ArticleInternational journal of toxicology
Embracing Transparency Through Data Sharing.
Article in International journal of toxicology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.JCO clinical cancer informatics · 2020Pooled it
- The case for data sharing in traditional, complementary, and integrative medicine research.Integrative medicine research · 2025Article
- HumanIslets: An integrated platform for human islet data access and analysis.bioRxiv : the preprint server for biology · 2024Article
- Ethical Impact Assessment of Sharing Nanosafety Data.Open research Europe · 2024Article
- Development of a Comprehensive Food Data Citation Standard: A Surprising Gap in the Nutrition Research Literature.Current developments in nutrition · 2024Article
- Individual notions of fair data sharing from the perspectives of Swiss stakeholders.BMC health services research · 2021Article
- Why ethics guidance needs to be updated for contemporary HIV prevention research.Journal of the International AIDS Society · 2020Review
- On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management.Healthcare (Basel, Switzerland) · 2020Article
- Systems toxicology assessment of a representative e-liquid formulation using human primary bronchial epithelial cells.Toxicology reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low rates of reproducibility and translatability of data from nonclinical research have been reported. Major causes of irreproducibility include oversights in study design, failure to characterize reagents and protocols, a lack of access to detailed methods and data, and an absence of universally accepted and applied standards and guidelines. Specific areas of concern include uncharacterized antibodies and cell lines, the use of inappropriate sampling and testing protocols, a lack of transparency and access to raw data, and deficiencies in the translatability of findings to the clinic from studies using animal models of disease. All stakeholders-academia, industry, funding agencies, regulators, nonprofit entities, and publishers-are encouraged to play active roles in addressing these challenges by formulating and promoting access to best practices and standard operating procedures and validating data collaboratively at each step of the biomedical research life cycle.
Indexed as
Identifiers
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.