24 papers · ranked by Valyu relevance
Melissa Finster, Markus Wenzel, Elham Taghizadeh
Background The use of health data supports knowledge-based decision-making in healthcare. Common Data Models (CDMs) and data standards facilitate the integration of diverse data sources and enable federated analysis by harmonizing data formats and terminologies. Methods To determine the best approaches to harmonizing…
Heather Hufstedler, Yannik Roell, Andressa Peña, Ankur Krishnan + 12 more
Data standardization offers significant benefits for industry and regulators alike, suggesting that it should be easy. In practice, however, the process has been hard and slow moving. Moving from an abstract incentive-based analysis to one focused on institutional detail reveals myriad frictions favoring the status quo…
Carrie Andrew, Sharif Islam, Claus Weiland, Dag Endresen
complex research projects and digital twins: a guide Authors: ['Carrie Andrew' 'Sharif Islam' 'Claus Weiland' 'Dag Endresen'] Abstract. Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more…
Rhonda Facile, Catherine Chronaki, Peter van Reusel, Rebecca Kush
The effective and meaningful exchange of data is pivotal for patient care, informed decision-making, and advancements in research and technology. This opinion piece explores the critical role of semantic interoperability (SI) in ensuring meaningful health data sharing across diverse systems. Emphasizing the imperative…
Amaryllis Mavragani, Guy Tsafnat, Rachel Dunscombe, Davera Gabriel + 2 more
'Grahame Grieve' 'Christian Reich'] Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably…
Eric W Deutsch, Juan Antonio Vizcaíno, Andrew R Jones, Pierre-Alain Binz + 25 more
The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception twenty years ago. Here we describe the general…
Kasper Claes, Valentina Ticcinelli, Reham Badawy, Yordan P. Raykov + 2 more
'Luc J. W. Evers' 'Max A. Little'] Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which a consensus on a common storage, exchange and…
Charlotte Vercammen, Antje Heinrich, Christophe Lesimple, Alessia Paglialonga + 2 more
'Alessia Paglialonga' 'Jan-Willem A. Wasmann' 'Mareike Buhl'] Results: Survey results emphasized the need for data standardisation in audiology aimed at facilitating research and improving patient care. Only 38% of survey respondents were aware of existing initiatives. Yet, 90% envisioned contributing to them moving…
Ariel Rokem, Vani Mandava, Nicoleta Cristea, Anshul Tambay + 3 more
'Kristofer Bouchard' 'Carolina Berys-Gonzalez' 'Andy Connolly'] Title: Summary Machine learning and artificial intelligence promise to accelerate research and understanding across many scientific disciplines. Harnessing the power of these techniques requires aggregating scientific data. In tandem, the importance of…
Stephen A. Fisher, Josef Hardi, Richard Morgan, Erik Nordgren + 7 more
Since publication of the FAIR Guiding Principles in 2016, the scientific community has increasingly sought to make experimental data findable, accessible, interoperable, and reusable. Operationalizing the FAIR principles in routine scientific workflows remains challenging without a standardized, workable…
Authors not listed
The nanosafety domain has seen significant advancements in data generation and sharing, yet challenges remain in ensuring data interoperability and reuse. This article focuses on developing a semantic interoperability framework for nanosafety data to maximize the FAIRness (Findability, Accessibility, Interoperability…
Ginger Tsueng, Marco A. Alvarado Cano, José Bento, Candice Czech + 14 more
Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage…
Ammar Ammar, Chris Evelo, Egon Willighagen
New nanomaterials improve our society. Understanding their effects on biological systems is of importance to improve our understanding of their properties and safety. However, reusability of previously produced data to help developing computational risk assessment tools is still limited, due to the inconsistency in…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…
Lucina Hackman, Pauline Mack, Hervé Ménard
Data underpinning science have become one of the most precious assets in research, and while the principles of FAIR (Findable, Accessible, Interoperable and Reusable) have been put forward as a guide to how to approach data handling, data sharing and long-term storage still remain a challenge for many research areas…
Bart Nijsse, Peter J. Schaap, Jasper J. Koehorst
The Life sciences is an interdisciplinary field of research and one of the the biggest suppliers of scientific data. Reusing and connecting this data can uncover hidden insights and lead to new concepts, provided there is machine-actionable metadata available about the scientific experiments conducted with a degree of…
Enrico Coiera
Results: The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide…
Manuel Schottdorf, Guoqiang Yu, Edgar Y. Walker
The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIH’s BRAIN Initiative. The…
Fathoni A. Musyaffa, Kirsten Rapp, Holger Gohlke
The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data…
Alastair McCullough
This paper adduces a novel definition of regulatory enterprise information governance as a strategic framework that acts through control mechanisms designed to assure accountability in managing decision rights over information and data assets in organizations. This new pragmatic definition takes the perspectives of…
Mahnoor Zulfiqar, Michael R. Crusoe, Birgitta König-Ries, Christoph Steinbeck + 2 more
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for…
Roman Lukyanenko
Data Management Authors: ['Roman Lukyanenko'] In an era dominated by information technology, the critical discipline of data management remains undervalued compared to the innovations it enables, such as artificial intelligence and social media. The ambiguity surrounding what constitutes data management and its…
Authors not listed
The discoverability and reusability of data is critical for machine learning to drive new discovery in the chemical sciences, and the ‘FAIR Guiding Principles for scientific data management and stewardship’ provide a measurable set of guidelines that can be used to ensure the accessibility of reusable data. We…
Haya Deeb, Hwee Yun Wong, Trisha Usman, Megan A. M. Kutzer + 2 more
The evolution of research practices emphasizes the role of Open Data in fostering transparency and collaboration. This study evaluates the adoption of Open Data practices in the School of Biological Sciences at a research-intensive university in the United Kingdom. Our analysis of research data sharing from 2014 to…