Search NASASearch

DOE OSTI · 2573492

WorkflowHub: a registry for computational workflows

Gustafsson, Ove Johan Ragnar [Univ. of Melbourne, VIC (Australia)] (ORCID:0000000229775032)·Wilkinson, Sean R. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)] (ORCID:0000000214437479)·Bacall, Finn [Univ. of Manchester (United Kingdom)] (ORCID:0000000200483300)·Soiland-Reyes, Stian [Univ. of Manchester (United Kingdom); Univ. of Amsterdam (Netherlands)] (ORCID:0000000198429718)·Leo, Simone [Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Cagliari (Italy)] (ORCID:0000000182715429)·Pireddu, Luca [Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Cagliari (Italy)] (ORCID:0000000246635613)·Owen, Stuart [Univ. of Manchester (United Kingdom)] (ORCID:0000000321300865)·Juty, Nick [Univ. of Manchester (United Kingdom)] (ORCID:0000000220368350)·Fernández, José M. [Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) (Spain); Spanish National Bioinformatics Institute (INB), Barcelona (Spain)] (ORCID:0000000248065140)·Brown, Tom [Leibniz Institute for Zoo- and Wildlife Research, Berlin (Germany)] (ORCID:0000000182934816)·Ménager, Hervé [Institut Pasteur, Paris (France). Bioinformatics of Biostatistics Hub; Université Paris Cité (France); Centre National de la Recherche Scientifique (CNRS), Evry (France). Institut Français de Bioinformatique (IFB)] (ORCID:0000000275521009)·Grüning, Björn [Univ. of Freiburg (Germany)] (ORCID:0000000230796586)·Capella-Gutierrez, Salvador [Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) (Spain); Spanish National Bioinformatics Institute (INB), Barcelona (Spain)] (ORCID:000000020309604X)·Coppens, Frederik [VIB Technologies, Ghent (Belgium). VIB Data Core] (ORCID:0000000165655145)·Goble, Carole [Univ. of Manchester (United Kingdom)] (ORCID:0000000312192137)

Abstract

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gustafsson, Ove Johan Ragnar [Univ. of Melbourne, VIC (Australia)] (ORCID:0000000229775032), Wilkinson, Sean R. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)] (ORCID:0000000214437479), Bacall, Finn [Univ. of Manchester (United Kingdom)] (ORCID:0000000200483300), Soiland-Reyes, Stian [Univ. of Manchester (United Kingdom); Univ. of Amsterdam (Netherlands)] (ORCID:0000000198429718), Leo, Simone [Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Cagliari (Italy)] (ORCID:0000000182715429), Pireddu, Luca [Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Cagliari (Italy)] (ORCID:0000000246635613), Owen, Stuart [Univ. of Manchester (United Kingdom)] (ORCID:0000000321300865), Juty, Nick [Univ. of Manchester (United Kingdom)] (ORCID:0000000220368350), Fernández, José M. [Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) (Spain); Spanish National Bioinformatics Institute (INB), Barcelona (Spain)] (ORCID:0000000248065140), Brown, Tom [Leibniz Institute for Zoo- and Wildlife Research, Berlin (Germany)] (ORCID:0000000182934816), Ménager, Hervé [Institut Pasteur, Paris (France). Bioinformatics of Biostatistics Hub; Université Paris Cité (France); Centre National de la Recherche Scientifique (CNRS), Evry (France). Institut Français de Bioinformatique (IFB)] (ORCID:0000000275521009), Grüning, Björn [Univ. of Freiburg (Germany)] (ORCID:0000000230796586), Capella-Gutierrez, Salvador [Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) (Spain); Spanish National Bioinformatics Institute (INB), Barcelona (Spain)] (ORCID:000000020309604X), Coppens, Frederik [VIB Technologies, Ghent (Belgium). VIB Data Core] (ORCID:0000000165655145), Goble, Carole [Univ. of Manchester (United Kingdom)] (ORCID:0000000312192137). 2025-05-21. WorkflowHub: a registry for computational workflows. https://doi.org/10.1038/s41597-025-04786-3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING