Workflows Community Summit: Bringing the Scientific Workflows Community Together

Silva, Rafael Ferreira da; Casanova, Henri; Chard, Kyle; Laney, Dan; Ahn, Dong H.; Jha, Shantenu; Goble, Carole; Ramakrishnan, Lavanya; Peterson, Luc; Enders, Bjoern; Thain, Douglas; Altıntaş, Ilkay; Babuji, Yadu; Badía, Rosa M.; Bonazzi, Vivien; Coleman, Tainã; Crusoe, Michael R.; Deelman, Ewa; Natale, Frank Di; Tommaso, Paolo Di; Fahringer, Thomas; Filgueira, Rosa; Fursin, Grigori; Ganose, Alex; Grüning, Björn; Katz, Daniel S.; Kuchar, Olga; Kupresanin, Ana; Ludäscher, Bertram; Maheshwari, Ketan; Mattoso, Marta; Mehta, Kshitij; Munson, Todd; Ozik, Jonathan; Peterka, Tom; Pottier, Loïc; Randles, Tim; Soiland‐Reyes, Stian; Tovar, Benjamín; Turilli, Matteo; Uram, Thomas D.; Vahi, Karan; Wilde, Michael; Wolf, Matthew; Wozniak, Justin M.

doi:10.5281/zenodo.4606958

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…There have been significant recent efforts to FAIRify various aspects of scientific computational workflows. In practice, the very large number of workflow technologies and standards has made this a non-trivial task [10].…”

Section: Fair Workflowsmentioning

confidence: 99%

User-friendly Composition of FAIR Workflows in a Notebook Environment

Richardson

Çelebi

Burg

et al. 2021

Proceedings of the 11th Knowledge Capture Conference

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There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles to scientific computational workflows. Jupyter notebooks are a very popular medium by which to program and communicate computational scientific analyses. However, they present unique challenges when it comes to reuse of only particular steps of an analysis without disrupting the usual flow and benefits of the notebook approach, making it difficult to fully comply with the FAIR principles. Here we present an approach and toolset for adding the power of semantic technologies to Pythonencoded scientific workflows in a simple, automated and minimally intrusive manner. The semantic descriptions are published as a series of nanopublications that can be searched and used in other notebooks by means of a Jupyter Lab plugin. We describe the implementation of the proposed approach and toolset, and provide the results of a user study with 15 participants, designed around image processing workflows, to evaluate the usability of the system and its perceived effect on FAIRness. Our results show that our approach is feasible and perceived as user-friendly. Our system received an overall score of 78.75 on the System Usability Scale, which is above the average score reported in the literature. CCS CONCEPTS• Software and its engineering → Software creation and management.

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Section: Fair Workflowsmentioning

confidence: 99%

User-friendly Composition of FAIR Workflows in a Notebook Environment

Richardson

Çelebi

Burg

et al. 2021

Proceedings of the 11th Knowledge Capture Conference

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“…We are not aware of prior attempts to survey network testbed users about obstacles in their testbed interaction. However, related works exist in other fields that use shared infrastructure, such as robotics [10] and scientific experimentation [8,17].…”

Section: Related Workmentioning

confidence: 99%

“…Da Silva et al report on a workshop on needs of the scientific workflows community [8]. Scientific workflows are different than testbed experimentation in a sense that they focus primarily on computation and data transfer, and that workflow managers offer good support for the entire experimentation lifecycle (running, analysis, sharing, etc.)…”

Section: Related Workmentioning

confidence: 99%

User Experiences on Network Testbeds

Mirković¹,

Pusey²

2021

Cyber Security Experimentation and Test Workshop

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Network testbeds are used by researchers to evaluate their research products in a controlled setting. Teachers and students also use network testbeds in classes to facilitate active learning in authentic settings. However, testbeds have scarce human resources to develop documentation or support users one-on-one. Therefore, using testbeds can be difficult, especially for novice users. A user's lack of experience, coupled with user support deficiencies, can turn into research or learning obstacles.In this paper we report on two surveys we administered to investigate and document possible obstacles in user interaction with network testbeds. In the first survey we conducted interviews with 13 students that used a network testbed in class. Informed by their answers, we created the second, more comprehensive online survey and circulated it to both research and education users of network testbeds. We received 69 responses. User responses indicate three broad sources of usability challenges: orientational -learning a new environment, implementational -setting up and running experiments and domain-specific -monitoring experiments and diagnosing failures. Responses further show that most users overcome their initial orientational obstacles, but that implementational and domain-specific obstacles remain and should be addressed by testbeds through significant new developments. Overall, users regard network testbeds as a positive and useful influence on their learning and research.

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“…The first summit [30] brought together 48 international participants representing many WMSs, with the goal to identify crucial challenges in the workflows community. The summit considered six broad themes: FAIR workflows, training and education, AI workflows, exascale challenges, APIs and interoperability, and developing workflow community.…”

Section: Building a Workflows Communitymentioning

confidence: 99%

ExaWorks: Workflows for Exascale

Al-Saadi¹,

Ahn²,

Babuji³

et al. 2021

Preprint

Self Cite

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Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. These software combinations and integrations, however, are difficult to achieve due to challenges of coordination and deployment of heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which can address many of these challenges: ExaWorks is leading a co-design process to create a workflow Software Development Toolkit (SDK) consisting of a wide range of workflow management tools that can be composed and interoperate through common interfaces. We describe the initial set of tools and interfaces supported by the SDK, efforts to make them easier to apply to complex science challenges, and examples of their application to exemplar cases. Furthermore, we discuss how our project is working with the workflows community, large computing facilities as well as HPC platform vendors to sustainably address the requirements of workflows at the exascale.

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Workflows Community Summit: Bringing the Scientific Workflows Community Together

Cited by 5 publications

References 11 publications

User-friendly Composition of FAIR Workflows in a Notebook Environment

User-friendly Composition of FAIR Workflows in a Notebook Environment

User Experiences on Network Testbeds

ExaWorks: Workflows for Exascale

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