ABSTRACT
Since their proposal in 2016, the FAIR principles have been largely discussed by different communities and initiatives involved in the development of infrastructures to enhance support for data findability, accessibility, interoperability, and reuse. One of the challenges in implementing these principles lies in defining a well-delimited process with organized and detailed actions. This paper presents a workflow of actions that is being adopted in the VODAN BR pilot for generating FAIR (meta)data for COVID-19 research. It provides the understanding of each step of the process, establishing their contribution. In this work, we also evaluate potential tools to (semi)automatize (meta)data treatment whenever possible. Although defined for a particular use case, it is expected that this workflow can be applied for other epidemical research and in other domains, benefiting the entire scientific community.
ABSTRACT
Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines (that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and (re)usability of data, so that new and robust analytical tools, such as machine learning (ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture. © 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
ABSTRACT
This article describes the FAIRification process (which involves making data Findable, Accessible, Interoperable and Reusable – or FAIR – for both machines and humans) for data related to the impact of COVID-19 on migrants, refugees and asylum seekers in Tunisia, Libya and Niger, according to the scheme adopted by GO FAIR. This process was divided into three phases: pre-FAIRification, FAIRification and post-FAIRification. Each phase consisted of seven steps. In the first phase, 118 in-depth interviews and 565 press articles and research reports were collected by students and researchers at the University of Sousse in Tunisia and researchers in Niger. These interviews, articles and reports constitute the dataset for this research. In the second phase, the data were sorted and converted into a machine actionable format and published on a FAIR Data Point hosted at the University of Sousse. In the third phase, an assessment of the implementation of the FAIR Guidelines was undertaken. Certain barriers and challenges were faced in this process and solutions were found. For FAIR data curation, certain changes need to be made to the technical process. People need to be convinced to make these changes and that the implementation of FAIR will generate a long-term return on investment. Although the implementation of FAIR Guidelines is not straightforward, making our resources FAIR is essential to achieving better science together. © 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
ABSTRACT
Since their proposal in 2016, the FAIR principles have been largely discussed by different communities and initiatives involved in the development of infrastructures to enhance support for data findability, accessibility, interoperability, and reuse. One of the challenges in implementing these principles lies in defining a well-delimited process with organized and detailed actions. This paper presents a workflow of actions that is being adopted in the VODAN BR pilot for generating FAIR (meta)data for COVID-19 research. It provides the understanding of each step of the process, establishing their contribution. In this work, we also evaluate potential tools to (semi)automatize (meta)data treatment whenever possible. Although defined for a particular use case, it is expected that this workflow can be applied for other epidemical research and in other domains, benefiting the entire scientific community. © 2022, Springer Nature Switzerland AG.