Étiquette : FAIR Data

Catégories
EN

Towards FAIR protocols and workflows: the OpenPREDICT use case

Authors : Remzi Celebi, Joao Rebelo Moreira, Ahmed A. Hassan, Sandeep Ayyar, Lars Ridder, Tobias Kuhn, Michel Dumontier

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others.

The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data.

We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces.

We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN.

This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

URL : Towards FAIR protocols and workflows: the OpenPREDICT use case

DOI : https://doi.org/10.7717/peerj-cs.281

Catégories
EN

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

Authors : Romain David, Laurence Mabile, Alison Specht, Sarah Stryeck, Mogens Thomsen, Mohamed Yahia, Clement Jonquet, Laurent Dollé, Daniel Jacob, Daniele Bailo, Elena Bravo, Sophie Gachet, Hannah Gunderman, Jean-Eudes Hollebecq, Vassilios Ioannidis, Yvan Le Bras, Emilie Lerigoleur, Anne Cambon-Thomsen, The Research Data Alliance – SHAring Reward and Credit (SHARC) Interest Group

The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing.

This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation.

It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process.

Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding.

These are essential milestones in developing adapted support and credit back mechanisms not yet in place.

URL : FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

DOI : http://doi.org/10.5334/dsj-2020-032

Catégories
EN

Low availability of code in ecology: A call for urgent action

Authors : Antica Culina, Ilona van den Berg, Simon Evans, Alfredo Sánchez-Tójar

Access to analytical code is essential for transparent and reproducible research. We review the state of code availability in ecology using a random sample of 346 nonmolecular articles published between 2015 and 2019 under mandatory or encouraged code-sharing policies.

Our results call for urgent action to increase code availability: only 27% of eligible articles were accompanied by code. In contrast, data were available for 79% of eligible articles, highlighting that code availability is an important limiting factor for computational reproducibility in ecology.

Although the percentage of ecological journals with mandatory or encouraged code-sharing policies has increased considerably, from 15% in 2015 to 75% in 2020, our results show that code-sharing policies are not adhered to by most authors.

We hope these results will encourage journals, institutions, funding agencies, and researchers to address this alarming situation.

URL : Low availability of code in ecology: A call for urgent action

DOI : https://doi.org/10.1371/journal.pbio.3000763

Catégories
FR

Le principe d’ouverture des données de la recherche scientifique. Réflexions autour du croisement de l’informatique et du droit

Auteur/Author : Agnès Robin

Les données de la recherche scientifique sont actuellement soumises à un programme de standardisation technique (FAIR) dont l’objectif est d’en permettre la diffusion aux fins de réutilisation par le public (entreprises privées ou autre).

Cette politique, qui sans se confondre avec elle, converge avec celle dite de « science ouverte », s’articule autour d’un principe normatif conflictuel, selon lequel le résultats de la recherche (et donc les données) doivent être « aussi ouverts que possibles et pas plus fermés que nécessaire », obligeant alors les chercheurs, ingénieurs et documentalistes, éventuellement chargés de la gestion des données de la recherche, à procéder à une qualification juridique délicate des données.

URL : http://intelligibilite-numerique.numerev.com/index.php/numeros/n-1-2020/9-le-principe-d-ouverture-des-donnees-de-la-recherche-scientifique

Catégories
EN

The role of metadata in reproducible computational research

Authors : Jeremy Leipzig, Daniel Nüst, Charles Tapley Hoyt, Stian Soiland-Reyes, Karthik Ram, Jane Greenberg

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results.

In addition to its role in research integrity, RCR has the capacity to significantly accelerate evaluation and reuse. This potential and wide-support for the FAIR principles have motivated interest in metadata standards supporting RCR.

Metadata provides context and provenance to raw data and methods and is essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described the relationship between metadata and RCR.

This article employs a functional content analysis to identify metadata standards that support RCR functions across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications.

Our article provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

URL : The role of metadata in reproducible computational research

Original location : https://arxiv.org/abs/2006.08589

Catégories
Non classé

Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications

Authors : Antonia Lock, Midori A Harris, Kim Rutherford, Jacqueline Hayles, Valerie Wood

Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases.

To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments.

Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase’s system.

With 8 years’ experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project.

We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.

URL : Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications

DOI : https://doi.org/10.1093/database/baaa028

Catégories
EN

FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Authors : Koenraad De Smedt, Dimitris Koureas, Peter Wittenburg

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability.

As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers.

In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data.

The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions.

We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

URL : FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Alternative location : https://www.mdpi.com/2304-6775/8/2/21