Dental Research Data Availability and Quality According to the FAIR Principles

Abstract

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable—for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.

Rates and predictors of data and code sharing in the medical and health sciences: A systematic review with meta-analysis of individual participant data

“…Combination of eligible studies in a random-effects meta-analysis suggests that 8% of medical articles published since 2016 declare data to be publicly available (95% CI: 5-11%, 95% PI: 0-30%, k = 27 studies, o = 700,054 primary articles, I 2 = 96%; Figure 2) and 2% actually share data publicly (95% CI: 1-3%, 95% PI: 0-11%, k = 25, o = 11,873, I 2 = 90%; Figure 3). Despite the included meta-research studies following similar methodologies, we do note high statistical heterogeneity for both analyses, with influence analyses showing that the greatest contributors to between-study heterogeneity for declared data sharing were the high precision findings of Uribe et al [125] and Serghiou et al [14], who used automated coding strategies. For actual data sharing, the high estimate by Hamilton et al [15], who assessed partial sharing of data rather than complete, was also a large contributor to between-study heterogeneity.…”

“…Like declared data sharing rates, despite similar methodologies, declared code sharing estimates were also associated with high statistical heterogeneity. Again, influence analyses revealed high precision estimates from Uribe et al [125] and Serghiou et al [14], in addition to the high estimate by Kobres et al [78], who evaluated the sharing of model code from Zika virus forecasting and prediction research, were the biggest contributors to between-study heterogeneity.…”

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

“…Developing DL-based solutions depends on the availability of large amounts of highquality data. A research by [91] analyzed 7,509 papers published in PubMed-indexed dental journals between 2016 and 2021, and it was found that only 112 of them (1.5%) provide the used or created datasets. This SM confirms the trend observed by [91], with 52 (75.36%) articles using their own datasets and not publishing them for future research.…”

Research transparency in dental research: A programmatic analysis

“…The study sample was restricted to open access articles in the EPMC database and thus the sample may not represent all research articles published in dental journals. However, investigation of biomedical research articles has shown that, in terms of these five transparency indicators, there are only small differences between articles accessible and inaccessible via the EPMC [ 40 , 41 ]. On the other hand, there are some notable differences between open access and non‐open access articles: open access articles are more frequently published by men, people employed at prestigious institutions, those with greater federal research funding, and those in more advanced career stages [ 42 ].…”