User-friendly Composition of FAIR Workflows in a Notebook Environment.

Robin A. Richardson; Remzi Celebi; Sven van der Burg; Djura Smits; Lars Ridder; Michel Dumontier; Tobias Kuhn

doi:10.1145/3460210.3493546

User-friendly Composition of FAIR Workflows in a Notebook Environment.

Robin A. Richardson, Remzi Celebi, Sven van der Burg, Djura Smits, Lars Ridder, Michel Dumontier, Tobias Kuhn^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

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 Python-encoded 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.

Original language	English
Title of host publication	Proceedings of the 11th on Knowledge Capture Conference (K-CAP '21)
Publisher	Association for Computing Machinery
Pages	1-8
Number of pages	8
ISBN (Print)	978-1-4503-8457-5
DOIs	https://doi.org/10.1145/3460210.3493546
Publication status	Published - 2021
Event	the 11th Knowledge Capture Conference - Virtual Event USA Duration: 2 Dec 2021 → 3 Dec 2021 https://dl.acm.org/doi/proceedings/10.1145/3460210

Conference

Conference	the 11th Knowledge Capture Conference
Abbreviated title	K-CAP '21
Period	2/12/21 → 3/12/21
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3460210

Access to Document

10.1145/3460210.3493546Licence: CC BY

https://dblp.org/rec/conf/kcap/RichardsonCBSRD21

Cite this

@inproceedings{3a2e7a8451674aeea3b2b5d64b787fc7,

title = "User-friendly Composition of FAIR Workflows in a Notebook Environment.",

abstract = "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 Python-encoded 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.",

author = "Richardson, {Robin A.} and Remzi Celebi and Burg, {Sven van der} and Djura Smits and Lars Ridder and Michel Dumontier and Tobias Kuhn",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; the 11th Knowledge Capture Conference, K-CAP '21 ; Conference date: 02-12-2021 Through 03-12-2021",

year = "2021",

doi = "10.1145/3460210.3493546",

language = "English",

isbn = "978-1-4503-8457-5",

pages = "1--8",

booktitle = "Proceedings of the 11th on Knowledge Capture Conference (K-CAP '21)",

publisher = "Association for Computing Machinery",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3460210",

}

User-friendly Composition of FAIR Workflows in a Notebook Environment. / Richardson, Robin A.; Celebi, Remzi; Burg, Sven van der et al.
Proceedings of the 11th on Knowledge Capture Conference (K-CAP '21). Association for Computing Machinery, 2021. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - User-friendly Composition of FAIR Workflows in a Notebook Environment.

AU - Richardson, Robin A.

AU - Celebi, Remzi

AU - Burg, Sven van der

AU - Smits, Djura

AU - Ridder, Lars

AU - Dumontier, Michel

AU - Kuhn, Tobias

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

N2 - 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 Python-encoded 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.

AB - 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 Python-encoded 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.

U2 - 10.1145/3460210.3493546

DO - 10.1145/3460210.3493546

M3 - Conference article in proceeding

SN - 978-1-4503-8457-5

SP - 1

EP - 8

BT - Proceedings of the 11th on Knowledge Capture Conference (K-CAP '21)

PB - Association for Computing Machinery

T2 - the 11th Knowledge Capture Conference

Y2 - 2 December 2021 through 3 December 2021

ER -