ArticlePloS one2019
Reproducible big data science: A case study in continuous FAIRness.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 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
19 citing papers in PubMed.
- Open science interventions to improve reproducibility and replicability of research: a scoping review.Royal Society open science · 2025Article
- Ten quick tips for building FAIR workflows.PLoS computational biology · 2023Article
- Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool.Scientific data · 2023Article
- FAIR for AI: An interdisciplinary and international community building perspective.Scientific data · 2023Article
- Article
- Current Challenges and Pitfalls in Soil Metagenomics.Microorganisms · 2022Article
- Article
- FAIRly big: A framework for computationally reproducible processing of large-scale data.Scientific data · 2022Article
- Regional synapse gain and loss accompany memory formation in larval zebrafish.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.Harvard data science review · 2022Article
- Orchestrating and sharing large multimodal data for transparent and reproducible research.Nature communications · 2021Article
- Cloudy with a Chance of Peptides: Accessibility, Scalability, and Reproducibility with Cloud-Hosted Environments.Journal of proteome research · 2021Article
- Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment.PloS one · 2021Article
- FaceBase 3: analytical tools and FAIR resources for craniofacial and dental research.Development (Cambridge, England) · 2020Review
- Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.Cell reports · 2020Article
- Towards Co-Evolution of Data-Centric Ecosystems.Scientific and statistical database management : International Conference, SSDBM ... : proceedings. International Conference on Scientific and Statistical Database Management · 2020Article
- Cluster equilibrium scheduling method based on backpressure flow control in railway power supply systems.PloS one · 2020Article
- Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv.GigaScience · 2019Article
- Future-Proofing YourMicrobiology resource announcements · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
Abstract
Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.
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.