This area contains public resources for ontology consumers and providers to support practical application and mapping.
The Ontologies Mapping project was set up to create better mapping tools and services, and to establish best practices for ontology management in the Life Sciences. For our purpose, ontologies can include hierarchical relationships, taxonomies, classifications and/or vocabularies which are becoming increasingly important for support of research and development.
Patient ownership and control of personal data and increased regulatory compliance are key areas of improvement in clinical trials. Blockchain is a form of Distributed Ledger Technology (DLT) that supports trust, immutability of transactions and prevents single point of failure.
Blockchain technology involves the implementation of Decentralized Identifiers (DID), ‘virtual’ wallets, Verifiable Credentials (VC), smart contracts, and a blockchain layer to record transactions between parties during the clinical trial Informed Consent process. Blockchain technology puts patients in control of their identity and their data and has the potential to significantly change how patients are enrolled and participate in clinical trials. Further, blockchain technology puts sponsors in control of the Informed Consent process and documentation and has the potential to increase speed and compliance through ‘anytime’, real-time auditing.
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.
The Pistoia Alliance Ontologies Mapping Project has covered two domains of interest: phenotype and disease, and laboratory analytics domain. In this paper we focus on the latter, for which alignment sets are not that common, we introduce the system Paxo, and we compare its results against participants of the Ontology Alignment Evaluation Initiative.
These Best Practice Guidelines are created to provide a strategy that derives as much value as possible to
the study collecting the data, the participants who supply the data and any external users who are reusing
the data outside of its original intended use.
This Quality Data Generation and Ethical Use (QDatE) code of ethics is complementary to the Best Practice
Guidelines and will ensure that the sensor-generated data from remote monitoring technologies (SDRM) is collected, stored, governed, used, and reused in a way that utilises the data to the best of its potential.
FAIR Toolkit and Future Direction presentation from the 2020 Virtual Conference
Supports the implementation of the Findable, Accessible, Interoperable and Re-usable (FAIR) guiding principles
Data is the key asset in biopharmaceutical research; it is highly valued, but how well is it managed?
Developing a rigorous and robust data governance strategy is the foundation for driving innovation and improving the efficiency and effectiveness of R&D. New technologies, such as AI/ML, currently being utilised in modern R&D including its laboratories are dependent on the machine-ready availability (FAIR) of well-categorised data. Furthermore, security and data integrity is crucial for ensuring regulatory compliance.
The first session in the Pistoia Alliance Data Governance Webinar Series will address some of the key challenges in developing a data governance framework.
This webinar explores how the Mondo ontology bridges clinical and translational research, featuring insights from Chris Mungall and hosted by the Pistoia Alliance’s FAIR Implementation Community of Interest.
