A design framework and exemplar metrics for FAIRness

Wilkinson, Mark D.; Sansone, Susanna-Assunta; Schultes, Erik; Doorn, Peter; Santos, Luiz Olavo Bonino da Silva; Dumontier, Michel

doi:10.1038/sdata.2018.118

Cited by 183 publications

(159 citation statements)

References 10 publications

Supporting

Mentioning

153

Contrasting

Unclassified

Order By: Relevance

“…The lack of practical specifications generated a large spectrum of interpretations and concerns and raised the need to define measurements of data FAIRness. Some of the authors of the seminal paper proposed a set of FAIR metrics [10], subsequently reformulated as FAIR maturity indicators [11]. At the same time, they invited consortia and communities to suggest and create alternative evaluators.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the proposed tools are online questionnaires that researchers and repository curators can manually fill to assess the FAIRness of their data (Table 1). However, the FAIR metrics guidelines emphasize the importance of creating "objective, quantitative, [and] machineinterpretable" evaluators [10]. Following these criteria, two platforms have recently been developed to automatically compute FAIR maturity indicators: FAIR Evaluation Services and FAIRshake.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Two real use cases of FAIR maturity indicators in the life sciences

Bonaretti

Willighagen

2019

Preprint

View full text Add to dashboard Cite

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult for lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated our method on two real use cases where researchers looked for datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress and Gene Expression Omnibus), a registry of repositories (re3data.org), and a searchable resource (Google Dataset Search) via application program interface (API) wherever possible. With our analysis, we found that the two datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and presence of specific attributes in registries of repositories could increase FAIRness of datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two real use cases of FAIR maturity indicators in the life sciences

Bonaretti

Willighagen

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…As the molecular size increases, so does the difficulty to implement refined techniques such as microkinetics or DFT characterization over the full reaction network, Scheme . To foster scientific discussions, all our DFT results are stored and can be retrieved from the ioChem‐BD repository, an open database that fulfils the FAIR principles of findability, accessibility, interoperability, and reusability.…”

Section: Introductionmentioning

confidence: 99%

“…In the foreseeable future, the combined use of databases and statistical learning algorithms will allow generating robust thermochemical models for large molecules . These databases should fulfill the FAIR principles, which require that data is findable, accessible, interoperable, and reusable . In this context, we have created the ioChem‐BD repository, in use since 2015, to store all calculations coming from our DFT‐based projects.…”

Section: Introductionmentioning

confidence: 99%

“…These provide a complete matrix of reactivity that can be downloaded as a csv file from the ioChem‐BD database to be further processed and then new elementary steps can be added if required. Since they are stored according to the FAIR database principles, the data can be employed by other researchers in the field of Catalysis to reduce the computational and human burden of calculating such large networks. Besides, the database is openly available to be further analyzed by anyone through statistical‐learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Developments in the Atomistic Modelling of Catalytic Processes for the Production of Platform Chemicals from Biomass

Garcı́a-Muelas

Rellán‐Piñeiro

et al. 2018

ChemCatChem

View full text Add to dashboard Cite

The transformation of biomass‐derived molecules into platform chemicals that can directly be employed by the chemical industry is one of the challenges in catalysis in this century. While some processes are cost‐effective and industrially available, the molecular insight has been advancing at a slow pace when compared to other areas (like energy conversion). In the present review we describe the main challenges imposed on the theoretical simulations for the study of these complex molecules and how the most crucial issues are typically addressed. In particular, we focus on technical aspects like the need for London dispersion and solvation contributions. We also deal with the complexity of reaction networks, which requires new approaches and ways to compile the results in the form of databases. This allows the study of large reaction networks for the decomposition of C2 alcohols, or the plethora of functional groups of cyclic molecules such as lignin and sugars. Finally, we put forward a few applications that show the potential of atomistic simulations in the field.

show abstract

Chemometrics Applied to Nuclear Magnetic Resonance Analysis

Beavis

Rusilowicz

Donarski

et al. 2019

Encyclopedia of Analytical Chemistry

View full text Add to dashboard Cite

Nuclear magnetic resonance (NMR) spectroscopy provides information about the physical and chemical properties of atoms within a sample and has become an essential tool in metabolomics. Although less sensitive than mass spectrometry, NMR spectroscopy is highly reproducible and gives wider coverage in comparison to other techniques used for the analysis of complex mixtures. Recent advances in technology, advances in methodology, and increased computer power have increased the need to develop mathematical and statistical methods to analyze and interpret the complex datasets acquired. This has resulted in developments in the field of chemical informatics known as chemometrics . In contrast to targeted analyses, chemometric approaches do not initially attempt to identify particular metabolites. Instead, statistical techniques and pattern recognition methods are used to identify spectral features that show consistent trends or provide discrimination between classes. These features can provide a fingerprint for a metabolic state to be used, for example, for disease diagnostics, and individual features of interest can then be related to specific compounds and metabolic pathways using database searches. NMR‐based chemometric methods are now used in a wide range of applications including clinical diagnostics, food science and traceability, process control, monitoring genetic modification, and predicting the side effects of pharmaceuticals and their environmental effects.

show abstract

A design framework and exemplar metrics for FAIRness

Cited by 183 publications

References 10 publications

Two real use cases of FAIR maturity indicators in the life sciences

Two real use cases of FAIR maturity indicators in the life sciences

Developments in the Atomistic Modelling of Catalytic Processes for the Production of Platform Chemicals from Biomass

Chemometrics Applied to Nuclear Magnetic Resonance Analysis

Contact Info

Product

Resources

About