Publications

FAIRsharing as a community approach to standards, repositories and policies

Published in Nature Biotechnology, 2019

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Recommended citation: FAIRsharing as a community approach to standards, repositories and policies Susanna-Assunta Sansone, Peter McQuilton, Philippe Rocca-Serra, Alejandra Gonzalez-Beltran, Massimiliano Izzo, Allyson L. Lister, Milo Thurston & the FAIRsharing Community Nat Biotechnol. 2019 Apr;37(4):358-367. doi: https://doi.org/10.1038/s41587-019-0080-8 https://doi.org/10.1038/s41587-019-0080-8

Interoperable and scalable data analysis with microservices: applications in metabolomics

Published in Bioinformatics, 2019

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Read more

Recommended citation: Payam Emami Khoonsari, Pablo Moreno, Sven Bergmann, Joachim Burman, Marco Capuccini, Matteo Carone, Marta Cascante, Pedro de Atauri, Carles Foguet, Alejandra N Gonzalez-Beltran, Thomas Hankemeier, Kenneth Haug, Sijin He, Stephanie Herman, David Johnson, Namrata Kale, Anders Larsson, Steffen Neumann, Kristian Peters, Luca Pireddu, Philippe Rocca-Serra, Pierrick Roger, Rico Rueedi, Christoph Ruttkies, Noureddin Sadawi, Reza M Salek, Susanna-Assunta Sansone, Daniel Schober, Vitaly Selivanov, Etienne A Thévenot, Michael van Vliet, Gianluigi Zanetti, Christoph Steinbeck, Kim Kultima, Ola Spjuth, Interoperable and scalable data analysis with microservices: applications in metabolomics, Bioinformatics, , btz160, https://doi.org/10.1093/bioinformatics/btz160 https://doi.org/10.1093/bioinformatics/btz160

Discovering Data Access and Use Requirements Using the Data Tag Suite (DATS)

Published in bioRxiv, 2019

This paper is about the representation of data access and data use requirements for the Data Tag Suite (DATS) model. Read more

Recommended citation: Discovering Data Access and Use Requirements Using the Data Tag Suite (DATS) Model George Alter, Alejandra Gonzalez-Beltran, Lucila Ohno-Machado, Philippe Rocca-Serra bioRxiv 518571; doi: https://doi.org/10.1101/518571 https://doi.org/10.1101/518571

PhenoMeNal: processing and analysis of metabolomics data in the cloud

Published in GigaScience, 2018

This paper PhenoMeNal provides a cloud e-infrastructures solution to analyse metabolomics data. It provides easy-to-use web interfaces that can be scaled to any custom public and private cloud environment.. Read more

Recommended citation: Kristian Peters, James Bradbury, Sven Bergmann, Marco Capuccini, Marta Cascante, Pedro de Atauri, Timothy M D Ebbels, Carles Foguet, Robert Glen, Alejandra Gonzalez-Beltran, Ulrich L Günther, Evangelos Handakas, Thomas Hankemeier, Kenneth Haug, Stephanie Herman, Petr Holub, Massimiliano Izzo, Daniel Jacob, David Johnson, Fabien Jourdan, Namrata Kale, Ibrahim Karaman, Bita Khalili, Payam Emami Khonsari, Kim Kultima, Samuel Lampa, Anders Larsson, Christian Ludwig, Pablo Moreno, Steffen Neumann, Jon Ander Novella, Claire O'Donovan, Jake T M Pearce, Alina Peluso, Marco Enrico Piras, Luca Pireddu, Michelle A C Reed, Philippe Rocca-Serra, Pierrick Roger, Antonio Rosato, Rico Rueedi, Christoph Ruttkies, Noureddin Sadawi, Reza M Salek, Susanna-Assunta Sansone, Vitaly Selivanov, Ola Spjuth, Daniel Schober, Etienne A Thévenot, Mattia Tomasoni, Merlijn van Rijswijk, Michael van Vliet, Mark R Viant, Ralf J M Weber, Gianluigi Zanetti, Christoph Steinbeck; PhenoMeNal: processing and analysis of metabolomics data in the cloud, GigaScience, Volume 8, Issue 2, 1 February 2019, giy149, [https://doi.org/10.1093/gigascience/giy149](https://doi.org/10.1093/gigascience/giy149) https://doi.org/10.1093/gigascience/giy149

Data discovery with DATS: exemplar adoptions and lessons learned

Published in Journal of the American Medical Informatics Association, 2017

This paper analyses the implementation of the DATS model for data discovery in a set of exemplar data sources Read more

Recommended citation: Alejandra N Gonzalez-Beltran, John Campbell, Patrick Dunn, Diana Guijarro, Sanda Ionescu, Hyeoneui Kim, Jared Lyle, Jeffrey Wiser, Susanna-Assunta Sansone, Philippe Rocca-Serra. "Data discovery with DATS: exemplar adoptions and lessons learned" Journal of the American Medical Informatics Association, Volume 25, Issue 1, 1 January 2018, Pages 13–16, https://doi.org/10.1093/jamia/ocx119 https://doi.org/10.1093/jamia/ocx119

The FAIR Guiding Principles for scientific data management and stewardship

Published in Scientific Data, 2016

This is the first formalisation of the FAIR guiding principes for data management and stewardship, which aim at making data Findable, Accessible, Interoperable and Reusable (FAIR). Read more

Recommended citation: Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and ’t Hoen, Peter A. C and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend. "The FAIR Guiding Principles for scientific data management and stewardship", Scientific Data, https://doi.org/10.1038/sdata.2016.18 https://doi.org/10.1038/sdata.2016.18