Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Schwanitz, Valeria Jana; Wierling, A.; Biresselioğlu, Mehmet Efe; Celino, Massimo; Demir, Muhittin Hakan; Bałazińska, Maria; Kruczek, Mariusz; Paier, Manfred; Suna, Demet

doi:10.1038/s41598-022-08774-0

Cited by 6 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1, we highlight that the Identification Service shows another crucial part of the FAIR ecosystem, as mandated by principle F1. Schwanitz et al [29] reviewed 80 representative databases employed in research on low carbon energy using the automatic evaluation framework proposed by proponents of FAIR principles [34]. However, none of these databases complied with F1, which means that data identifiers are not persistent in any of these cases.…”

Section: ) Under-specified Areasmentioning

confidence: 99%

“…2, when data is inaccessible (i.e., the complement class of Accessible Data in that model), Metadata should be accessible through the corresponding event. However, there are still several examples in the literature where this does not occur; e.g., in the energy domain [29] or the genomics domain [23].…”

Section: ) Under-specified Areasmentioning

confidence: 99%

“…For instance, Uniprot [26] claims to be FAIR for all other principles except R1.3, because the recognized authority is not known within the field of proteins [7]). Similarly, this aspect also remains unresolved in the field of low carbon energy data after a systematic assessment [29].…”

Section: ) Under-specified Areasmentioning

confidence: 99%

See 2 more Smart Citations

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Bernasconi¹,

S.²,

Guizzardi³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the 'FAIR' acronym becoming a common part of the vocabulary of data scientists. However, there is still some controversy on how datasets should be interpreted since not all datasets that are claimed to be FAIR, necessarily follow the principles. In this research, we propose the OntoUML FAIR Principles Schema, as an ontological representation of FAIR principles for data practitioners. The work is based on OntoUML, an ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is a proxy for ontological analysis that has proven effective in supporting the explanation of complex domains. Our schema aims to disentangle the intricacies of the FAIR principles' definition, by resolving aspects that are ambiguous, under-specified, recursively-specified, or implicit. The schema can be considered as a blueprint, or a template to follow when the FAIR classification strategy of a dataset must be designed. To demonstrate the usefulness of the schema, we present a practical example based on genomic data and discuss how the results provided by the OntoUML FAIR Principles Schema contribute to existing data guidelines.

show abstract

Section: ) Under-specified Areasmentioning

confidence: 99%

Section: ) Under-specified Areasmentioning

confidence: 99%

See 1 more Smart Citation

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Bernasconi¹,

S.²,

Guizzardi³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Some studies have been able to perform assessments; for example, a study has been conducted on 80 databases that are representative of the data streams that are critical for low carbon energy transition, where the results obtained reveal the difficulty of translating FAIR guidance principles into domain-specific applications, as current FAIR data practices in the energy domain are yet to be developed, although the community has initiated efforts but still the platforms and tools are not yet ready to be integrated into the workflows of research teams (Schwanitz et al, 2022).…”

Section: Implementation Of the Principles In Research Centersmentioning

confidence: 99%

Fair Data: History and Present Context

Carballo-Garcia

Boté‐Vericad

2022

CEJER

View full text Add to dashboard Cite

In this paper, we discuss FAIR Data, why it exists, and who it applies to. We further review the principles of FAIR data and how they are managed in research centers. We also discuss the types of problems that researchers encounter, and what an information professional can do to assist them. At present, the vast majority of centers subscribe to the FAIR principles. However, both center and researcher face the arduous task of understanding, managing, and implementing the model. They must know data formats and standards. For a correct description and to facilitate data retrieval and interoperability, they must know about different types of metadata schemas. They must know about digital preservation and specific aspects of knowledge and information management. In addition, there are also ethical issues, intellectual property, and cultural differences. All these controversies translate into extra workload for researchers, who only get a return in the form of citations. It is critical to note that these information professionals can play a key role in the proper management of research data, and can help achieve the objectives described in the principles: making data findable, accessible, interoperable, and reusable.

show abstract

“…Furthermore, the lessons learned could be applied in part to other contexts such as low carbon energy databases (DB) mentioned in [ 17 ]. The latter report that energy DBs are significantly heterogeneous and can benefit from common data exchange formats and semantic representations to improve interoperability among these DBs.…”

Section: Introductionmentioning

confidence: 99%

Lessons learned to boost a bioinformatics knowledge base reusability, the Bgee experience

Mendes de Farias,

Wollbrett,

Robinson-Rechavi

et al. 2022

GigaScience

View full text Add to dashboard Cite

Background Enhancing interoperability of bioinformatics knowledge bases is a high-priority requirement to maximize data reusability and thus increase their utility such as the return on investment for biomedical research. A knowledge base may provide useful information for life scientists and other knowledge bases, but it only acquires exchange value once the knowledge base is (re)used, and without interoperability, the utility lies dormant. Results In this article, we discuss several approaches to boost interoperability depending on the interoperable parts. The findings are driven by several real-world scenario examples that were mostly implemented by Bgee, a well-established gene expression knowledge base. To better justify the findings are transferable, for each Bgee interoperability experience, we also highlight similar implementations by major bioinformatics knowledge bases. Moreover, we discuss ten general main lessons learned. These lessons can be applied in the context of any bioinformatics knowledge base to foster data reusability. Conclusions This work provides pragmatic methods and transferable skills to promote reusability of bioinformatics knowledge bases by focusing on interoperability.

show abstract

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Cited by 6 publications

References 36 publications

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Fair Data: History and Present Context

Lessons learned to boost a bioinformatics knowledge base reusability, the Bgee experience

Contact Info

Product

Resources

About