Packaging research artefacts with RO-Crate

Soiland‐Reyes, Stian; Sefton, Peter; Crosas, Mercè; Castro, Leyla Jael; Coppens, Frederik; Fernández, José M.; Garijo, Daniel; Grüning, Björn; Rosa, M.; Leo, Simone; Carragáin, Eoghan Ó; Portier, Marc; Trisovic, A.; Community, RO-Crate; Groth, Paul; Goble, Carole

doi:10.3233/ds-210053

Cited by 83 publications

(45 citation statements)

References 91 publications

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“…The workflow around a model can also be made public analogous to publishing the protocol of a lab experiment, for example via protocols.io ( Teytelman and Stoliartchouk, 2015 ). The recently published Research Object Crate (RO-Crate) provides a way to package and aggregate research ‘artifacts’ with their metadata and relationships ( Soiland-Reyes et al, 2022 ), like spreadsheets, code, examples, and figures.…”

Section: Toward Fair Modeling Workflowsmentioning

confidence: 99%

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Eriksson

Bhalla

Blackwell

et al. 2022

eLife

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Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

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Section: Toward Fair Modeling Workflowsmentioning

confidence: 99%

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Eriksson

Bhalla

Blackwell

et al. 2022

eLife

View full text Add to dashboard Cite

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“…One challenge found is how to publish such canonical workflows in registries like the WorkflowHub. The hub supports the registration of Digital Objects in the form of RO-Crate [34], with the option of abstract CWL for describing the canonical workflow template, along with direct references to the workflow's GitHub repository.…”

Section: Making Canonical Workflow Building Blocks Interoperable Acro...mentioning

confidence: 99%

Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

et al. 2022

Self Cite

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We introduce the concept of Canonical Workflow Building Blocks (CWBB), a methodology of describing and wrapping computational tools, in order for them to be utilised in a reproducible manner from multiple workflow languages and execution platforms. The concept is implemented and demonstrated with the BioExcel Building Blocks library (BioBB), a collection of tool wrappers in the field of computational biomolecular simulation. Interoperability across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow, built using this library and run with 5 different Workflow Manager Systems (WfMS). We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research (CWFR) in order to improve widespread adoption and reuse of computational methods across workflow language barriers.

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“…Within the RO-Crate, workflow entities metadata is annotated using Bioschemas  17 markup to further increase the findability. RO-Crate aligns with the principles of FAIR Digital Objects [16] and is being adopted by services across scientific domains.…”

Section: F Air Digital Objects (Fdos) Through the Workflowhubmentioning

confidence: 99%

Galaxy: A Decade of Realising CWFR Concepts

et al. 2022

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Despite recent encouragement to follow the FAIR principles, the day-to-day research practices have not changed substantially. Due to new developments and the increasing pressure to apply best practices, initiatives to improve the efficiency and reproducibility of scientific workflows are becoming more prevalent. In this article, we discuss the importance of well-annotated tools and the specific requirements to ensure reproducible research with FAIR outputs. We detail how Galaxy, an open-source workflow management system with a web-based interface, has implemented the concepts that are put forward by the Canonical Workflow Framework for Research (CWFR), whilst minimising changes to the practices of scientific communities. Although we showcase concrete applications from two different domains, this approach is generalisable to any domain and particularly useful in interdisciplinary research and science-based applications.

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Packaging research artefacts with RO-Crate

Cited by 83 publications

References 91 publications

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

Galaxy: A Decade of Realising CWFR Concepts

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