23 papers · ranked by Valyu relevance
Caspar Schmitt, T. Kuhr
A workflow describes the entirety of processing steps in an analysis, such as employed in many fields of physics. Workflow management makes the dependencies between individual steps of a workflow and their computational requirements explicit, such that entire workflows can be executed in a stand-alone manner. Though…
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…
Gregor von Laszewski, J. P. Fleischer, Geoffrey Fox
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing to access computational resources offered through the owner's computer, HPC…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
'Christina Ertural' 'Joerg Schaarschmidt' 'Alex M. Ganose' 'Giovanni Pizzi' 'Tilmann Hickel' 'Joerg Neugebauer'] 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…
Ask Hjorth Larsen, Mikael J. Kuisma, Tara M. Boland, Fredrik A. Nilsson + 1 more
'Fredrik A. Nilsson' 'Kristian S. Thygesen'] We introduce Taskblaster, a generic and lightweight Python framework for composing, executing, and managing computational workflows with automated error handling. Taskblaster supports dynamic workflows including flow control using branches and iteration, making the system…
Sebastian Pohl, Nourhan Elfaramawy, Kedi Cao, Birte Kehr + 1 more
'Matthias Weidlich'] Scientific workflows automate the analysis of large-scale scientific data, fostering the reuse of data processing operators as well as the reproducibility and traceability of analysis results. In exploratory research, however, workflows are continuously adapted, utilizing a wide range of tools and…
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…
Yuma Ito, Masanori Hirose, Makio Tokunaga
Single-molecule imaging is a promising method for direct quantification of the dynamics and distribution of biomolecules in living cells. Although numerous methods have been developed to gain biological insights into molecular behavior, the high diversity of microscopes and single-molecule dynamics can result in…
Boris Yamrom, Yoon-ha Lee, Steven Marks, Lubomir Chorbadjiev + 2 more
Snakemake is one of the most popular workflow management systems, particularly in biological sciences. Snakemake workflows are highly portable, scalable, and transparent. Moreover, they enable the painless reproduction of published results and adaption to similar data processing and analysis projects. Here we present…
Panchal, Deven
—Generative Agentic AI systems are emerging as a powerful paradigm for automating complex, multi-step tasks. However, many existing frameworks for building these systems introduce significant complexity, a steep learning curve, and substantial boilerplate code, hindering rapid prototyping and deployment. This paper…
Malwina Kotowicz, Magdalena Shumanska, Sven Fengler, Birgit Kurkowsky + 14 more
'Birgit Kurkowsky' 'Anja Meyer-Berhorn' 'Elisa Moretti' 'Josephine Blersch' 'Gisela Schmidt' 'Jakob Kreye' 'Scott van Hoof' 'Elisa Sánchez-Sendín' 'S. Momsen Reincke' 'Lars Krüger' 'Harald Prüß' 'Philip Denner' 'Eugenio Fava' 'Dominik Stappert' 'Bhanwar Lal Puniya'] Data management and sample tracking in complex…
Damien J. Mannion, Maria del Mar Quiroga, Jacob M. Paul, Marta I. Garrido
The processing of neuroimaging data typically involves a complicated set of operations, which often require different software packages and have intensive computational and storage demands. Although there are many options available for the neuroimaging researcher to establish their preferred set of processing…
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…
J. Harry Moore, Matthias R. Bauer, Jeff Guo, Atanas Patronov + 2 more
We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a…
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…
Matt Burridge, Zhen Ou, Katherine James, Gizem Buldum + 4 more
Advances in laboratory automation and AI-driven experimental design have increased the scale and complexity of data generated in synthetic biology. Whilst biofoundries provide significant resources and infrastructure to execute these experiments, most laboratories rely on isolated automated instruments and software…
Károly Bósa, Paul Heinzlreiter
Background Data preparation is a fundamental aspect of data engineering, a prerequisite for later tasks such as data visualization, reporting, and training machine learning models. Despite the recurring patterns in data transformation processes, the specific steps often vary depending on the project context, data…
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…
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…
Authors not listed
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…
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…
Knut Rand, Ivar Grytten, Milena Pavlovic, Chakravarthi Kanduri + 1 more
Python is a popular and widespread programming language for scientific computing, in large part due to the powerful array programming library NumPy, which makes it easy to write clean, vectorized and efficient code for handling large datasets. A challenge with using array programming for biological data is that the…
Mauro Silberberg, Henning Hermjakob, Rahuman S. Malik-Sheriff, Hernán E. Grecco
Chemical Reaction Networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level modelling of CRNs enables various simulation approaches, including deterministic and stochastic methods. However, existing Python tools for CRN modelling…