FAIR assessment tools: evaluating use and performance

Publishing research data using a findable, accessible, interoperable, and reusable (FAIR) approach is paramount to further innovation in many areas of research. In particular in developing innovative approaches to predict (eco)toxicological risks in (nano or advanced) material design where efficient use of existing data is essential. The use of tools assessing the FAIRness of data helps the future improvement of data FAIRness and therefore their re-use. This paper reviews ten FAIR assessment tools that have been evaluated and characterized using two datasets from the nanomaterials and microplastics risk assessment domain. The tools were grouped into four categories: online and offline self-assessment survey based, online (semi-) automated and other tools. We found that the online self-assessment tools can be used for a quick scan of a user's dataset due to their ease of use, little need for experience and short time investment. When a user is looking to assess full databases, and not just datasets, for their FAIRness, (semi-)automated tools are more practical. The offline assessment tools were found to be limited and unreliable due to a lack of guidance and an under-developed state. To further characterize the usability, two datasets were run through all tools to check the similarity in the tools' results. As most of the tools differ in their implementation of the FAIR principles, a large variety in outcomes was obtained. Furthermore, it was observed that only one tool gives recommendations to the user on how to improve the FAIRness of the evaluated dataset. This paper gives clear recommendations for both the user and the developer of FAIR assessment tools.

Gene expression and copy number profiling suggests the importance of allelic imbalance in 19p in asbestos-associated lung cancer

Asbestos is a pulmonary carcinogen known to give rise to DNA and chromosomal damage, but the exact carcinogenic mechanisms are still largely unknown. In this study, gene expression arrays were performed on lung tumor samples from 14 heavily asbestos-exposed and 14 non-exposed patients matched for other characteristics. Using a two-step statistical analysis, 47 genes were revealed that could differentiate the tumors of asbestos-exposed from those of non-exposed patients. To identify asbestos-associated regions with DNA copy number and expressional changes, the gene expression data were combined with comparative genomic hybridization microarray data. As a result, a combinatory profile of DNA copy number aberrations and expressional changes significantly associated with asbestos exposure was obtained. Asbestos-related areas were detected in 2p21-p16.3, 3p21.31, 5q35.2-q35.3, 16p13.3, 19p13.3-p13.1 and 22q12.3-q13.1. The most prominent of these, 19p13, was further characterized by microsatellite analysis in 62 patients for the differences in allelic imbalance (AI) between the two groups of lung tumors. 79% of the exposed and 45% of the non-exposed patients (P=0.008) were found to be carriers of AI in their lung tumors. In the exposed group, AI in 19p was prevalent regardless of the histological tumor type. In adenocarcinomas, AI in 19p appeared to occur independently of the asbestos exposure.