With companies acknowledging the value of their data, it becomes increasingly important that data integrity be assured – and data stewardship implemented. In this article, Ian Harrow and Thomas Liener, Consultant Project Managers at The Pistoia Alliance explain the importance of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles, factors currently limiting their implementation and the potential benefits should these challenges be overcome.
Resource Tag: Delivering Data Driven Value
FAIR Data: From Principles To Best Practices
The FAIR Data Principles are a hot topic in research data management. Everybody wants to play FAIR, but how can principles become best practices? This webinar discusses the production and use of research data that is Findable, Accessible, Interoperable, and Reusable (FAIR) and what it means for the research ecosystem.
DataFAIRy Bioassay Annotation Pilot Project
In 2020, a team of scientists from AstraZeneca, Bristol Myers Squibb, Novartis, and Roche set forth to find a way to convert unstructured biological assay descriptions into FAIR information objects.
In this talk, we will present the lessons learned in the pilot project to annotate bioassay descriptions (bioassay) en masse and will chart a way to expand this effort in the future.
- Isabella Feierberg, Associate Principal Scientist, AstraZeneca
- Dana Vanderwall, Director of Biology & Preclinical IT, Bristol Myers Squibb
- Rama Balakrishnan, Biomedical Ontology Specialist, Genentech
- Martin Romacker, Senior Principal Scientist in Scientific Solution Engineering and Architecture, Roche
- Samantha Jeschonek, Research Scientist, Collaborative Drug Discovery
- Timothy Ikeda, Automation Principal Scientist, AstraZeneca
- Gabriel Backiananthan, Novartis
- Anosha Siripala, Technical Associate Director, Scientific Products, Novartis Institutes for BioMedical Research (NIBR)
Lynx: A FAIR-Fueled Reference Data Integration & Look up Service at Roche
Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.
In this webinar, we will present and provide technical details of Lynx, a FAIR-fueled system to enable seamless access across that ecosystem. On the one hand, Lynx exploits machine-readable, FAIR Knowledge Graphs to allow for accessing and combining multiple and disparate reference data systems. On the other hand, Lynx bridges the gap for non-experts with an intuitive and user-friendly way of finding and exploring FAIR data.
Speakers
Dr. Javier D. Fernández is a Senior Information Architect at Roche in Basel, Switzerland.
Ontologies Mapping Resources
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.
Informed Consent in Clinical Trials – Application of Blockchain Technology
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.
Results of the Ontology Alignment Evaluation Initiative 2020
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.
Ontology Matching for the Laboratory Analytics Domain
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.
QDatE Best Practice Guidelines
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.
QDatE Code of Ethics
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 Implementation in Clinical Healthcare and the FAIR Toolkit
FAIR Toolkit and Future Direction presentation from the 2020 Virtual Conference