This Community of Experts supports the implementation of the Findable, Accessible, Interoperable and Re-usable (FAIR) guiding principles in the life sciences by promoting best practices as well as supporting enabling methods and tools across industries.
This project aims to convert unstructured assay protocol descriptions into a high-quality FAIR data set, and create standards for this information.
FAIR refers to findability, accessibility, interoperability, and reusability. These principles are foundational to enable data-centric organizations and value creation. Implementing the FAIR data principles in a life sciences organization involves transformational journeys that tend to be complex. At any given time, organizations may be experiencing different stages of their respective FAIR implementation journey, which makes it challenging to perform benchmarks and assess progress. So while there are multiple FAIR data maturity models, […]
FAIR data is expected to add value to organisations among others by reducing time needed to find valuable information, facilitating interoperability, reusability and enabling AI initiatives. When starting to implement that FAIR data principles, many organisations realise they embark in a transformation journey which often goes beyond data itself.
How can we evaluate how “FAIR” data sets actually are? How do we evaluate, measure and support the maturation of organisations as they become increasingly adept at FAIR data?
FAIR implementation project’s goal is to enable pre-competitive instruments for the implementation of FAIR data principles.
In this podcast, Giovanni Nisato, Project Manager at the Pistoia Alliance discusses data integrity and the progress towards implementation of FAIR data principles in the pharmaceutical industry.
Agenda Introduction to In Vitro Pharmacology (IVP) and problematics faced during submission of IVP data for an IND application How to pave the way to the FAIRification of IVP data? Two key steps: Standardised data structure for submission of results – IVP module/GSRS Repository of safety and secondary in vitro pharmacology assays: using a single, […]
At any given time, different organisations are at different stages of their FAIR implementation journeys (i.e. implementing the FAIR principles of Findability, Accessibility, Interoperability, and Reusability) and benchmarking the level of FAIRness in an organisation is challenging. While there are multiple FAIR data maturity models and metrics, there is no simple, agreed, maturity assessment model of […]
Technological advancements exhibit varying degrees of longevity. Some are tried and trusted, enduring longer than others, more often when applied strategically to address tangible business challenges. Conversely, certain technologies succumb to fleeting hype without attaining substantive fruition. A constant, in this dynamic landscape is the data. To harness the full potential of cutting-edge technologies, it […]
Achieving data interoperability is fundamental for successful FAIR (Findable, Accessible, Interoperable, and Reusable) data initiatives, and the utilisation of ontology and terminology standards plays a crucial role in this endeavor. Nevertheless, challenges arise when organisations adopt competing or overlapping standards, necessitating access to mapping solutions to align datasets effectively. This issue is especially prevalent in […]
A FAIR toolkit/use case focused on best practices for the use of Artificial Intelligence and Machine Learning in Life Sciences research.
The launch of the second phase of its DataFAIRy: Bioassay project, which aims to convert bioassay data into machine-readable formats that adhere to the FAIR guiding principles of Findable, Accessible, Interoperable and Reusable.
