Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities
With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.
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Subjects: | C30 - Documentation et information, U30 - Méthodes de recherche, informatique, analyse de données, fouille de textes, http://aims.fao.org/aos/agrovoc/c_27769, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_dca12b72, |
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dig-cirad-fr-5832552024-01-29T05:38:00Z http://agritrop.cirad.fr/583255/ http://agritrop.cirad.fr/583255/ Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities. Cohen-Boulakia Sarah, Belhajjame Khalid, Collin Olivier, Chopard Jérôme, Froidevaux Christine, Gaignard Alban, Hinsen Konrad, Larmande Pierre, Le Bras Yvan, Lemoine Frédéric, Mareuil Fabien, Ménager Hervé, Pradal Christophe, Blanchet Christophe. 2017. Future Generation Computer Systems, 75 : 284-298.https://doi.org/10.1016/j.future.2017.01.012 <https://doi.org/10.1016/j.future.2017.01.012> Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities Cohen-Boulakia, Sarah Belhajjame, Khalid Collin, Olivier Chopard, Jérôme Froidevaux, Christine Gaignard, Alban Hinsen, Konrad Larmande, Pierre Le Bras, Yvan Lemoine, Frédéric Mareuil, Fabien Ménager, Hervé Pradal, Christophe Blanchet, Christophe eng 2017 Future Generation Computer Systems C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/acceptedVersion http://agritrop.cirad.fr/583255/1/scientific-workflows-computational%283%29.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.future.2017.01.012 10.1016/j.future.2017.01.012 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.future.2017.01.012 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.future.2017.01.012 |
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C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 |
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C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 Cohen-Boulakia, Sarah Belhajjame, Khalid Collin, Olivier Chopard, Jérôme Froidevaux, Christine Gaignard, Alban Hinsen, Konrad Larmande, Pierre Le Bras, Yvan Lemoine, Frédéric Mareuil, Fabien Ménager, Hervé Pradal, Christophe Blanchet, Christophe Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
description |
With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments. |
format |
article |
topic_facet |
C30 - Documentation et information U30 - Méthodes de recherche informatique analyse de données fouille de textes http://aims.fao.org/aos/agrovoc/c_27769 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_dca12b72 |
author |
Cohen-Boulakia, Sarah Belhajjame, Khalid Collin, Olivier Chopard, Jérôme Froidevaux, Christine Gaignard, Alban Hinsen, Konrad Larmande, Pierre Le Bras, Yvan Lemoine, Frédéric Mareuil, Fabien Ménager, Hervé Pradal, Christophe Blanchet, Christophe |
author_facet |
Cohen-Boulakia, Sarah Belhajjame, Khalid Collin, Olivier Chopard, Jérôme Froidevaux, Christine Gaignard, Alban Hinsen, Konrad Larmande, Pierre Le Bras, Yvan Lemoine, Frédéric Mareuil, Fabien Ménager, Hervé Pradal, Christophe Blanchet, Christophe |
author_sort |
Cohen-Boulakia, Sarah |
title |
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
title_short |
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
title_full |
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
title_fullStr |
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
title_full_unstemmed |
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities |
title_sort |
scientific workflows for computational reproducibility in the life sciences: status, challenges and opportunities |
url |
http://agritrop.cirad.fr/583255/ http://agritrop.cirad.fr/583255/1/scientific-workflows-computational%283%29.pdf |
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