EOSC-Life Methodology framework to enhance reproducibility within EOSC-Life – revised version
Creators: name: Bietrix, Florence
name: Carazo, José Maria
name: Capella-Gutierrez, Salvador
name: Coppens, Frederik
name: Chiusano, Maria Luisa
name: David, Romain
name: Fernandez, Jose Maria
name: Fratelli, Maddalena
name: Heriche, Jean-Karim
name: Goble, Carole
name: Gribbon, Philip
name: Holub, Petr
name: P. Joosten, Robbie
name: Leo, Simone
name: Owen, Stuart
name: Parkinson, Helen
name: Pieruschka, Roland
name: Pireddu, Luca
name: Porcu, Luca
name: Raess, Michael
name: Rodriguez-Navas, Laura
name: Scherer, Andreas
name: Soiland-Reyes, Stian
name: Tang, Jing
name: Saunders, Gary
Publisher: name: Zenodo
Year: 2022
Subjects: Reproducibility
Open Science
Data sharing
Data sharing tools
Life science research infrastructures

description: The original scope of task 8.3 is to develop a framework to assess the impact on reproducibility of the availability of life-science open data and workflows in the cloud. Such a framework should include the understanding of the tools and services produced and made available, the gaps they are filling up and their expected impact. A great part of the activities within EOSC-Life is actually related to reproducibility and provides in several ways tools that will have an impact. We describe here such activities and explain why and how they will have an impact on reproducibility in life sciences. In addition, for each action, we provide an indication of how we could measure its impact in practical ways, using adequate proxies. For these reasons, we changed the title of the deliverable, from “framework to assess ...” to “framework to enhance reproducibility”. First of all, we reasoned on what can be the contribution of open science to improve the reproducibility of research. Publicly sharing data, protocols, tools and computational workflows makes it possible to compare or combine the data and outcomes from different studies within a discipline as well as integrate data across scientific domains. It allows conclusions to be validated and possibly corrected as well as being reinforced by meta-analyses. Replication data and test/training data can also be used in many applications to contribute to reproducible research. Moreover, new hypotheses, different from the original aims of the study, can be explored. Datasets can be re-used to develop and test new methods, to conduct scientific and technical benchmarking activities and to support training activities. Therefore, in addition to generating more value from research investments, data sharing has the potential to increase confidence in research outcomes and increase knowledge dissemination. These benefits of open sharing have long been recognized in some fields such as bioinformatics, which has a long history of publicly sharing data with, for example, public repositories for nucleotide sequences going back 30 years and the Protein Data Bank (PDB), a repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies, that celebrates its 50th birthday this year. In this notion, any improvement in sharing of data, tools and workflows among scientists and across disciplines, which is the aim of EOSC-Life and the wider EOSC, will contribute to reproducible science. In addition to this general scope, several specific actions to frame transparency in the reporting of experimental protocols, data and analytical workflows warrant the reproducibility of every single object (experimental results, data, or workflows) that is made available on the cloud. We describe here the initiatives in EOSC-Life to implement existing tools for reproducibility as well as to develop new tools for its enhancement. As the final goal of EOSC-Life is to make data resources available to the wider community of life scientists, although necessarily technical in several points, this document aims at a general readership, including experimental in addition to data scientists.