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Substandard and falsified (SF) pharmaceuticals account for an estimated 10% of the pharmaceutical supply chain in low- and middle-income countries (LMICs), where a lack of regulatory and laboratory resources limits the ability to conduct effective post-market surveillance and allows SF products to penetrate the supply chain. The Distributed Pharmaceutical Analysis Laboratory (DPAL) was established in 2014 to expand testing of pharmaceutical dosage forms sourced from LMICs; DPAL is an alliance of academic institutions throughout the United States and abroad that provides high quality, validated chemical analysis of pharmaceutical dosage forms sourced from partners in LMICs. Results from analysis are reported to relevant regulatory agencies and are used to inform purchasing decisions made by in-country stakeholders. As the DPAL program has expanded to testing more than 1000 pharmaceutical dosage forms annually, challenges have surfaced regarding data management and sample tracking. Here, we describe a pilot project between DPAL and ARTiFACTs that applies blockchain to organize and manage key data generated during the DPAL workflow, including a sample’s progress through the workflow, its physical location, provenance of metadata, and lab reputability. Recording time and date stamps with this data will create a permanent and verifiable chain-of-custody for samples. This secure, distributed ledger will be linked to an easy-to-use dashboard, allowing stakeholders to view results and experimental details for each sample in real time and verify the integrity of DPAL analysis data. Introducing this blockchain-based system as a pilot will allow us to test the technology with real users analyzing real samples. Feedback from users will be recorded and necessary adjustments will be made to the system before the implementation of blockchain across all DPAL sites. Anticipated benefits of implementing blockchain for managing DPAL data include efficient management for routing work, increasing throughput, creating a chain of custody for samples and their data in alignment with the distributed nature of DPAL, and using the analysis results to detect patterns of quality within and across brands of products and develop enhanced sampling techniques and best practices. 

The FAIR Hackathon Workshop for Mathematics and the Physical Sciences (MPS) February 27-28, 2019 in Alexandria, Virginia brought together forty-four stakeholders in the physical sciences community to share skills, tools and techniques to FAIRify research data. As one of the first efforts of its kind in the US, the workshop offered participants a way to engage with FAIR principles (Findable, Accessible, Interoperable and Reusable) Data and metrics in the context of a hackathon. The workshop was designed to address issues of public access to data and to provide experience with FAIR tools and relevant hands-on experience for researchers. Existing FAIR tools and infrastructure were introduced. Hands-on hackathon breakout time was devoted to testing FAIR metrics and tools against physical sciences data. The hackathon invited MPS research data management stakeholders to react to the FAIR principles and to jointly consider gaps in the MPS data sharing ecosystem in the context of researcher’s actual projects. FAIR Gap analysis was introduced as a way to identify community-specific tools or infrastructure that could dramatically enhance the ability of domain scientists to make their data more FAIR. 

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.