ArticlePLoS computational biology2021
Principles for data analysis workflows.
Article in PLoS computational biology, 2021. 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.
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Statistical software reporting in dental research: an evaluation of current practices.BMC medical research methodology · 2026Review
- Ten particularly frequent and consequential questionable research practices in quantitative research: Bias mechanisms, preventive strategies, and a simulation-based framework.Behavior research methods · 2026Article
- Article
- Ten simple rules for building and maintaining a responsible data science workflow.PLoS computational biology · 2024Article
- Critical Review of Selected Analytical Platforms for GC-MS Metabolomics Profiling-Case Study: HS-SPME/GC-MS Analysis of Blackberry's Aroma.Foods (Basel, Switzerland) · 2024Article
- A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.BMC biology · 2024Article
- A multi-omics data analysis workflow packaged as a FAIR Digital Object.GigaScience · 2024Article
- Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis.PloS one · 2024Article
- Why don't we share data and code? Perceived barriers and benefits to public archiving practices.Proceedings. Biological sciences · 2022Article
- Ten simple rules on writing clean and reliable open-source scientific software.PLoS computational biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A systematic and reproducible "workflow"-the process that moves a scientific investigation from raw data to coherent research question to insightful contribution-should be a fundamental part of academic data-intensive research practice. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining 3 phases: the Explore, Refine, and Produce Phases. Each phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between design principles and established practice in software development. The guidance provided here is not intended to be a strict rulebook; rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students new to research and current researchers who are new to data-intensive work.
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.