Alban Gaignard

I obtained a Ph.D. in Computer Science from the University of Nice-Sophia Antipolis. My research interests cover knowledge graphs and distributed systems (workflows, provenance) in the context of life sciences.

From 2018 to 2020, I co-lead the INEXMED integrative bioinformatics project funded by IFB (Institut Français de Bioinformatique) to design a FAIR data infrastructure bridging imaging and sequencing data.

Since 2019 I co-lead the IFB interoperability working group and represents the french activities in this domain for the European ELIXIR infrastructure.

Since 2020, I co-lead the R2P2 interdisciplinary project funded by CNRS, and aimed at better sharing and citing scientific workflows.

news

Sep 8, 2023	Invited talk to present Provenance metadata in workflows at CNRS “Reproducibility and Replicability in Science” workshop [Slides].
Sep 4, 2023	“Schema.org: How is it used ?” poster accepted at ISWC2023.
Aug 19, 2023	Invited talk on intracranial aneurysm knowledge graph and ontologies at ICS (Interdisciplinary Cerbrovascular Symposium) [Slides]
Jul 7, 2023	FAIR-Checker presented at SOSEM, the Open Science workshop associated to PFIA 2023. [(slides)]
Jul 7, 2023	FAIR-Checker paper published in Journal of Biomedical Semantics. https://doi.org/10.1186/s13326-023-00289-5
May 31, 2023	Invited talk to present Bioschemas at “machine actionable software management plans” Elixir workshop in Cologne.
Apr 27, 2023	Invited talk to present Bioschemas at the Elixir Machine Learning SIS kick-off meeting in Padova.
Mar 28, 2023	Invited talk to present Bioschemas at the Elixir Tools Platform face-to-face meeting in Leiden.
Nov 29, 2022	Invited talk to present FAIR-Checker at the EOSC General Assembly.
Aug 29, 2022	Invited talk to present FAIR-Checker at the Agro-Hackathon in Montpellier.

selected publications

FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards

Alban Gaignard, Thomas Rosnet, Frédéric Lamotte, and 2 more authors

Journal of Biomedical Semantics, Dec 2023

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Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems

Marine Djaffardjy, George Marchment, Clémence Sebe, and 5 more authors

Computational and Structural Biotechnology Journal, Dec 2023

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Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.
Large-scale regulatory and signaling network assembly through linked open data

Marie Lefebvre, Alban Gaignard, Maxime Folschette, and 2 more authors

Database: The Journal of Biological Databases and Curation, Dec 2021
Findable and reusable workflow data products: A genomic workflow case study

Alban Gaignard, Hala Skaf-Molli, and Khalid Belhajjame

Semantic Web, Dec 2020
Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population

Olivia Rousseau, Matilde Karakachoff, Alban Gaignard, and 15 more authors

Journal of Neurology, Neurosurgery, and Psychiatry, Dec 2020
Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

Sarah Cohen-Boulakia, Khalid Belhajjame, Olivier Collin, and 11 more authors

Future Generation Computer Systems, Dec 2017

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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.