ArticleScientific data2022
FAIRly big: A framework for computationally reproducible processing of large-scale data.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Exposome-wide patterns predict brain health in aging.Nature communications · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Two axes of white matter development.Nature communications · 2026Article
- Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations.bioRxiv : the preprint server for biology · 2025Article
- Article
- An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function.bioRxiv : the preprint server for biology · 2025Article
- NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics.bioRxiv : the preprint server for biology · 2025Article
- Challenging the status quo: A guide to open and reproducible neuroimaging for early career researchers.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Open-source platforms to investigate analytical flexibility in neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.JAMIA open · 2024Article
- Provenance Information for Biomedical Data and Workflows: Scoping Review.Journal of medical Internet research · 2024Article
- Article
- A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition.eLife · 2024Article
- Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme.Human brain mapping · 2024Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition.bioRxiv : the preprint server for biology · 2023Article
- A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.bioRxiv : the preprint server for biology · 2023Article
- How to establish and maintain a multimodal animal research dataset using DataLad.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework's performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).
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.