Towards the Implementation of FAIR Principles on an Earthquake Analysis Platform

Pană, Gabriel Tiberiu; Ivanoaica, Teodor; Raportaru, Mihaela Carina; Baran, V.; Nicolin, Alexandru I.

doi:10.1109/roedunet54112.2021.9638283

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…In Assante et al (2021), the authors propose the AGINFRA PLUS platform, which enables researchers to store, analyze, visualize, and publish agriculture and food data in accordance with open science. Moreover, Panȃ et al (2021) developed a pipeline that extracts data and metadata from several seismic databases available online, integrating them into a single repository stored in a PostgreSQL local database to enable analytics. Furthermore, the work of Pestryakova et al (2022) describes a pipeline that extracts data and metadata from COVID-19 related publications, transforming them into CovidPubGraph, a knowledge graph which, in itself, can be considered as a data sharing repository.…”

Section: Group 2: Pipelines To Implement Specific Fair Repositoriesmentioning

confidence: 99%

“…Additionally, both BigFAIR [Castro et al 2022a] and CloudFAIR [Castro et al 2022b] use COVID-19 patients data in their experiments, whereas GADDS [Vazquez et al 2022] employs fiber cell tissue research data during its instantiation. Another commonly addressed context is that of earth-related data, being covered by the repositories of Panȃ et al (2021), Felikson et al (2022), Sciacca et al (2022), andRueda-Ruiz et al (2022). The proportion of studies per data context is illustrated in Figure 2b.…”

Section: Tendenciesmentioning

confidence: 99%

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Big Data Architectures for FAIR-compliant Repositories: A Systematic Review

Castro,

Aguiar

2023

Anais Do XXXVIII Simpósio Brasileiro De Banco De Dados (SBBD 2023)

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The FAIR Principles state that scientific data should be Findable, Accessible, Interoperable, and Reusable in order to adhere to the Open Science movement. However, designing a FAIR-compliant repository can be a challenge due to the complexity of managing a huge volume and variety of research data and metadata, which can also be generated at a high velocity. This complexity calls for a Software Reference Architecture (SRA) to guide data engineers during the implementation process. In this paper, we conduct a systematic review that encompasses research efforts regarding architectural solutions for implementing FAIR-compliant repositories. We analyze 323 references from Scopus, ACM, IEEEXplore, and specialists recommendations. From this analysis, we discover 7 studies that describe general purpose big data SRAs, 13 pipelines that implement the FAIR Principles to specific contexts, and 3 FAIR-compliant big data SRAs. We describe their key characteristics and discuss their limitations, highlighting tendencies and research opportunities.

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Section: Group 2: Pipelines To Implement Specific Fair Repositoriesmentioning

confidence: 99%