Archives des mots-clés : FAIR Data

Evaluating FAIR maturity through a scalable, automated, community-governed framework

Posté le par

Authors : Mark D. Wilkinson, Michel Dumontier, Susanna-Assunta Sansone, Luiz Olavo Bonino da Silva Santos, Mario Prieto, Dominique Batista, Peter McQuilton, Tobias Kuhn, Philippe Rocca-Serra, Mercѐ Crosas, Erik Schultes

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments.

The components of the framework are: (1) Maturity Indicators – community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests – small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine “sees” when it visits that resource.

We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.

URL : Evaluating FAIR maturity through a scalable, automated, community-governed framework

DOI : https://doi.org/10.1038/s41597-019-0184-5

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Posté le par

Authors : Kevin M. Mendez, Leighton Pritchard, Stacey N. Reinke, David I. Broadhurst

Background

A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility.

The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases.

Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work.

To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike.

Aim of Review

To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science.

Key Scientific Concepts of Review

This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

URL : Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

DOI : https://doi.org/10.1007/s11306-019-1588-0

Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data

Posté le par

Authors : Agustin Barba, Santiago Dominguez, Carlos Cobas, David P. Martinsen, Charles Romain, Henry S. Rzepa; Felipe Seoane

There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse.

One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed.

In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines.

Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort.

Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository.

Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.

URL : Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data

DOI : https://doi.org/10.1021/acsomega.8b03005

On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

Posté le par

Authors : Heather Andrews Mancilla, Marta Teperek, Jasper van Dijck, Kees den Heijer, Robbert Eggermont, Esther Plomp, Yasemin Turkyilmaz-van der Velden, Shalini Kurapati

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties.

The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018.

The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty.

Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories.

The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results.

Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

URL : On a Quest for Cultural Change – Surveying Research Data Management Practices at Delft University of Technology

DOI : http://doi.org/10.18352/lq.10287

Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

Posté le par

Authors : Charles Vesteghem, Rasmus Froberg Brøndum, Mads Sønderkær, Mia Sommer, Alexander Schmitz, Julie Støve Bødker, Karen Dybkær, Tarec Christoffer El-Galaly, Martin Bøgsted

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance.

The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives.

For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively.

For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society’s standard.

For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

URL : Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

DOI : https://doi.org/10.1093/bib/bbz044

Are Research Datasets FAIR in the Long Run?

Posté le par

Authors : Dennis Wehrle, Klaus Rechert

Currently, initiatives in Germany are developing infrastructure to accept and preserve dissertation data together with the dissertation texts (on state level – bwDATA Diss, on federal level – eDissPlus).

In contrast to specialized data repositories, these services will accept data from all kind of research disciplines. To ensure FAIR data principles (Wilkinson et al., 2016), preservation plans are required, because ensuring accessibility, interoperability and re-usability even for a minimum ten year data redemption period can become a major challenge.

Both for longevity and re-usability, file formats matter. In order to ensure access to data, the data’s encoding, i.e. their technical and structural representation in form of file formats, needs to be understood. Hence, due to a fast technical lifecycle, interoperability, re-use and in some cases even accessibility depends on the data’s format and our future ability to parse or render these.

This leads to several practical questions regarding quality assurance, potential access options and necessary future preservation steps. In this paper, we analyze datasets from public repositories and apply a file format based long-term preservation risk model to support workflows and services for non-domain specific data repositories.

URL : Are Research Datasets FAIR in the Long Run?

DOI : https://doi.org/10.2218/ijdc.v13i1.659

Hors norme ? Une approche normative des données de la recherche

Posté le par

Auteur : Joachim Schöpfel

Nous proposons une réflexion sur le rôle des normes et standards dans la gestion des données de la recherche, dans l’environnement de la politique de la science ouverte.

A partir d’une définition générale des données de la recherche, nous analysons la place et la fonction des normes et standards dans les différentes dimensions du concept des données. En particulier, nous nous intéressons à trois aspects faisant le lien entre le processus scientifique, l’environnement réglementaire et les données de la recherche : les protocoles éthiques, les systèmes d’information recherche et les plans de gestion des données.

A l’échelle internationale, nous décrivons l’effet normatif des principes FAIR qui, par la mobilisation d’autres normes et standards, créent une sorte de « cascade de standards » autour des plateformes et entrepôts, avec un impact direct sur les pratiques scientifiques.

URL : https://revue-cossi.info/numeros/n-5-2018-processus-normalisation-durabilite-information/730-5-2018-schopfel