26 papers · ranked by Valyu relevance
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…
Michael Kotliar, Andrey V Kartashov, Artem Barski
Modern biomedical research has seen a remarkable increase in the production and computational analysis of large datasets, leading to an urgent need to share standardized analytical techniques. However, of the >100 computational workflow systems used in biomedical research, most define their own specifications for…
Nishchay Karle, Ben Clifford, Yadu Babuji, Ryan Chard + 2 more
'Daniel S. Katz' 'Kyle Chard'] Abstract—The Common Workflow Language (CWL) is a widely adopted language for defining and sharing computational workflows. It is designed to be independent of the execution engine on which workflows are executed. In this paper, we describe our experiences integrating CWL with Parsl, a…
Michael R. Crusoe, Sanne Abeln, Alexandru Iosup, Peter Amstutz + 7 more
'John Chilton' 'Nebojša Tijanić' 'Hervé Ménager' 'Stian Soiland‐Reyes' 'Bogdan Gavrilović' 'Carole Goble' 'The CWL Community'] Computational Workflows are widely used in data analysis, enabling innovation and decision-making for the modern society. In many domains the analysis components are numerous and written in…
Michael Kotliar, Andrey V. Kartashov, Artem Barski
Massive growth in the amount of research data and computational analysis has led to increased utilization of pipeline managers in biomedical computational research. However, each of more than 100 such managers uses its own way to describe pipelines, leading to difficulty porting workflows to different environments and…
Mahnoor Zulfiqar, Michael R. Crusoe, Birgitta König-Ries, Christoph Steinbeck + 3 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…
Daniel J.B. Clarke, John Erol Evangelista, Zhuorui Xie, Giacomo B. Marino + 36 more
Many biomedical research projects produce large-scale datasets that may serve as resources for the research community for hypothesis generation, facilitating diverse use cases. Towards the goal of developing infrastructure to support the findability, accessibility, interoperability, and reusability (FAIR) of biomedical…
Azza E Ahmed, Joshua M Allen, Tajesvi Bhat, Prakruthi Burra + 16 more
The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software…
Farah Zaib Khan, Stian Soiland-Reyes, Richard O Sinnott, Andrew Lonie + 2 more
Amongst the many big data domains, genomics is considered the most demanding with respect to all stages of the data lifecycle, including acquisition, storage, distribution, and analysis . Because genomic data are growing at an unprecedented rate due to improved sequencing technologies and reduced cost, it is currently…
Luis de la Garza, Johannes Veit, Andras Szolek, Marc Röttig + 4 more
'Stephan Aiche' 'Sandra Gesing' 'Knut Reinert' 'Oliver Kohlbacher'] Background Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the…
Michael J. Jackson, Edward Wallace, Kostas Kavoussanakis
Workflow management systems represent, manage, and execute multi-step computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental…
Amruta Kale, Ziheng Sun, Xiaogang Ma
Computational workflows are widely used in data analysis, enabling automated tracking of steps and storage of provenance information, leading to innovation and decision-making in the scientific community. However, the growing popularity of workflows has raised concerns about reproducibility and reusability which can…
Daniel J.B. Clarke, John Erol Evangelista, Zhuorui Xie, Giacomo B. Marino + 38 more
'Giacomo B. Marino' 'Anna I. Byrd' 'Mano R. Maurya' 'Sumana Srinivasan' 'Keyang Yu' 'Varduhi Petrosyan' 'Matthew E. Roth' 'Miroslav Milinkov' 'Charles Hadley King' 'Jeet Kiran Vora' 'Jonathon Keeney' 'Christopher Nemarich' 'William Khan' 'Alexander Lachmann' 'Nasheath Ahmed' 'Alexandra Agris' 'Juncheng Pan' 'Srinivasan…
Jason P. Kurs, Manuele Simi, Fabien Campagne
Computational workflows and pipelines are often created to automate series of processing steps. For instance, workflows enable one to standardize analysis for large projects or core facilities, but are also useful for individual biologists who need to perform repetitive data processing. Some workflow systems, designed…
Tainã Coleman, Henri Casanova, Loïc Pottier, Manav Kaushik + 2 more
'Ewa Deelman' 'Rafael Ferreira da Silva'] Abstract—Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed on heterogeneous, distributed…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
Shixiang Wang
Bioinformatics analyses depend on workflow engines to coordinate dozens of computational tools across complex dependency chains. The most widely adopted engines—Snakemake, Nextflow, the Common Workflow Language (CWL), and the Workflow Description Language (WDL)—run on interpreted or just-in-time (JIT) compiled language…
Ward Jaradat, Alan Dearle, Adam Barker
—Orchestrating centralised service-oriented workflows presents significant scalability challenges that include: the consumption of network bandwidth, degradation of performance, and single points of failure. This paper presents a high-level dataflow specification language that attempts to address these scalability…
Jay Jay Billings, Shantenu Jha
The role of scalable high-performance workflows and flexible workflow management systems that can support multiple simulations will continue to increase in importance. For example, with the end of Dennard scaling, there is a need to substitute a single long running simulation with multiple repeats of shorter…
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Tainã Coleman + 54 more
The Workflows Community Summit was supported by the National Science Foundation (NSF) under grants number 2016610, 2016619, and 2016682, and the Department of Energy (DOE). Any opinions, findings, and conclusions or recommendations expressed at the event or in this report are those of the authors and do not necessarily…
Luciano V Araújo, Simon Malkowski, Kelly R Braghetto, Maria R Passos-Bueno + 3 more
'Maria R Passos-Bueno' 'Mayana Zatz' 'Calton Pu' 'João E Ferreira'] Background Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for…
Samuel Lampa, Martin Dahlö, Jonathan Alvarsson, Ola Spjuth
The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines and assist with job automation and aids reproducibility of analyses. Many contemporary workflow tools are specialized and not designed…
Alexander Garcia Castro, Samuel Thoraval, Leyla J Garcia, Mark A Ragan
'Mark A Ragan'] Background Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Natural language processing with the help of large language models such as ChatGPT has become ubiquitous in many software applications and allows users to interact even with complex hardware or software in an intuitive way. The recent concepts of Self-Driving Labs and Material Acceleration Platforms stand to benefit…
Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan Jones + 5 more
Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research…