Dataset Information

FAIR principles to improve the impact on health research management outcomes.

ABSTRACT:

Background

The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes.

Objective

This paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution.

Methods

To analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed.

Results

In the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used.

Conclusions

Adopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques.

SUBMITTER: Martinez-Garcia A

PROVIDER: S-EPMC10189186 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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FAIR principles to improve the impact on health research management outcomes.

Martínez-García Alicia A Alvarez-Romero Celia C Román-Villarán Esther E Bernabeu-Wittel Máximo M Luis Parra-Calderón Carlos C

Heliyon 20230503 5

<h4>Background</h4>The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research man ...[more]

PMID: 37205991

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Project description:BackgroundAccording to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1-22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data.Results5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809).ConclusionThis paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

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Project description:IntroductionChild mortality rates remain high in sub-Saharan Africa, including Ethiopia. We are conducting a cluster randomised control trial in the Gondar zone of the Amhara region to determine the impact of pairing Orthodox priests with community health workers, known locally as the Health Development Army (HDA), on newborns' nutritional status, early illness identification and treatment, and vaccination completeness.Ensuring intervention efficacy with scientific rigour is essential, but there are often delays in adopting evidence into policy and programmes. Here, we present a protocol for conducting parallel implementation research alongside an efficacy study to understand intervention implementability and scalability. This will help develop a scale-up strategy for effective elements of the intervention to ensure rapid implementation at scale.Methods and analysisWe will conduct a stakeholder analysis of key implementation stakeholders and readiness surveys to assess their readiness to scale up the intervention. We will conduct semistructured interviews and focus group discussions with stakeholders, including HDA members, health workers, Orthodox priests, and caregivers, to determine the core intervention elements that need to be scaled, barriers and facilitators to scaling up the intervention in diverse sociocultural settings, as well as the human and technical requirements for national and regional implementation. Finally, to determine the financial resources necessary for sustaining and scaling the intervention, we will conduct activity-based costing to estimate implementation costs from the provider's perspective.Ethics and disseminationThe study received approval from the University of Gondar Institutional Review Board (approval no: VP/RTT/05/1030/2022) and the University of Washington Human Subjects Division (approval no: STUDY00015369). Participants will consent to participate. Results will be disseminated through workshops with stakeholders, local community meetings, presentations at local and international conferences, and journal publications. The study will provide evidence for factors to consider in developing a scale-up strategy to integrate the intervention into routine health system practices.

Dataset Information

FAIR principles to improve the impact on health research management outcomes.

Background

Objective

Methods

Results

Conclusions

Publications

FAIR principles to improve the impact on health research management outcomes.

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