ArticleInternational journal of medical informatics2019
ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata.
Article in International journal of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Provenance Information for Biomedical Data and Workflows: Scoping Review.Journal of medical Internet research · 2024Article
- Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain.Database : the journal of biological databases and curation · 2024Article
- NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.Frontiers in neuroinformatics · 2023Article
- NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.Frontiers in neuroinformatics · 2023Article
- End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach.Journal of biomedical semantics · 2022Article
- Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- The role of metadata in reproducible computational research.Patterns (New York, N.Y.) · 2021Review
- Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook.Yearbook of medical informatics · 2020Review
- NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study.JMIR medical informatics · 2019Article
- Semantic Provenance Graph for Reproducibility of Biomedical Research Studies: Generating and Analyzing Graph Structures from Published Literature.Studies in health technology and informatics · 2019Article
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.
Funding
Abstract
objectiveReproducibility of research studies is key to advancing biomedical science by building on sound results and reducing inconsistencies between published results and study data. We propose that the available data from research studies combined with provenance metadata provide a framework for evaluating scientific reproducibility. We developed the ProvCaRe platform to model, extract, and query semantic provenance information from 435, 248 published articles.
methodsThe ProvCaRe platform consists of: (1) the S3 model and a formal ontology; (2) a provenance-focused text processing workflow to generate provenance triples consisting of subject, predicate, and object using metadata extracted from articles; and (3) the ProvCaRe knowledge repository that supports "provenance-aware" hypothesis-driven search queries. A new provenance-based ranking algorithm is used to rank the articles in the search query results.
resultsThe ProvCaRe knowledge repository contains 48.9 million provenance triples. Seven research hypotheses were used as search queries for evaluation and the resulting provenance triples were analyzed using five categories of provenance terms. The highest number of terms (34%) described provenance related to population cohort followed by 29% of terms describing statistical data analysis methods, and only 5% of the terms described the measurement instruments used in a study. In addition, the analysis showed that some articles included a higher number of provenance terms across multiple provenance categories suggesting a higher potential for reproducibility of these research studies.
conclusionThe ProvCaRe knowledge repository (https://provcare. CASE: edu/) is one of the largest provenance resources for biomedical research studies that combines intuitive search functionality with a new provenance-based ranking feature to list articles related to a search query.
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.