innovation, and scalability

Labs today produce rigorously quantified and well contextualized data. But with the explosion of innovation in the life sciences, they must now be able to integrate that data with other systems, whether to simply bring in a qualified list of reagents or to import a biobank dataset for a validation study.

However, most informatics systems available today were built when the value of combining data with its context (or metadata) was poorly understood. Such systems suffer from:

Non-standardized data

Using standardized and flexible data formats is key for interoperability and knowledge transfer. Legacy software, however, might not collect and store data in such formats.

Poor integration

Older systems often poorly integrate with other lab tools and systems. Manual data entry, mapping, and clean-up is therefore often required, and tracing data provenance across those systems is challenging.

An unsuitable, non-interoperable database format

Most, if not all, LIMS available today are built on relational databases, which don’t support several functions critical for findable, sharable, accessible, and reusable data. For example, relational databases don’t allow storage of data together with its critical context (metadata). Instead, they store data and metadata in separate tables connected by non-intuitive data constraints.

As a result, data remains locked up in a lab’s informatics systems and can’t be easily viewed by interested parties such as regulators and investigators. Data also cannot be shared in a user-friendly format.

With legacy solutions, exchanging data across sites and customized systems is a laborious and potentially error-prone process. It involves time-consuming and complex extract, transform, and load (ETL) procedures to get data into non-real time storage systems such as data lakes.

Critically, such a burdensome and slow process hinders innovation in your lab.

Discover the benefits of a FAIR-ready LIMS in your lab

Effortless data integration: Combine data from diverse sources for a comprehensive view of lab operations. Draw insights that are only possible with a knowledge graph such as discovering multi-hop relationships.

Improved data consistency: a unified data, meta-data, and provenance model ensures consistent data representation, improving data quality and trustworthiness.

Flexible data querying: Perform flexible queries across diverse datasets without being constrained by predefined schemas. This ensures that the data collected today will be optimally useful in the future, even if the future use of this data is not fully understood today.

Adaptive data model: Accommodate data structure changes now and into the future with RDF's graph-based data model, helping you adapt to evolving lab needs. Labbit places the data model fully under your control in the configuration layer, greatly simplifying validation scope for regulated applications.

Immutability without constraint: Preserve data integrity with immutable data storage, preventing unwanted data modifications.

Chained history: Access and analyze high-fidelity historical data for a comprehensive understanding of your lab's operations and performance.

Provenance: track the origin and lineage of data, enhancing transparency and data trustworthiness.

Semantic interoperability: Leverage RDF's semantic capabilities for seamless data exchange and integration between systems, eliminating the need for data cleanup and normalization, particularly in AI and ML applications.

Open data and data sharing: Promote open science and data sharing by making data and configuration elements from Labbit easily shareable on code-sharing platforms or other collaboration tools.