25 papers · ranked by Valyu relevance
T. Paul, William C. Regli
Interfaces Authors: ['T. Paul' 'William C. Regli'] Abstract—In this position paper we argue for standardizing how we share and process data in scientific workflows at the network-level to maximize step re-use and workflow portability across platforms and networks in pursuit of a foundational workflow stack. We look to…
Kyle A. O’Connell, Zelaikha B. Yosufzai, Ross A. Campbell, Collin J. Lobb + 7 more
As genome sequencing becomes a more integral part of scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of…
Kyle A. O’Connell, Zelaikha B. Yosufzai, Ross A. Campbell, Collin J. Lobb + 7 more
'Collin J. Lobb' 'Haley T. Engelken' 'Laura M. Gorrell' 'Thad B. Carlson' 'Josh J. Catana' 'Dina Mikdadi' 'Vivien R. Bonazzi' 'Juergen A. Klenk'] Background As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is…
Tong Zhu, Pankaj Vats, Seth Onken, Al Dunstan + 13 more
Next-generation sequencing (NGS) has transformed genomics, enabling breakthroughs in biotechnology, healthcare, and pharmaceuticals. However, exponential data growth-outpacing Moore’s Law-presents critical computational challenges for secondary analysis. We introduce Parabricks, a freely accessible, GPU-accelerated…
Shizhao Lu, Tanny Chavez, Wiebke Koepp, Guanhua Hao + 2 more
'Petrus H. Zwart' 'Alexander Hexemer'] MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically…
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…
Joshua Philpott, Alina Kurjan, Adam P Cribbs
Reproducibility, automation, and flexibility remain persistent challenges in bioinformatics, where complex workflows require integration of diverse computational tools, rigorous quality control, and dynamic adaptability to evolving datasets. Existing workflow managers often trade off usability for flexibility, limiting…
Xiaoda Wang, Yuan Tang, Tengda Guo, Bo Sang + 5 more
'Ke Zhang' 'Jiang Qian' 'Mingjie Tang'] Abstract—Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and timeconsuming. Expanding an ML workflow to encompass…
Valentin Carl, Trever Schirmer, Tobias Pfandzelter, David Bermbach
Function-as-a-Service (FaaS) is a popular cloud computing model in which applications are implemented as workflows of multiple independent functions. While cloud providers usually offer composition services for such workflows, they do not support cross-platform workflows forcing developers to hard-code the composition…
Kevin Kang, Jinwen Wo, Jon Jiang, Zhong Wang
We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based…
Chaudhry, Gohar Irfan, Choukse, Esha + 10 more
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving them remains inefficient with today's frameworks. The key problem is that they expose workflows as opaque sequences of model and tool calls…
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…
Jue Jiang, Georgina Samaha, Cali E. Willet, Tracy Chew + 4 more
'Vanessa M. Hayes' 'Weerachai Jaratlerdsiri' 'Stefano Cacciatore' 'Luiz Zerbini'] Title: Simple Summary Africa faces the highest mortality rates across eight cancer types. However, cancer studies are biased toward European populations, leading to major concerns that cancer treatments may be ineffective for African…
Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen + 9 more
The future of materials science is borderless, cooperative, and distributed across the globe. This necessitates flexible, reconfigurable software defined research workflows, which we herein demonstrate by integrating multiple disciplines and modalities. Our brokering approach to research orchestration exposes entire…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Ozgur O. Kilic, Tianle Wang, Matteo Turilli, М. И. Титов + 3 more
Representations of Scientific Workflows Authors: ['Ozgur O. Kilic' 'Tianle Wang' 'Matteo Turilli' 'М. И. Титов' 'André Merzky' 'Line Pouchard' 'Shantenu Jha'] Abstract—Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and…
Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke, Ninon De Mecquenem + 5 more
'Ninon De Mecquenem' 'Ansgar Lößer' 'Soeren Becker' 'Katarzyna Ewa Lewińska' 'Lauritz Thamsen' 'Ulf Leser'] Abstract—Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM) for task-to-node…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…
Min Cui, Yipeng Wang
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Patrick McKeever, Varun Mittal, Bryce Fukuda, Ka Yee Yeung + 1 more
The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow…
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…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Chengqiu Gou, Jifeng Li, Yufeng Li, Jian Liu + 3 more
Numerical simulation is an efficient tool for evaluation and prediction of material properties and behavior in many industrial domains such as the development of novel materials and medicines. For numerical studies of complex processes or systems with high fidelity, various data processing tools, modeling and…