ArticleEuropean journal of epidemiology2023
Research-ready data: the C-Surv data model.
Article in European journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- A benchmark of text embedding models for semantic harmonization of Alzheimer's disease cohorts.The journal of prevention of Alzheimer's disease · 2025Article
- Dementia Data Landscape 1. Cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A natural language processing approach to support biomedical data harmonization: Leveraging large language models.PloS one · 2025Article
- We must discuss research environments.Royal Society open science · 2024Article
- National and international collaborations to advance research into vascular contributions to cognitive decline.Cerebral circulation - cognition and behavior · 2024Article
- Evaluating the harmonisation potential of diverse cohort datasets.European journal of epidemiology · 2023Article
- Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.Journal of the Royal Society, Interface · 2023Review
- The pursuit of approaches to federate data to accelerate Alzheimer's disease and related dementia research: GAAIN, DPUK, and ADDI.Frontiers in neuroinformatics · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Research-ready data (data curated to a defined standard) increase scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following stakeholder consultation, a standard data model (C-Surv) optimised for data discovery, was developed using data from 5 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. Data preparation times were compared between cohort specific data models and C-Surv.It was concluded that adopting a common data model as a data standard for the discovery and analysis of research cohort data offers multiple benefits.
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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.