ArticleNanomaterials (Basel, Switzerland)2020
A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences.
Article in Nanomaterials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Fifteen years of ChEMBL and its role in cheminformatics and drug discovery.Journal of cheminformatics · 2025Review
- Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines.Discover nano · 2024Review
- FAIR assessment of nanosafety data reusability with community standards.Scientific data · 2024Article
- Advancing healthcare through data: the BETTER project's vision for distributed analytics.Frontiers in medicine · 2024Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools.Journal of cheminformatics · 2023Article
- Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review.Archives of toxicology · 2023Review
- FAIR+E pathogen data for surveillance and research: lessons from COVID-19.Frontiers in public health · 2023Article
- Prospects and challenges for FAIR toxicogenomics data.Nature nanotechnology · 2022Article
- An automated solution for measuring the progress toward FAIR research data.Patterns (New York, N.Y.) · 2021Article
- FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study onComputational toxicology (Amsterdam, Netherlands) · 2021Article
- Towards FAIR nanosafety data.Nature nanotechnology · 2021Article
- A methodology for developing key events to advance nanomaterial-relevant adverse outcome pathways to inform risk assessment.Nanotoxicology · 2021Article
- Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials.Scientific data · 2021Article
- Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance.Nanomaterials (Basel, Switzerland) · 2021Article
- ELIXIR and Toxicology: a community in development.F1000Research · 2021Article
- Methods, models, mechanisms and metadata: Introducing the Nanotoxicology collection at F1000Research.F1000Research · 2021Article
- Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.Nanomaterials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.